the continuum representation of matter is widely used for material dynamics in science and engineering .spatially - resolved continuum dynamics simulations are the most widespread and familiar , solving the initial value problem by discretizing the spatial domain and integrating the dynamical equations forward in time to predict the motion and deformation of components of the system .this type of simulation is used , for instance , to study hypervelocity impact problems such as the vulnerability of armor to projectiles , the performance of satellite debris shields , and the impact of meteorites with planets , notably the formation of the moon .the problem can be divided into the dynamical equations of the continuum , the state field of the components , and the inherent properties of the materials .given the local material state , the material properties allow the stress to be determined .given the stress field and mass density field , the dynamical equations describe the fields of acceleration , compression , and thermodynamic work done on the materials .the equations of continuum dynamics describe the behavior of a dynamically deforming system of arbitrary complexity .particular , simpler deformation paths can be described more compactly by different sets of equations , and solved by different techniques than those used for continuum dynamics in general .simpler deformation paths occur often in experiments designed to develop and calibrate models of material properties .these paths can be regarded as different ways of interrogating the material properties .the principal examples in material dynamics are shock and ramp compression .typical experiments are designed to induce such loading histories and measure or infer the properties of the material in these states before they are destroyed by release from the edges or by reflected waves .the development of the field of material dynamics was driven by applications in the physics of hypervelocity impact and high explosive systems , including nuclear weapons . in the regimes of interest ,typically components with dimensions ranging from millimeters to meters and pressures from 1gpa to 1tpa , material behavior is dominated by the scalar equation of state ( eos ) : the relationship between pressure , compression ( or mass density ) , and internal energy .other components of stress ( specifically shear stresses ) are much smaller , and chemical explosives react promptly so can be treated by simple models of complete detonation .eos were developed as fits to experimental data , particularly to series of shock states and to isothermal compression measurements .it is relatively straightforward to construct shock and ramp compression states from an eos algebraically or numerically depending on the eos , and to fit an eos to these measurements .more recently , applications and scientific interest have grown to include a wider range of pressures and time scales , such as laser - driven inertial confinement fusion , and experiments are designed to measure other aspects than the eos , such as the kinetics of phase changes , constitutive behavior describing shear stresses , incomplete chemical reactions , and the effects of microstructure , including grain orientation and porosity .theoretical techniques have also evolved to predict the eos with %  accuracy and elastic contributions to shear stress with slightly poorer accuracy .a general convention for representing material states is described , and numerical methods are reported for calculating shock and ramp compression states from general representations of material properties .the desired structure for the description of the material state and properties under dynamic loading was developed to be as general as possible with respect to the types of material or models to be represented in the same framework , and designed to give the greatest amount of commonality between spatially - resolved simulations and calculations of shock and ramp compressions .  in condensed matter on sub - microsecond time scales ,heat conduction is often too slow to have a significant effect on the response of the material , and is ignored here .the equations of non - relativistic continuum dynamics are , in lagrangian form , _i.e. _ along characteristics moving with the local material velocity , where is the mass density and the specific internal energy .changes in can be related to changes in the temperature through the heat capacity .the inherent properties of each material in the problem are described by its constitutive relation or equation of state . as well as experiencing compression and work from mechanical deformation, the local material state can evolve through internal processes such as plastic flow .in general ,   \quad:\quad u\equiv { \mbox{grad}}\,\vec u(\vec r , t ) \\\ ] ] which can also include the equations for and .thus the material properties must describe at a minimum and $ ] for each material .if they also describe , the conductivity , and , then heat conduction can be treated .other functions may be needed for particular numerical methods in continuum dynamics , such as the need for wave speeds ( _ e.g. _ the longitudinal sound speed ) , which are needed for time step control in explicit time integration .internally , within the material properties models , it is desirable to re - use software as much as possible , and other functions of the state are therefore desirable to allow models to be constructed in a modular and hierarchical way . arithmetic manipulations must be performed on the state during numerical integration , and these can be encoded neatly using operator overloading , so the operator of the appropriate type is invoked automatically without having to include ` if - then - else ' structures for each operator as is the case in non - object - oriented programming languages such as fortran-77 . for instance , if is calculated in a forward - time numerical method then changes of state are calculated using numerical evolution equations such as thus for a general state and its time derivative , which has an equivalent set of components , it is necessary to multiply a state by a real number and to add two states together . for a specific software implementation ,other operations may be needed , for example to create , copy , or destroy a new instance of a state .the attraction of this approach is that , by choosing a reasonably general form for the constitutive relation and associated operations , it is possible to separate the continuum dynamics part of the problem from the inherent behavior of the material .the relations describing the properties of different types of material can be encapsulated in a library form where the continuum dynamics program need know nothing about the relations for any specific type of material , and _ vice versa_. the continuum dynamics programs and the material properties relations can be developed and maintained independently of each other , provided that the interface remains the same ( table [ tab : interface ] ) .this is an efficient way to make complicated material models available for simulations of different types , including lagrangian and eulerian hydrocodes operating on different numbers of dimensions , and calculations of specific loading or heating histories such as shock and ramp loading discussed below .software interfaces have been developed in the past for scalar eos with a single structure for the state , but object - oriented techniques make it practical to extend the concept to much more complicated states , to combinations of models , and to alternative types of model selected when the program is run , without having to find a single super - set state encompassing all possible states as special cases .a very wide range of types of material behavior can be represented with this formalism . at the highest level ,different types of behavior are characterized by different structures for the state ( table [ tab : stateexamples ] ) . for each type of state ,different specific models can be defined , such as perfect gas , polytropic and grneisen eos . for each specific model , different materialsare represented by choosing different values for the parameters in the model , and different local material states are represented through different values for the components of . in the jargon of object - oriented programming ,the ability to define an object whose precise type is undetermined until the program is run is known as polymorphism . for our application, polymorphism is used at several levels in the hierarchy of objects , from the overall type of a material ( such as ` one represented by a pressure - density - energy eos ' or ` one represented by a deviatoric stress model ' ) through the type of relation used to describe the properties of that material type ( such as perfect gas , polytropic , or grneisen for a pressure - density - energy eos , or steinberg - guinan or preston - tonks - wallace for a deviatoric stress model ) , to the type of general mathematical function used to represent some of these relations ( such as a polynomial or a tabular representation of in a polytropic eos ) ( table [ tab : polymorphism ] ) .states or models may be defined by extending or combining other states or models this can be implemented using the object - oriented programming concept of inheritance .thus deviatoric stress models can be defined as an extension to _ any _ pressure - density - energy eos ( they are usually written assuming a specific type , such as steinberg s cubic grneisen form ) , homogeneous mixtures can be defined as combinations of any pressure - density - temperature eos , and heterogeneous mixtures can be defined as combinations of materials each represented by any type of material model .trial implementations have been made as libraries in the c++ and java programming languages .the external interface to the material properties was general at the level of representing a generic material type and state .the type of state and model were then selected when programs using the material properties library were run . in c++ , objects which were polymorphic at run time had to be represented as pointers , requiring additional software constructions to allocate and free up physical memory associated with each object .it was possible to include general re - usable functions as polymorphic objects when defining models : real functions of one real parameter could be polynomials , transcendentals , tabular with different interpolation schemes , piecewise definitions over different regions of the one dimensional line , sums , products , etc ; again defined specifically at run time .object - oriented polymorphism and inheritance were thus very powerful techniques for increasing software re - use , making the software more compact and more reliable through the greater use of functions which had already been tested .  