Scalability is a major issue for Internet of Things (IoT) as the total amount of traffic data collected and or the number of sensors deployed grow. In some IoT applications such as healthcare, power consumption is also a key design factor for the IoT devices. In this paper, a multi-signal compression and encoding method based on Analog Joint Source Channel Coding (AJSCC) is proposed that works fully in the analog domain without the need for power-hungry Analog-to-Digital Converters (ADCs). Compression is achieved by quantizing all the input signals but one. While saving power, this method can also reduce the number of devices by combining one or more sensing functionalities into a single device (called 'AJSCC device'). Apart from analog encoding, AJSCC devices communicate to an aggregator node (FPMM receiver) using a novel Frequency Position Modulation and Multiplexing (FPMM) technique. Such joint modulation and multiplexing technique presents three mayor advantages&#x2014;it is robust to interference at particular frequency bands, it protects against eavesdropping, and it consumes low power due to a very low Signal-to-Noise Ratio (SNR) operating region at the receiver. Performance of the proposed multi-signal compression method and FPMM technique is evaluated via simulations in terms of Mean Square Error (MSE) and Miss Detection Rate (MDR), respectively.
This paper is the first work to propose this structure for multiplexing data of AJSCC sensors. Table compares the power numbers of our circuit @cite_7 with state-of-the-art wireless sensor motes (all of which are digital). We can notice that @math is possible with our circuit, which is essential in low-power applications. The existing circuit realizations of spiral-type mapping also are all based on digital circuits and systems @cite_6 . In this scenario, it is worth noting that the Hybrid Digital Analog (HDA) coding can also perform signal compression @cite_26 @cite_17 . However, the digital part still needs digitization of the signals. Contrary to all these approaches, we propose signal compression and encoding in the analog domain with no need for ADCs DACs Microprocessors. To show the feasibility of our vision (analog compression and encoding), we previously developed a novel circuit to compress two signals @cite_7 and verified its applicability to two pathological signals (molecular biomarkers and physiological signal) @cite_21 . In this paper, we extend the theory for N-dimensional signal compression and propose novel multiplexing techniques that address the above mentioned challenges of scalability and power in the context of healthcare IoT as one of the key applications.
@cite_26 @cite_7 @cite_21 @cite_6 @cite_17
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A low-power wearable wireless sensor measuring both molecular biomarkers and physiological signals is proposed, where the former are measured by a microfluidic biosensing system while the latter are measured electrically. The low-power consumption of the sensor is achieved by an all-analog circuit implementing Analog Joint Source-Channel Coding (AJSCC) compression. The sensor is applicable to a wide range of biomedical applications that require real-time concurrent molecular biomarker and physiological signal monitoring. Sensors are embedded in security-critical applications from medical devices to nuclear power plants, but their outputs can be spoofed through signals transmitted by attackers at a distance. To address the lack of a unifying framework for evaluating the effect of such transmissions, we introduce a system and threat model for signal injection attacks. Our model abstracts away from specific circuit-design issues, and highlights the need to characterize the response of Analog-to-Digital Converters (ADCs) beyond their Nyquist frequency. This ADC characterization can be conducted using direct power injections, reducing the amount of circuit-specific experiments to be performed. We further define the concepts of existential, selective, and universal security, which address attacker goals from mere disruptions of the sensor readings to precise waveform injections. As security in our framework is not binary, it allows for the direct comparison of the level of security between different systems. We additionally conduct extensive experiments across all major ADC types, and demonstrate that an attacker can inject complex waveforms such as human speech into ADCs by transmitting amplitude-modulated (AM) signals over carrier frequencies up to the GHz range. All ADCs we test are vulnerable and demodulate the injected AM signal, although some require a more fine-tuned selection of the carrier frequency. We finally introduce an algorithm which allows circuit designers to concretely calculate the security level of real systems, and we apply our definitions and algorithm in practice using measurements of injections against a smartphone microphone. Overall, our work highlights the importance of evaluating the susceptibility of systems against signal injection attacks, and introduces both the terminology and the methodology to do so. A low-complexity and low-power all-analog circuit is proposed to perform efficiently Analog Joint Source Channel Coding (AJSCC). The proposed idea is to adopt Voltage Controlled Voltage Source (VCVS) to realize the rectangular-type mapping in AJSCC. The proposal is verified by Spice simulations as well as via breadboard and Printed Circuit Board (PCB) implementations. Field testing results indicate that the design is feasible for low-complexity and low-power systems such as wireless sensor networks for environmental monitoring. The recently developed compressive sensing (CS) framework enables the design of sub-Nyquist analog-to-digital converters. Several architectures have been proposed for the acquisition of sparse signals in large swaths of bandwidth. In this paper we consider a more flexible multi-channel signal model consisting of several discontiguous channels where the occupancy of the combined bandwidth of the channels is sparse. We introduce a new compressive acquisition architecture, the compressive multiplexer (CMUX), to sample such signals. We demonstrate that our architecture is CS-feasible and suggest a simple implementation with numerous practical advantages. Advances in the field of synthetic biology have been key to demonstration of molecular computing systems in general and DNA in particular. This paper presents an overview of how continuous-time, discrete-time, and digital signal processing systems can be implemented using molecular reactions and DNA. In this paper, discrete-time systems refer to sampled signals with continuous signal amplitude. Signals that are sampled in discrete time steps with digital amplitude are referred to as digital signals. Delay elements in sampled signals are implemented using molecular reactions in the form of molecular transfer reactions. Completion of all phases of transfer reactions once corresponds to a computation cycle. These molecular systems can be implemented in a fully-synchronous, globally-synchronous locally-asynchronous or fully-asynchronous manner. The paper also presents molecular sensing systems where molecular reactions are used to implement analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). Molecular implementations of digital logic systems are presented. A complete example of the addition of two molecules using digital implementation is described where the