given conceptual and software structures designed to represent general material properties suitable for use in spatially - resolved continuum dynamics simulations , we now consider the use of these generic material models for calculating idealized loading paths .experiments to investigate the response of materials to dynamic loading , and to calibrate parameters in models of their behavior , are usually designed to apply as simple a loading history as is consistent with the transient state of interest .the simplest canonical types of loading history are shock and ramp .methods of solution are presented for calculating the result of shock and ramp loading for materials described by generalized material models discussed in the previous section .such direct solution removes the need to use a time- and space - resolved continuum dynamics simulation , allowing states to be calculated with far greater efficiency and without the need to consider and make allowance for attributes of resolved simulations such as the finite numerical resolution and the effect of numerical and artificial viscosities .ramp compression is taken here to mean compression or decompression . if the material is represented by an inviscid scalar eos , _i.e. _ ignoring dissipative processes and non - scalar effects from elastic strain , ramp compression follows an isentrope .this is no longer true when dissipative processes such as plastic heating occur .the term ` quasi - isentropic ' is sometimes used in this context , particularly for shockless compression ; here we prefer to refer to the thermodynamic trajectories as adiabats since this is a more appropriate term : no heat is exchanged with the surroundings on the time scales of interest .  for adiabatic compression , the state evolves according to the second law of thermodynamics , where is the temperature and the specific entropy .thus or for a more general material whose stress tensor is more complicated than a scalar pressure , where is the component of stress normal to the direction of deformation .the velocity gradient was expressed through a compression factor and a strain rate . in all ramp experiments used in the development and calibration of accurate material models ,the strain has been applied uniaxially .more general strain paths , for instance isotropic or including a shear component , can be treated by the same formalism , and that the working rate is then a full inner product of the stress and strain tensors .the acceleration or deceleration of the material normal to the wave as it is compressed or expanded adiabatically is from which it can be deduced that where is the longitudinal wave speed .  as with continuum dynamics , internal evolution of the material statecan be calculated simultaneously with the continuum equations , or operator split and calculated periodically at constant compression .the results are the same to second order in the compression increment .operator - splitting allows calculations to be performed without an explicit entropy , if the continuum equations are integrated isentropically and dissipative processes are captured by internal evolution at constant compression .operator - splitting is desirable when internal evolution can produce highly nonlinear changes , such as reaction from solid to gas : rapid changes in state and properties can make numerical schemes unstable .operator - splitting is also desirable when the integration time step for internal evolution is much shorter than the continuum dynamics time step .neither of these considerations is very important for ramp compression without spatial resolution , but operator - splitting was used as an option in the ramp compression calculations for consistency with continuum dynamics simulations .the ramp compression equations were integrated using forward - time runge - kutta numerical schemes of second order .the fourth order scheme is a trivial extension .the sequence of operations to calculate an increment of ramp compression is as follows :  1 .time increment : 2 .predictor : 3 .corrector : 4 .internal evolution :   where is the model - dependent state evolution from applied strain , and is internal evolution at constant compression .the independent variable for integration is specific volume or mass density ; for numerical integration finite steps are taken in and .the step size can be controlled so that the numerical error during integration remains within chosen limits .a tabular adiabat can be calculated by integrating over a range of or , but when simulating experimental scenarios the upper limit for integration is usually that one of the other thermodynamic quantities reaches a certain value , for example that the normal component of stress reaches zero , which is the case on release from a high pressure state at a free surface .specific end conditions were found by monitoring the quantity of interest until bracketed by a finite integration step , then bisecting until the stop condition was satisfied to a chosen accuracy . during bisection , each trial calculation was performed as an integration from the first side of the bracket by the trial compression .shock compression is the solution of a riemann problem for the dynamics of a jump in compression moving with constant speed and with a constant thickness .the rankine - hugoniot ( rh ) equations describing the shock compression of matter are derived in the continuum approximation , where the shock is a formal discontinuity in the continuum fields . in reality , matter is composed of atoms , and shocks have a finite width governed by the kinetics of dissipative processes at a fundamental level , matter does not distinguish between shock compression and ramp compression with a high strain rate but the rh equations apply as long as the width of the region of matter where unresolved processes occur is constant .compared with the isentropic states induced by ramp compression in a material represented by an eos , a shock always increases the entropy and hence the temperature. with dissipative processes included , the distinction between a ramp and a shock may become blurred .the rh equations express the conservation of mass , momentum , and energy across a moving discontinuity in state .they are usually expressed in terms of the pressure , but are readily generalized for materials supporting shear stresses by using the component of stress normal to the shock ( _ i.e. _ , parallel with the direction of propagation of the shock ) , : where is the speed of the shock wave with respect to the material , is the change in material speed normal to the shock wave ( _ i.e. _ , parallel to its direction of propagation ) , and subscript refers to the initial state .  the rh relations can be applied to general material models if a time scale or strain rate is imposed , and an orientation chosen for the material with respect to the shock .shock compression in continuum dynamics is almost always uniaxial .the rh equations involve only the initial and final states in the material .if a material has properties that depend on the deformation path such as plastic flow or viscosity then physically the detailed shock structure may make a difference .this is a limitation of discontinuous shocks in continuum dynamics : it may be addressed as discussed above by including dissipative processes and considering ramp compression , if the dissipative processes can be represented adequately in the continuum approximation .spatially - resolved simulations with numerical differentiation to obtain spatial derivatives and forward time differencing are usually not capable of representing shock discontinuities directly , and an artificial viscosity is used to smear shock compression over a few spatial cells .the trajectory followed by the material in thermodynamic space is a smooth adiabat with dissipative heating supplied by the artificial viscosity . if plastic work is also included during this adiabatic compression , the overall heating for a given compression is greater than from the rh equations . to be consistent ,plastic flow should be neglected while the artificial viscosity is non - zero .this localized disabling of physical processes , particularly time - dependent ones , during the passage of the unphysically smeared shock was previously found necessary for numerically stable simulations of detonation waves by reactive flow .are reactive shock waves . steady planar detonation ( the chapman - jouguet state ) may be calculated using the rh relations , by imposing the condition that the material state behind the shock is fully reacted .several numerical methods have been used to solve the rh equations for materials represented by an eos only .the general rh equations may be solved numerically for a given shock compression by varying the specific internal energy until the normal stress from the material model equals that from the rh energy equation , eq . [ eq : rh3 ] .the shock and particle speeds are then calculated from eqs [ eq : rh1 ] and [ eq : rh2 ] .this numerical method is particularly convenient for eos of the form , as may be varied directly .solutions may still be found for general material models using , by which the energy may be varied until the solution is found .numerically , the solution was found by bracketing and bisection :  1 .for given compression , take the low - energy end for bracketing as a nearby state ( _ e.g. _ the previous state , of lower compression , on the hugoniot ) , compressed adiabatically ( to state ) , and cooled so the specific internal energy is .2 .  bracket the desired state : apply successively larger heating increments to , evolving each trial state internally , until from the material model exceeds from eq .[ eq : rh3 ] .3 .  bisect in , evolving each trial state internally , until equals to the desired accuracy .as with ramp compression , the independent variable for solution was mass density , and finite steps were taken .each shock state was calculated independently of the rest , so numerical errors did not accumulate along the shock hugoniot .the accuracy of the solution was independent of .a tabular hugoniot can be calculated by solving over a range of , but again when simulating experimental scenarios it is usually more useful to calculate the shock state where one of the other thermodynamic quantities reaches a certain value , often that and match the values from another , simultaneous shock calculation for another material the situation in impact and shock transmission problems , discussed below .specific stop conditions were found by monitoring the quantity of interest until bracketed by a finite solution step , then bisecting until the stop condition was satisfied to a chosen accuracy . during bisection , each trial calculation was performed as a shock from the initial conditions to the trial shock compression .the accuracy of these numerical schemes was tested by comparing with shock and ramp compression of a material represented by a perfect gas eos , the numerical solution requires a value to be chosen for every parameter in the material model , here .air was chosen as an example material , with .air at standard temperature and pressure has approximately / and / kg .isentropes for the perfect gas eos have the form and shock hugoniots have the form the numerical solutions reproduced the principal isentrope and hugoniot to % and 0.1% respectively , for a compression increment of 1% along the isentrope and a solution tolerance of for each shock state ( fig .[ fig : pgascmp ] ) . over most of the range ,the error in the hugoniot was 0.02% or less , only approaching 0.1% near the maximum shock compression .the ability to calculate shock and ramp loci in state space , _ i.e. _ as a function of varying loading conditions , is particularly convenient for investigating complex aspects of the response of condensed matter to dynamic loading . each locus can be obtained by a single series of shock or ramp solutions , rather than having to perform a series of time- and space - resolved continuum dynamics simulations , varying the initial or boundary conditions and reducing the solution .we consider the calculation of temperature in the scalar eos , the effect of material strength and the effect of phase changes .the continuum dynamics equations can be closed using a mechanical eos relating stress to mass density , strain , and internal energy . for a scalar eos ,the ideal form to close the continuum equations is , with the natural choice for the primitive state fields .however , the temperature is needed as a parameter in physical descriptions of many contributions to the constitutive response , including plastic flow , phase transitions , and chemical reactions . here, we discuss the calculation of temperature in different forms of the scalar eos .if the scalar eos is constructed from its underlying physical contributions for continuum dynamics , it may take the form , from which can be calculated using the second law of thermodynamics .an example is the ` sesame ' form of eos , based on interpolated tabular relations for  .a pair of relations can be used as a mechanical eos by eliminating , which is equivalent to inverting to find , then substituting in . for a general relation , for example for the sesame eos, the inverse can be calculated numerically as required , along an isochore . in this way, a can be used as a eos .alternatively , the same relation can be used directly with a primitive state field including temperature instead of energy : .the evolution of the state under mechanical work then involves the calculation of , _i.e. _ the reciprocal of the specific heat capacity , which is a derivative of . as this calculation does not require to be inverted , it is computationally more efficient to use eos with a temperature - based , rather than energy - based , state .the main disadvantage is that it is more difficult to ensure exact energy conservation as the continuum dynamics equations are integrated in time , but any departure from exact conservation is at the level of accuracy of the algorithm used to integrate the heat capacity .both structures of eos have been implemented for material property calculations . taking a sesame type eos ,thermodynamic loci were calculated with or primitive states , for comparison ( fig .[ fig : alcmpt ] ) . for a monotonic eos ,the results were indistinguishable within differences from forward or reverse interpolation of the tabular relations . when the eos , or the effective surface using a given order of interpolating function , was non - monotonic , the results varied greatly because of non - uniqueness when eliminating for the primitive state .mechanical eos are often available as empirical , algebraic relations , derived from shock data .temperature can be calculated without altering the mechanical eos by adding a relation .while this relation could take any form in principle , one can also follow the logic of the grneisen eos , in which the pressure is defined in terms of its deviation from a reference curve . thus temperatures can be calculated by reference to a compression curvealong which the temperature and specific internal energy are known , , and a specific heat capacity defined as a function of density . in the calculations , this augmented eos was represented as a ` mechanical - thermal ' form comprising any eos plus the reference curves an example of software inheritance and polymorphism .one natural reference curve for temperature is the cold curve , .the cold curve can be estimated from the principal isentrope using the estimated density variation of the grneisen parameter :  . in this work, the principal isentrope was calculated in tabular form from the mechanical eos , using the ramp compression algorithm described above .empirical eos are calibrated using experimental data .shock and adiabatic compression measurements on strong materials inevitably include elastic - plastic contributions as well as the scalar eos itself .if the elastic - plastic contributions are not taken into account self - consistently , the eos may implicitly include contributions from the strength .a unique scalar eos can be constructed to reproduce the normal stress as a function of compression for any unique loading path : shock or adiabat , for a constant or smoothly - varying strain rate .such an eos would not generally predict the response to other loading histories .the eos and constitutive properties for the materials considered here were constructed self - consistently from shock data this does not mean the models are accurate for other loading paths , as neither the eos nor the strength model includes all the physical terms that real materials exhibit .this does not in any case matter for the purposes of demonstrating the properties of the numerical schemes .this mechanical - thermal procedure was applied to al using a grneisen eos fitted to the same shock data used to calculate the eos discussed above .temperatures were in good agreement ( fig .[ fig : alcmpt ] ) . the mechanical - thermal calculations required a similar computational effort to the tabular eos with a primitive states ( andwere thus much more efficient than the tabular eos with states ) , and described the eos far more compactly .for dynamic compressions to and above , on microsecond time scales , the flow stress of solids is often treated as a correction or small perturbation to the scalar eos . however , the flow stress has been observed to be much higher on nanosecond time scales , and interactions between elastic and plastic waves may have a significant effect on the compression and wave propagation .the rankine - hugoniot equations should be solved self - consistently with strength included .there is an inconsistency in the standard continuum dynamics treatment of scalar ( pressure ) and tensor ( stress ) response .the scalar eos expresses the pressure as the dependent quantity , which is the most convenient form for use in the continuum equations .standard practice is to use sub - hookean elasticity ( hypoelastic form ) ( table [ tab : stateexamples ] ) , in which the state parameters include the stress deviator , evolved by integration where is the shear modulus and the strain rate deviator . thus the isotropic and deviatoric contributions to stress are not treated in an equivalent way : the pressure is calculated from a local state involving a strain - like parameter ( mass density ) , whereas the stress deviator evolves with the time - derivative of strain .this inconsistency causes problems along complicated loading paths because varies strongly with compression : if a material is subjected to a shear strain , then isotropic compression ( increasing the shear modulus from to , leaving unchanged ) , then shear unloading to isotropic stress , the true unloading strain is , whereas the hypoelastic calculation would require a strain of . using be and the steinberg - guinan strength model as an example of the difference between hypoelastic and hyperelastic calculations , consider an initial strain to a flow stress of 0.3gpa followed by isothermal , isotropic compression to 100gpa , .the strain to unload to a state of isotropic stress is 0.20% ( hyperelastic ) and 0.09% ( hypoelastic ) .the discrepancy arises because the hypoelastic model does not increase the deviatoric stress under compression at constant deviatoric strain .the stress can be considered as a direct response of the material to the instantaneous state of elastic strain : .this relation can be predicted directly with electronic structure calculations of the stress tensor in a solid for a given compression and elastic strain state , and is a direct generalization of the scalar equation of state .a more consistent representation of the state parameters is to use the strain deviator rather than , and to calculate from scratch when required using  a hyperelastic formulation .the state parameters are then .the different formulations give different answers when deviatoric strain is accumulated at different compressions , in which case the hyperelastic formulation is correct . if the shear modulus varies with strain deviator _i.e. _ , for nonlinear elasticity then the definition of must be adjusted to give the same stress for a given strain .many isotropic strength models use scalar measures of the strain and stress to parameterize work hardening and to apply a yield model of flow stress : inconsistent conventions for equivalent scalar measures have been used by different workers . in the present work , the common shock physics convention was used that the flow stress component of is where is the flow stress . for consistency with published speeds and amplitudes for elastic waves , , in contrast to other values previously used for lower - rate deformation . in principle , the values of and do not matter as long as the strength parameters were calibrated using the same values then used in any simulations .   the flow stress measured from laser - driven shock experiments onbe crystals a few tens of micrometers thick is , at around 5 - 9gpa , much greater than the 0.3 - 1.3gpa measured on microsecond time scales .a time - dependent crystal plasticity model for be is being developed , and the behavior under dynamic loading depends on the detailed time dependence of plasticity .calculations were performed with the steinberg - guinan strength model developed for microsecond scale data , and , for the purposes of rough comparison , with elastic - perfectly plastic response with a flow stress of 10gpa .the elastic - perfectly plastic model neglected pressure- and work- hardening .calculations were made of the principal adiabat and shock hugoniot , and of a release adiabat from a state on the principal hugoniot .calculations were made with and without strength .considering the state trajectories in stress - volume space , it is interesting to note that heating from plastic flow may push the adiabat above the hugoniot , because of the greater heating obtained by integrating along the adiabat compared with jumping from the initial to the final state on the hugoniot ( fig .[ fig : becmpvp ] ) .even with an elastic - perfectly plastic strength model , the with - strength curves do not lie exactly above the strengthless curves , because heating from plastic flow contributes an increasing amount of internal energy to the eos as compression increases .an important characteristic for the seeding of instabilities by microstructural variations in shock response is the shock stress at which an elastic wave does not run ahead of the shock . inbe with the high flow stress of nanosecond response , the relation between shock and particle speeds is significantly different from the relation for low flow stress ( fig .[ fig : becmppus ] ) . for low flow stress ,the elastic wave travels at 13.2km / s .a plastic shock travels faster than this for pressures greater than 110gpa , independent of the constitutive model .the speed of a plastic shock following the initial elastic wave is similar to the low strength case , because the material is already at its flow stress , but the speed of a single plastic shock is appreciably higher .  for compression to a given normal stress ,the temperature is significantly higher with plastic flow included .the additional heating is particularly striking on the principal adiabat : the temperature departs significantly from the principal isentrope .thus ramp - wave compression of strong materials may lead to significant levels of heating , contrary to common assumptions of small temperature increases .plastic flow is largely irreversible , so heating occurs on unloading as well as loading .thus , on adiabatic release from a shock - compressed state , additional heating occurs compared with the no - strength case .these levels of heating are important as shock or release melting may occur at a significantly lower shock pressure than would be expected ignoring the effect of strength .[ fig : becmptp ] . )an important property of condensed matter is phase changes , including solid - solid polymorphism and solid - liquid .an equilibrium phase diagram can be represented as a single overall eos surface as before .multiple , competing phases with kinetics for each phase transformation can be represented conveniently using the structure described above for general material properties , for example by describing the local state as a set of volume fractions of each possible simple - eos phase , with transition rates and equilibration among them .this model is described in more detail elsewhere . however , it is interesting to investigate the robustness of the numerical scheme for calculating shock hugoniots when the eos has the discontinuities in value and gradient associated with phase changes .the eos of molten metal , and the solid - liquid phase transition , can be represented to a reasonable approximation as an adjustment to the eos of the solid : where < \delta h_m \\ e-\delta h_m & \quad : \quad \mbox{otherwise } \\\end{array } \right.\ ] ] and is the specific latent heat of fusion . taking the eos and a modified lindemann melting curve for al , and using / kg ,the shock hugoniot algorithm was found to operate stably across the phase transition ( fig .[ fig : almelt ] ) .given methods to calculate shock and adiabatic loading paths from arbitrary initial states , a considerable variety of experimental scenarios can be treated from the interaction of loading or unloading waves with interfaces between different materials , in planar geometry for uniaxial compression .the key physical constraint is that , if two dissimilar materials are to remain in contact after an interaction such as an impact or the passage of a shock , the normal stress and particle speed in both materials must be equal on either side of the interface .the change in particle speed and stress normal to the waves were calculated above for compression waves running in the direction of increasing spatial ordinate ( left to right ) . across an interface, the sense is reversed for the material at the left .thus a projectile impacting a stationary target to the right is decelerated from its initial speed by the shock induced by impact .the general problem at an interface can be analyzed by considering the states at the instant of first contact on impact , or when a shock traveling through a sandwich of materials first reaches the interface .the initial states are .the final states are where is the joint particle speed , , and is connected to by either a shock or an adiabat , starting at the appropriate initial velocity and stress , and with orientation given by the side of the system each material occurs on . each type of wave is considered in turn , looking for an intersection in the plane .examples of these wave interactions are the impact of a projectile with a stationary target ( fig .[ fig : impact ] ) , release of a shock state at a free surface or a material ( _ e.g. _ a window ) of lower shock impedance ( hence reflecting a release wave into the shocked material fig .[ fig : shockrel ] ) , reshocking at a surface with a material of higher shock impedance ( fig .[ fig : shockrel ] ) , or tension induced as materials try to separate in opposite directions when joined by a bonded interface ( fig .[ fig : doublerel ] ) .each of these scenarios may occur in turn following the impact of a projectile with a target : if the target is layered then a shock is transmitted across each interface with a release or a reshock reflected back , depending on the materials ; release ultimately occurs at the rear of the projectile and the far end of the target , and the oppositely - moving release waves subject the projectile and target to tensile stresses when they interact ( fig .[ fig : composite ] ) .  as an illustration of combining shock and ramp loading calculations ,consider the problem of an al projectile , initially traveling at 3.6km / s , impacting a stationary , composite target comprising a mo sample and a lif release window .the shock and release states were calculated using published material properties .the initial shock state was calculated to have a normal stress of 63.9gpa .on reaching the lif , the shock was calculated to transmit at 27.1gpa , reflecting as a release in the mo .these stresses match the continuum dynamics simulation to within 0.1gpa in the mo and 0.3gpa in the lif , using the same material properties ( fig .[ fig : impactrelcmp ] ) .the associated wave and particle speeds match to a similar accuracy ; wave speeds are much more difficult to extract from the continuum dynamics simulation .an extension of this analysis can be used to calculate the interaction of oblique shocks with an interface .a general formulation was developed to represent material models for applications in dynamic loading , suitable for software implementation in object - oriented programming languages .numerical methods were devised to calculate the response of matter represented by the general material models to shock and ramp compression , and ramp decompression , by direct evaluation of the thermodynamic pathways for these compressions rather than spatially - resolved simulations .this approach is a generalization of earlier work on solutions for materials represented by a scalar equation of state .the numerical methods were found to be flexible and robust : capable of application to materials with very different properties .the numerical solutions matched analytic results to a high accuracy .care was needed with the interpretation of some types of physical response , such as plastic flow , when applied to deformation at high strain rates .the underlying time - dependence of processes occurring during deformation should be taken into account .the actual history of loading and heating experienced by material during the passage of a shock may influence the final state this history is not captured in the continuum approximation to material dynamics , where shocks are treated as discontinuities .thus care is also needed in spatially resolved simulations when shocks are modeled using artificial viscosity to smear them unphysically over a finite thickness .calculations were shown to demonstrate the operation of the algorithms for shock and ramp compression with material models representative of complex solids including strength and phase transformations .the basic ramp and shock solution methods were coupled to solve for composite deformation paths , such as shock - induced impacts , and shock interactions with a planar interface between different materials .such calculations capture much of the physics of typical material dynamics experiments , without requiring spatially - resolving simulations .the results of direct solution of the relevant shock and ramp loading conditions were compared with hydrocode simulations , showing complete consistency .ian gray introduced the author to the concept of multi - model material properties software .lee markland developed a prototype hugoniot - calculating computer program for equations of state while working for the author as an undergraduate summer student .evolutionary work on material properties libraries was supported by the u.k .atomic weapons establishment , fluid gravity engineering ltd , and wessex scientific and technical services ltd .refinements to the technique and applications to the problems described were undertaken at los alamos national laboratory ( lanl ) and lawrence livermore national laboratory ( llnl ) .the work was performed partially in support of , and funded by , the national nuclear security agency s inertial confinement fusion program at lanl ( managed by steven batha ) , and llnl s laboratory - directed research and development project 06-si-004 ( principal investigator : hector lorenzana ) .the work was performed under the auspices of the u.s .department of energy under contracts w-7405-eng-36 , de - ac52 - 06na25396 , and de - ac52 - 07na27344 .00  .interface to material models required for explicit forward - time continuum dynamics simulations . [ cols="<,<",options="header " , ]   continuum dynamics programs can refer to material properties as an abstract ` material type ' with an abstract material state .the actual type of a material ( _ e.g. _ mechanical equation of state ) , the specific model type ( _ e.g. _ polytropic ) , and the state of material of that type are all handled transparently by the object - oriented software structure .the reactive equation of state has an additional state parameter , and the software operations are defined by extending those of the mechanical equation of state .spalling materials can be represented by a solid state plus a void fraction , with operations defined by extending those of the solid material .homogeneous mixtures are defined as a set of thermal equations of state , and the state is the set of states and mass fractions for each .heterogeneous mixtures are defined as a set of ` pure ' material properties of any type , and the state is the set of states for each component plus its volume fraction .gpa ( dotted ) . ]  gpa ( dotted ) . ]  gpa ( dotted ) . ]s after impact .structures on the waves are elastic precursors . ]1 .  principal isentrope and shock hugoniot for air ( perfect gas ) : numerical calculations for general material models , compared with analytic solutions .shock hugoniot for al in pressure - temperature space , for different representations of the equation of state .3 .  principal adiabat and shock hugoniot for be in normal stress - compression space , neglecting strength ( dashed ) , for steinberg - guinan strength ( solid ) , and for elastic - perfectly plastic with ( dotted ) .4 .  principal adiabat and shock hugoniot for be in shock speed - normal stress space , neglecting strength ( dashed ) , for steinberg - guinan strength ( solid ) , and for elastic - perfectly plastic with ( dotted ) .principal adiabat , shock hugoniot , and release adiabat for be in normal stress - temperature space , neglecting strength ( dashed ) , for steinberg - guinan strength ( solid ) , and for elastic - perfectly plastic with ( dotted ) .demonstration of shock hugoniot solution across a phase boundary : shock - melting of al , for different initial porosities . 7 .wave interactions for the impact of a flat projectile moving from left to right with a stationary target .dashed arrows are a guide to the sequence of states . for a projectile moving from right to left ,the construction is the mirror image reflected in the normal stress axis .wave interactions for the release of a shocked state ( shock moving from left to right ) into a stationary ` window ' material to its right .the release state depends whether the window has a higher or lower shock impedance than the shocked material .dashed arrows are a guide to the sequence of states . for a shock moving from right to left ,the construction is the mirror image reflected in the normal stress axis .wave interactions for the release of a shocked state by tension induced as materials try to separate in opposite directions when joined by a bonded interface .material damage , spall , and separation are neglected : the construction shows the maximum tensile stress possible . for general material properties , _e.g. _ if plastic flow is included , the state of maximum tensile stress is not just the negative of the initial shock state .dashed arrows are a guide to the sequence of states .the graph shows the initial state after an impact by a projectile moving from right to left ; for a shock moving from right to left , the construction is the mirror image reflected in the normal stress axis . 10 .schematic of uniaxial wave interactions induced by the impact of a flat projectile with a composite target .hydrocode simulation of al projectile at 3.6km / s impacting a mo target with a lif release window , 1.1 after impact .structures on the waves are elastic precursors .

Abstract: a general formulation was developed to represent material models for applications in dynamic loading . numerical methods were devised to calculate response to shock and ramp compression , and ramp decompression , generalizing previous solutions for scalar equations of state . the numerical methods were found to be flexible and robust , and matched analytic results to a high accuracy . the basic ramp and shock solution methods were coupled to solve for composite deformation paths , such as shock - induced impacts , and shock interactions with a planar interface between different materials . these calculations capture much of the physics of typical material dynamics experiments , without requiring spatially - resolving simulations . example calculations were made of loading histories in metals , illustrating the effects of plastic work on the temperatures induced in quasi - isentropic and shock - release experiments , and the effect of a phase transition .


the increasing need for context - aware information and the rapid advancements in communication networks have motivated significant research effort in the area of location - based services . this effort resulted in the development of many location determination systems , including the gps system , ultrasonic - based systems , infrared - based ( ir ) systems , and radio frequency - based ( rf ) systems .moreover , motion detection systems , that aim at detecting the motion of an entity carrying a device , were also developed .these systems require the tracked entity to carry a device that participates in the localization process .thus , we refer to them as device - based systems .motivated by the wide use of wireless lans for indoor communication , we recently introduced the concept of device - free passive _ dfp _ localization which enables the detection and tracking of entities that do not carry any devices nor participate in the localization process .this concept depends on the fact that the presence and motion of entities in an rf environment affects the rf signal strength , especially when dealing with the 2.4 ghz band which is used in different ieee standards such as 802.11b and 802.11 g ( wifi ) .dfp _ algorithms were proposed for detection and tracking of entities in indoor environments .our focus in this paper is on the detection problem .in particular , we address the problem of designing a low - overhead , accurate , and robust _ dfp _ motion detection system .we introduce the _ rasid _ system that provides a software only solution on top of the already installed wireless networks enabling a wide set of applications including intrusion detection , border protection , and smart homes . as a typical _ dfp _ system , _ rasid _consists of signal transmitters , such as access points ( aps ) , signal receivers or monitoring points ( mps ) , such as standard laptops , and an application server which collects and processes information about the received signals from each mp .the application server contains the main system modules responsible for performing the detection function ( figure [ fig : arch ] ) .  our research on _rasid _ is motivated by several factors : first , the technologies that can be used to provide the desired detection capability ( e.g. cameras , ir sensors , radio tomographic imaging , pressure sensors , etc ) share the requirement of installing special hardware .in addition , cameras and ir sensors are limited to line - of - sight vision and thus the cost of covering an area might be prohibitive .moreover , regular cameras fail to work in the dark or in the presence of smoke ._ rasid _ avoids these drawbacks by using the already installed wireless infrastructure without installing any special hardware .it also makes use of the fact that rf waves do not require los for propagation .  from another perspective , the previously proposed wlan _ dfp _detection techniques provide good performance under strong assumptions , which limit their application domain .for example , they are not robust to changes in the environment .that is they do not adapt to changes in the environment , e.g. humidity and temperature changes .moreover , their parameters need to be changed as the deployment area changes . in addition, the technique proposed in requires the construction of a human motion profile which leads to high overhead inside large - scale environments .the cost of this technique may be prohibitive , as it requires access to all areas of a building which might include restricted or private areas and requires several hours of calibration .finally , all techniques were either evaluated in controlled environments , e.g. , or in small - scale real environments , e.g. .  in order to achieve its objectives ,_ rasid _ uses a statistical anomaly detection technique to detect motion inside indoor environments._rasid _ only constructs a non - parametric profile for the signal strength readings received at the mps when there is no human activity during a short training phase of only two minutes , leading to minimal deployment overhead ._ rasid _ also employs techniques for continuously updating its silence profile to adapt to the environment changes .the system also applies a decision refinement procedure in order to reduce the false alarms due to the signal noise .furthermore , _rasid _ also provides an interface by which the regions of activity can be identified .we evaluate the system in two different large - scale environments rich in multi - path and compare _ rasid _ to the state - of - the - art _ dfp _ detection techniques .our results show that _ rasid _ achieves its goals of high accuracy in both environments with minimal deployment overhead .in addition , it is robust to changes in the environment .  in summary ,the contributions of this paper are four - fold :  * we present the architecture and implementation of _ rasid _ : a system that provides robust device - free motion detection along with techniques for adapting to environment changes and handling the wireless signal noise .* we analyze different signal strength features that can be used for detection and identify the most promising one .* we evaluate the system in two different large - scale real testbeds and compare it to the state - of - the - art _ dfp _ detection techniques . *we present a comparison of parametric and non - parametric approaches for system operation .the rest of this paper is organized as follows : section [ sec : relatedwork ] reviews related work .section [ sec : rasid ] presents the _ rasid _ system architecture and operation . section [ sec : evaluation ] presents the experimental evaluation of _ rasid _ and a comparison with other techniques .section [ sec : analyticalmodel ] compares the non - parametric approach used in the system to a parametric analytical model for the system operation . in section [ sec : discussion ] , we discuss our experience with _ rasid _ and present some open research issues for future work .finally , section [ sec : conclusion ] concludes the paper and discusses future work .motion detection in * device - based * systems has been an active field of research .several works have been proposed to detect the motion of an entity carrying a device either with the use of special hardware like accelerometers or motion sensors , or by using the existing network infrastructures like wireless networks and gsm .  from the * device - free * perspective, multiple technologies can be used to provide the desired capabilities including : ultra - wide band radar , computer vision , physical contact based systems and radio tomographic imaging .other technologies include the usage of wireless sensors for tracking transceiver - free objects as well as the usage of rfid tags .those technologies share the requirement of installing special hardware to handle the device - free different functionalities .in addition , cameras and ir sensors are limited to line - of - sight vision and thus they require a high cost deployment to cover all site regions .moreover , regular cameras can fail to work in the dark or in the presence of smoke , and they can cause privacy concerns .ultra - wide band radar based techniques also suffer from high complexity .moreover , some techniques can require high density to provide full coverage like radio tomographic imaging and physical contact based systems using pressure sensors .wlan device - free passive systems try to avoid the above drawbacks by using the already available wireless infrastructure .the concept of device - free passive detection and tracking using wlans was first proposed in with a large number of applications including intrusion detection , border protection , smart homes , and traffic estimation .techniques for _ dfp _ detection and tracking were introduced .the proposed techniques for the detection capability are either based on time - series analysis like the moving average and moving variance techniques proposed in or based on classification using the maximum likelihood estimation .  in comparison ,_ rasid _ uses anomaly detection techniques to identify the deviations from the normal ( silence ) state ._ rasid _ system uses a semi - supervised statistical technique that models the learned normal behavior using a kernel - function based non - parametric estimation . the kernel - function based anomaly detection has been used in several applications where the distribution of the normal behavior is not known .for example , non - parametric estimation using gaussian kernels was used in network intrusion detection and novelty detection applied to oil flow data . also , density estimation using epanechnikov kernels was used in online outlier detection in sensor data and to achieve continuous adaptive outlier detection on distributed data streams .compared to the previously proposed wlan _detection techniques , the usage of the statistical anomaly detection technique , along with the other techniques devised for adapting to environment changes and refining the decision , enable _ rasid _ to achieve low deployment overhead , high accuracy and high robustness .in this section , we give the details of the _ rasid _ system .we start by an overview of the system architecture followed by the details of the system modules .figure [ fig : arch ] gives an overview of the system architecture .the modules of the proposed system are implemented in the application server that collects samples from the monitoring points and processes them .the system works in two phases : 1 ) a short _ offline _ phase , during which the system studies the signal strength values when no human is present inside the area of interest to construct what we call a `` normal or silence profile '' for each stream .the profiles of all streams are constructed concurrently in that short phase .2 ) a _ monitoring _ phase , in which the system collects readings from the monitoring points and decides whether there is human activity ( anomalous behavior ) or not based on the information gathered in the offline phase .it also updates the stored normal profile so that it can adapt to environment changes .finally , a decision refinement procedure is applied to further enhance the accuracy .the _ normal profile construction module _ constructs the initial silence profiles based on a short , typically two minutes , training sample taken when there is no human motion present in the area of interest .( section [ sec : nprofile ] )  the _ basic detection module _ examines each stream readings in the monitoring phase and decides whether there is an anomalous behavior or not .this operation is applied to each stream independently .it also assigns an anomaly score to each stream to express the intensity of the anomalous behavior .( section [ sec : basic ] )  the _ normal profile update module _ updates the normal profiles constructed in the offline phase in order to adapt to changes in the environment .( section [ sec : uprofile ] )  the _ decision refinement module _ applies heuristic methods to refine the decision generated by the basic detection module to reduce the false alarm rates .( section [ sec : refine ] )  the _ region tracking interface _ provides an interface that visualizes the output of the above modules .this interface enables the user to identify the detected events and provides the regions of the moving entities .( section [ sec : ui ] )  we start by giving the mathematical notations followed by the details of the different modules .let be the number of streams , which is equal to the number of aps times the number of mps .let denote the received signal strength ( rss ) reading for a stream that is received at a time instant .the system studies the behavior of a sliding window of size that ends at time , i.e. $ ] .  in order to study the behavior of the sliding windows ,each sliding window is mapped to a single _ feature _ or value through a function .for example , if the mean is the selected feature , then .two types of features can be considered : measures of central tendency , such as the mean , and measures of dispersion or variation , such as the variance .the purpose of the normal profile construction module is to construct a normal profile , capturing the received signal strength characteristics when there is no human in the area of interest .this is used later by other modules to detect anomalies .this module runs in the offline phase .it extracts the feature values from the sliding windows over the collected data and estimates its distribution .the density function of the feature values observed is estimated using non - parametric kernel density estimation , we present the motivation for using a non - parametric approach by providing a performance comparison with a parametric modeling of the system operation . ] .this is done for each stream independently .figure [ fig : basic ] illustrates the operation .    formally , for a stream , given a set of sliding windows , each of length samples ,each window is mapped to a value , where .assume is the density function representing the distribution of the observed s , then given a random sample , the estimated density function is given by : where is the bandwidth and is the kernel function .the choice of the kernel function is not significant for the results of the approximation .hence , we choose the epanechnikov kernel as it is bounded and efficient to integrate :    also , we used scott s rule to estimate the optimal bandwidth : where is an estimate for the standard deviation for the s .  after estimating the density function for the feature values extracted from the sliding windows , critical boundsare selected so that if the feature values observed in the monitoring state exceed those bounds , the observed values are considered anomalous .given a significance parameter and assuming is the cdf of distribution shown in equation [ eq : kernel_density ] , if the feature is a measure of central tendency , which can deviate to the left or the right , then lower and upper bounds will be calculated such that the lower bound is and the upper bound is .however , if the feature is a measure of dispersion , which can only deviate in the positive ( or right ) direction , then an upper bound is only needed and is equal to . in the next subsection, we study different features that can be selected .as the system requires an offline phase before operation , to learn the behavior of the signal readings in the normal state , the selected feature for system operation should be resistant to possible environmental changes that may affect the stored data , e.g. temporal variations .in addition , the selected feature should also be sensitive to the human motion to enhance the detection accuracy .  in this section, we compare two categories of features : central tendency measures and dispersion measures .the goal of this study is to identify the category that will be more promising for the system operation . for this study, we consider the mean as a central tendency measure , and the standard deviation as a measure of dispersion .we use the standard deviation , rather than the variance , as the variance is a squared measure , while the mean is not.   the selected feature should be sensitive to human activity . to compare the two features, we use the euclidean distance between the normalized histograms representing the silence and motion states .the euclidean distance is defined as the square root of the sum of the squared distance between each corresponding histogram bin .the histograms are constructed over a two - minute period for each state using testbed 1 , which is discussed later in section [ sec : evaluation ] .figure [ fig : human - effect ] shows the comparison versus different window sizes .the figure shows that the distance between the histograms of the standard deviation is larger than the distance between the histograms of the mean .this indicates that the standard deviation feature is more discriminant of the human motion than the mean feature .this conclusion can be justified by observing the motion effect on typical wireless signals .figure [ fig : rss - example ] provides a visualization of the raw signal strength for two different streams during silence and human motion periods .the figure shows that in the case of human motion , the fluctuations can be up or down around the normal / silence signal level , which leads to a limited effect on the mean as compared to the standard deviation .as the proposed system requires a learning phase before operation , it is necessary to reduce the temporal variation effect on the stored profiles . to compare the two features , we use two different _ * silence * _ data sets collected two weeks apart .figure [ fig : temporal ] shows the results .the more similar the histograms , the more resistive the feature is to the introduced variations .the figure shows that the standard deviation feature is less affected by temporal variations .this is due to the fact that the standard deviation is a relative measure as it is calculated with respect to the mean , whereas the mean itself provides an absolute value that is more susceptible to be affected by changes in the conditions .    from this study, we conclude that the measures of dispersion , e.g. the standard deviation or variance , are more suitable for our proposed system . for the rest of the paper, we use the sample variance as the selected feature .the basic detection module runs during the monitoring phase .the purpose of this module is to detect signal strength anomalies , i.e. human presence , based on the normal profiles constructed during the offline phase . in particular , for a window of samples for stream at a given time instant , the module calculates the corresponding feature value , i.e. the sample variance .a stream is considered anomalous if is above a critical bound .given a significance parameter and assuming is the cdf of distribution shown in equation [ eq : kernel_density ] , the upper bound will be equal to the percentile of the cdf function , such that .the basic detection module declares a global alarm when any stream is anomalous .this approach can lead to many false positives due to signal strength outliers .this is enhanced later by the decision refinement module .the basic detection module also calculates an anomaly score for each stream to keep track of the significance of any anomalous activity . for a given window , , the anomaly score , ,can be calculated as : where is the sample variance of the window and is the critical value .this means that a detected anomaly will have a score greater than one and a silence window will have a score of less than one .the anomaly score is used by the normal profile update and decision refinement modules to further enhance the accuracy .  in summary ,the basic detection procedure requires two parameters : the window size and the significance .analysis of both parameters is presented in section [ sec : evaluation - performance ] .   due to the dynamic changes in the environment , the stored profiles may not capture the real normal state .therefore , the systems needs to update the stored profiles during the online phase .the technique we employ for handling the update process is based on continuously updating the estimated density in equation [ eq : kernel_density ] , by adding s , that do not have high anomaly scores in average to it . in particular , during the monitoring phase , the system groups the consecutive s in disjoint groups of size .the group that has an average anomaly score of less than one is added to the normal profile .the parameter can be tuned to provide the desired performance .we quantify the effect of the parameter in detail in section [ sec : evaluation - performance - normal ] .adding new data to the normal profiles implies the need to give more weight to the recent data .therefore , equation [ eq : kernel_density ] is modified to :    where .we choose linear weights such that ( is constant ) .we found that exponential weights do not provide good performance due to the high discrimination introduced between older and newer data .typical wireless environments are noisy .this fact can cause many false alarms if the system generates alarms just based on a single stream .the goal of the decision refinement module is to reduce the false alarm rate by fusing different streams .since the _ basic detection module _ assigns an anomaly score to each detected event that expresses its significance , this can be leveraged to enhance the detection performance .the decision refinement module studies the behavior of a global anomaly score that is calculated by summing the individual anomaly scores for each stream . if a noticeable change in occurs , based on a threshold , while at least one stream is anomalous , this implies the start of an anomalous behavior .the module makes use of the history of the activity state inside the environment through the usage of _ exponential smoothing _ to monitor the in order to avoid the noisy samples , hence reducing the false alarm rate .it also implicitly makes use of the locality of human motion , meaning that the human will continue to affect the same stream and/or other streams near it , causing the sum of anomaly scores smoothed curve to have higher values during the motion period ( figure [ fig : sum ] ) .the system provides an interface that provides information about the probable regions of the detected event .this is based on visualizing the anomaly degree of each stream enabling the user to identify the regions that probably have moving entities inside .this is done by coloring each pixel on the map according to its distance from each stream endpoints and according to the anomaly score of each stream .figure [ fig : shot ] displays the output of this interface when two persons are moving inside a typical site , showing the true locations of the two persons .in this section , we study the effect of the different parameters on the performance of the _ rasid _ system and compare it to the previous wlan _ dfp _ detection techniques .we collected two sets of data to evaluate the system performance , each in a different testbed .the first testbed is an office of approximately 2000 ft .the second experiment was conducted in a two - floor home building where each floor was approximately 1500 ft .both tesbeds were covered with typical furniture . for both testbeds , we used four cisco aironet 1130ag series access points and used three dell laptops equipped with d - link airplus g+ dwl-650 + wireless nics as mps .the access points were operating on different channels .the experiments were conducted in typical ieee 802.11b environments .figures [ fig : exp1 ] and [ fig : exp2 ] show the layouts of both experiments .  for the data collection , sets of normal ( silence ) state readings and continuous motion readingswere collected for each testbed .a total of about one hour and 15 minutes of data was collected for each testbed with a sampling rate of one sample per second using the active scanning technique . for testbed 1 , this includes three motion sets , while for testbed 2 , this includes two motion sets .a motion set covers the entire area of the testbed , as shown in figures [ fig : exp1 ] and [ fig : exp2 ] , and represents the motion of a single person walking normally around the site without any stops .  for system evaluation ,extreme conditions were employed : the training period is chosen to be only the first two minutes of the entire data collected with the absence of human motion .in addition , only one person moved in the area of interest .more people in the area of interest will lead to higher variance and hence better detection .therefore , the reported results present a lower bound on the performance of the _ rasid _ system .we used three metrics to analyze the detection performance : the false positive ( fp ) rate , the false negative ( fn ) rate and the f - measure .the false positive rate refers to the probability that the system generates an alarm while there is no human motion in the area of interest .the false negative rate refers to the probability that the system fails to detect the human motion in any place in the area .we also use the f - measure , which provides a single value to measure the effectiveness of the detection system .since each anomalous sample may not be detected simultaneously , we also studied the detection latency , i.e. how much time the system needs to associate an anomalous sample with a detected event .the overall detection latency percentile in both testbeds was found to be less than one second .  [cols=">,>,^,^,^ " , ]   table [ tab : comparison ] shows the comparison results in two cases .  in terms of the static accuracy , the results show that the f - measure of the _ rasid _ system is better than other systems in testbed 1 and is slightly lower than the mle technique in testbed 2 .compared to the moving average and moving variance techniques , the _ rasid _ system provides high accuracy due to the techniques it uses to enhance the performance . on the other hand ,the mle technique achieves slightly higher accuracy in testbed 2 as it stores a motion profile , which requires much higher overhead than the _ rasid _ system .  in terms of profiles robustness, the moving average technique does not store any profiles .therefore , its overall performance is low but almost the same as the profiles change . on the other hand ,the robustness of the mle technique is the least as it uses the mean signal strength values as the features used for classification .therefore , after two weeks , the distribution of the signal strength does not follow the learned one .this is why the fp rate for the mle technique is too high due to the shift that occurred in the signal distributions .it can also be noted that _rasid _ performance in the two cases was the best because _ rasid _ uses the variance for its operation ( dispersion feature ) and employs techniques for adapting to changes in the environment and for enhancing the performance .this is why _ rasid _ performance is better than the moving variance in general , although the moving variance uses the same feature as _rasid_. in terms of overhead , the moving average technique has the minimum overhead as it does not need any learning phase . the moving variance and _ rasid_ deployment need to construct normal profiles by collecting samples for two minutes when the human is not present . on the other hand , the mle technique has the worst overhead as it constructs motion profile at each location in the area of interest in addition to the normal profile .  in summary , although the static detection accuracy of _ rasid _ is as accurate as the mle technique , the mle technique has significantly higher overhead than _ rasid _ because of its motion profile requirements .in addition , _ rasid _ is the most robust technique to temporal changes in the training profiles and significantly outperforms the remaining techniques .in this section , we compare the performance of the system s non - parametric approach to an analytical model that models the sample variance parametrically .the results of this model can help validate the results of our parameter analysis in the previous section and can also motivate the usage of the non - parametric density estimation .the next evaluation will be based on the results of the basic detection module only , so as to evaluate the two approaches without the enhancements .first , we describe the analytical model , then we present the results of the comparison .the sample variance can be modeled parametrically given some conditions .according to cochran s theorem , the sample variance of independent normally distributed random samples follows a chi - square distribution with degrees of freedom such that , where is the population variance . according to ,the signal strength readings ( in dbm ) distributions for a stream can be assumed to follow a normal distribution . given that assumption , a parametric model can be devised for the system when the sample variance is used . given a significance and a window size , the upper bound for the sample variance observed during the monitoring phase is .however , also stated that the normality assumption may not hold in some cases .in addition , the signal strength readings may not be independent .thus , we believe that the non - parametric model described before will provide better performance than the parametric model .this will be verified in the following subsection .first , to check how close the parametric model is to the actual system , we compare the critical upper bounds obtained by both methods . for example , in figure [ fig : critical - variance - comparison ] we compare the critical sample variance values in both cases for the stream ap4-mp3 from experiment 1 , when the population variance is assumed to be 2.02 dbm which is an experimental estimate for the population variance of that stream .the figure shows that the parametric model and the actual system critical values follow the same trends .however the difference between the curves suggests that the real case does not exactly follow the assumed parametric model .in addition , the effects of basic detection module parameters can be inferred from the parametric model curves .as the window size parameter increases , the critical variance value decreases which results in increased system sensitivity ( i.e. higher fp rate and lower fn rate ) . also , as the significance parameter increases , the critical variance value decreases which also results in increased sensitivity .this is consistent with the analysis presented in figure  [ fig : basic - param - analysis ] .the next point is to study how the usage of the parametric model instead of non - parametric estimation can affect the system performance .as the distribution of the sample variance depends on the population variance , we analyze its effect .figure [ fig : model - performance ] shows the effect of the population variance on the performance of the basic detection module when the parametric model is used for experiment 1 . from the figure , we can conclude that the best performance achieved in terms of the f - measure ( 0.843 ) is less than the f - measure obtained using non - parametric estimation ( 0.8683 ) .to conclude , the parametric model leads to lower performance compared to the non - parametric estimation because the assumptions that the signal strength values are independent and follow a normal distribution may not hold .also , the parametric model requires the selection of an accurate population variance .this can not be done accurately without training for long time periods .therefore , we conclude that _ rasid _ approach of constructing non - parametric profiles in a short offline phase and updating them in the online phase does provide a better option .   and .the best population variance configuration provides an f - measure of 0.843 compared to 0.8683 that was obtained using the non - parametric estimation.,scaledwidth=50.0% ]in this section , we discuss some points related to the configuration and the performance of the _ rasid _ system .we also highlight some research issues and some challenges that can be addressed in future work .   as mentioned before , the basic detection module studies each stream independently by estimating the univariate density for the selected feature of the sliding windows extracted from the training data .another possibility was to construct a multivariate density estimate for the data of all streams .this implies a modification to the anomaly detection criteria .different algorithms can be applied in this case , e.g. .our experience with this algorithm shows that this leads to a degradation of the system accuracy .the main reason for this degradation is that the system sensitivity is significantly reduced , especially when the number of streams is large . in that case, the system may not be able to detect an anomaly in one stream only , as its effect may not be much sensed .   typically in real wireless environments, it is expected that many monitoring points may be using the wireless network for handling typical tasks ( e.g. downloading updates or patches ) .the question is whether such network activities will require any change in the system normal profiles if they were originally collected while there is no network activity . in this subsection, we present an experimental study to investigate that effect .  in order to examine that effect ,a simple experiment was conducted on a single stream between an access point and a laptop acting as a monitoring point in silence state .two signal strength data sets were collected while there was no network activity at the monitoring point , while another two sets were collected while the monitoring point were downloading data through the wireless stream with the maximum download speed allowed ( 50 kbytes per second ) .the collected data are used to construct normal profiles in the same way presented earlier in section [ sec : nprofile ] .figure [ fig : network - activity ] compares the constructed profiles for the four sets . from the figure ,it is clear that the difference between the distributions in both cases is negligible .furthermore , we apply the two - sample kolmogorov - smirnov test to each of the four different pairs of those constructed profiles .the test accepted the hypotheses that those estimated distributions came from the same underlying distribution with a significance of 0.05 . therefore, we can conclude that the constructed sample variance profiles are invariant with respect to the state of network activity .the above experiments showed that the system is capable of detecting a single person moving inside the area of interest .obviously , the detection performance will be enhanced if there were more than one entity in the area of interest .we verified that the system will be able to declare that there is anomalous behavior inside the area more clearly in this case .it would be useful to _ identify _ the number of moving entities in some applications .figure [ fig : shot ] shows that we can detect that there are multiple entities in the area of interest .however , as our system uses limited data to satisfy the feasibility design goal ( normal profiles only ) , the system can not provide full information about the number of entities in all cases .for example , if two entities are affecting a single stream only , the system will detect them as one entity .this is because there is no enough information that enables the system to differentiate between the two cases . on the other hand , in some cases , the system can tell with high probability that some events are due to independent entities . here , we briefly describe the constraints through which the system can provide information about the number of independent entities .first , let , a square matrix denote what we call a minimum time reachability matrix .each entry in this matrix stores the minimum time needed for an entity to affect two streams and , such that    , where represents the minimum distance between the nearest two points on the and streams lines of sight and represents the maximum movement velocity inside the area .the distance can be calculated from the site map , and can be estimated based on empirical observations .two events and are considered independent ( i.e. not generated by the same entity ) , if they satisfy the following conditions .first , they should be affecting two different streams and and second , the time difference between and is less than the value .the time difference between the two events are calculated based on the time difference between the times when the anomaly scores for the two events reach the peaks as they express the moments when the entities are affecting the streams the most . to tell that events are independent, each pair of those events should satisfy the conditions described above .the above conditions imply that the system can not detect more than moving entities , where is the number of streams as stated earlier .  to conclude , despite the limited information the system uses, the system can provide information about the number of independent events inside the monitored area given some conditions .the significance of this point can be clear when applied inside large scale environments .another possibility is to use the level of the change in variance as an indication of the number of entities .the hypothesis is that the more human affecting a single stream , the higher the variance should be .this hypothesis still needs to be verified though .our system can provide useful information to _ dfp _ tracking systems like the ones proposed in .first , a _ dfp _tracking system can use our system to decide whether to start the tracking process or not .also , the system can enhance the tracking accuracy by limiting the probable locations to a certain area ( e.g. as in figure [ fig : shot ] ) .in addition , given the conditions described earlier , our system can help the tracking system identify the number of intruders and the area of each one , so that it can apply the tracking algorithms to each area independently . this will need further investigation and experimentation .although we showed in this paper that using the variance as a feature is better than using the mean , both features can be used concurrently to achieve better performance .our initial results show that combining both features and using a simple voting scheme can enhance the results in some cases .this is a subject for future investigations .the synchronization of the signal strength readings received at the monitoring point can be necessary in some cases .for example , the technique described before for checking the independence of the detected events requires synchronization of the readings across the streams .in addition , the decision refinement module requires the different streams to be synchronized . in this paper , we took a centralized approach for synchronization , where the application server requests the mps to initiate a reading .other approaches , such as time synchronization of the mps can be employed .the advantages and disadvantages of each technique in terms of accuracy and overhead can also be investigated .the hardware used to capture the signal strength values can affect system performance .through our experiments , we studied how the wlan nic type affects the quality of the collected readings .we found that nics differ in two main aspects : sensitivity to human activity and noise readings .for example , some cards can not sense the human shadowing effect unless it is sustained for a sufficient period of time .the readings of some other cards are noisy and requires extensive filtering .these experiments considered the nics only . howeverthis can hold from the aps perspective too .therefore , we believe a study is needed to identify which hardware will be more suitable for the system operation and how to account for these variation between cards and allow the system to operate with different cards .in this paper , we presented the _rasid _ system , a system that enables device - free passive motion detection using the already installed wireless networks ._ rasid _ uses non - parametric statistical anomaly detection techniques to provide the detection capability .the _ rasid _ system also employs profile update techniques to capture changes in the environment and to enhance the detection accuracy .the system was evaluated in two different real environments . using the same parameters for the two testbeds , the system provided an accurate detection capability reaching an f - measure of at least 0.93 in both testbeds .the performance of the _ rasid _ system was compared to the previously introduced techniques for wlan _ dfp _ detection .the results showed that the _ rasid _ system outperformed the state - of - the - art techniques in terms of robustness and accuracy .in addition , we showed that the non - parametric approach employed by _rasid _ has significant advantages over a parametric approach for the system operation .currently , we are expanding _ rasid _ in several directions : one direction is to integrate _ rasid _s detection capability with _tracking systems while considering larger testbeds .another direction is to study possible sources of noise in typical wireless environments , e.g. other devices inside or outside the area of interest , and how 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Abstract: wlan device - free passive ( _ dfp _ ) indoor localization is an emerging technology enabling the localization of entities that do not carry any devices nor participate actively in the localization process using the already installed wireless infrastructure . this technology is useful for a variety of applications such as intrusion detection , smart homes and border protection .   we present the design , implementation and evaluation of _ rasid _ , a _ dfp _ system for human motion detection . _ rasid _ combines different modules for statistical anomaly detection while adapting to changes in the environment to provide accurate , robust , and low - overhead detection of human activities using standard wifi hardware . evaluation of the system in two different testbeds shows that it can achieve an accurate detection capability in both environments with an f - measure of at least 0.93 . in addition , the high accuracy and low overhead performance are robust to changes in the environment as compared to the current state of the art _ dfp _ detection systems . we also relay the lessons learned during building our system and discuss future research directions .