concentrations of two molecules are converted to digital by two 3-bit ADCs, and the 4-bit output of the digital adder is converted to analog by a 4-bit DAC. This system is demonstrated using both molecular reactions and DNA. A brief comparison of molecular and electronic systems is also presented. Data compression techniques have extensive applications in power-constrained digital communication systems, such as in the rapidly-developing domain of wireless sensor network applications. This paper explores energy consumption tradeoffs associated with data compression, particularly in the context of lossless compression for acoustic signals. Such signal processing is relevant in a variety of sensor network applications, including surveillance and monitoring. Applying data compression in a sensor node generally reduces the energy consumption of the transceiver at the expense of additional energy expended in the embedded processor due to the computational cost of compression. This paper introduces a methodology for comparing data compression algorithms in sensor networks based on the figure of merit D E, where D is the amount of data (before compression) that can be transmitted under a given energy budget E for computation and communication. We develop experiments to evaluate, using this figure of merit, different variants of linear predictive coding. We also demonstrate how different models of computation applied to the embedded software design lead to different degrees of processing efficiency, and thereby have significant effect on the targeted figure of merit. We propose a novel measurement system that evaluates the frequency stability of high-precision oscillators such as atomic oscillators. This measurement system consists of a pair of high-speed analog-to-digital converters (ADCs) and digital-signal-processing (DSP) circuits on a field programmable gate array. The pair of high-speed ADCs simultaneously digitizes the two sinusoidal-voltage outputs from the oscillator under test and a reference oscillator. The DSP circuits calculate the phase difference between the two sinusoidal voltages. The frequency stability evaluated as the Allan deviation sigmay(tau) is calculated from the phase difference. The main advantage of this system is that the hardware configuration is much more compact than the conventional systems using a dual mixer and a local oscillator. Nonetheless, the system noise floor of the prototype at the nominal frequency of 5 MHz is sigmay(1000 s) = 6.8 times 10-17, which is sufficient to evaluate a commercial hydrogen maser oscillator and is better by a factor of 5 than our previously reported system. We have verified that the measurement accuracy of the prototype depends on the nominal frequency and the level of the sinusoidal voltages, but it does not depend on the initial phase difference and offset frequency. Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use. The article introduces digital image restoration to the reader who is just beginning in this field, and provides a review and analysis for the reader who may already be well-versed in image restoration. The perspective on the topic is one that comes primarily from work done in the field of signal processing. Thus, many of the techniques and works cited relate to classical signal processing approaches to estimation theory, filtering, and numerical analysis. In particular, the emphasis is placed primarily on digital image restoration algorithms that grow out of an area known as "regularized least squares" methods. It should be noted, however, that digital image restoration is a very broad field, as we discuss, and thus contains many other successful approaches that have been developed from different perspectives, such as optics, astronomy, and medical imaging, just to name a few. In the process of reviewing this topic, we address a number of very important issues in this field that are not typically discussed in the technical literature. Compressed sensing is a new area of signal processing. Its goal is to minimize the number of samples that need to be taken from a signal for faithful reconstruction. The performance of compressed sensing on signal classes is directly related to Gelfand widths. Similar to the deeper constructions of optimal subspaces in Gelfand widths, most sampling algorithms are based on randomization. However, for possible circuit implementation, it is important to understand what can be done with purely deterministic sampling. In this note, we show how to construct sampling matrices using finite fields. One such construction gives cyclic matrices which are interesting for circuit implementation. While the guaranteed performance of these deterministic constructions is not comparable to the random constructions, these matrices have the best known performance for purely deterministic constructions. We analyse the pros and cons of analog versus digital computation in living cells. Our analysis is based on fundamental laws of noise in gene and protein expression, which set limits on the energy, time, space, molecular count and part-count resources needed to compute at a given level of precision. We conclude that analog computation is significantly more efficient in its use of resources than deterministic digital computation even at relatively high levels of precision in the cell. Based on this analysis, we conclude that synthetic biology must use analog, collective analog, probabilistic and hybrid analog–digital computational approaches; otherwise, even relatively simple synthetic computations in cells such as addition will exceed energy and molecular-count budgets. We present schematics for efficiently representing analog DNA–protein computation in cells. Analog electronic flow in subthreshold transistors and analog molecular flux in chemical reactions obey Boltzmann exponential laws of thermodynamics and are described by astoundingly similar logarithmic electrochemical potentials. Therefore, cytomorphic circuits can help to map circuit designs between electronic and biochemical domains. We review recent work that uses positive-feedback linearization circuits to architect wide-dynamic-range logarithmic analog computation in Escherichia coli using three transcription factors, nearly two orders of magnitude more efficient in parts than prior digital implementations. In this paper, we study transmission of a memoryless Laplacian source over three types of channels: additive white Laplacian noise (AWLN), additive white Gaussian noise (AWGN), and slow flat-fading Rayleigh channels under both bandwidth compression and bandwidth expansion. For this purpose, we analyze two well-known hybrid digital-analog (HDA) joint source-channel coding schemes for bandwidth compression and one for bandwidth expansion. Then we obtain achievable (absolute-error) distortion regions of the HDA schemes for the matched signal-to-noise ratio (SNR) case as well as the mismatched SNR scenario. Using numerical examples, it is shown that these schemes can achieve a distortion very close to the provided lower bound (for the AWLN channel) and to the optimum performance theoretically attainable bound (for AWGN and Rayleigh fading channels) on mean-absolute error distortion under matched SNR conditions. In addition, a non-linear analog coding scheme is analyzed, and its performance is compared to the HDA schemes for bandwidth compression under both matched and mismatched SNR scenarios. The results show that the HDA schemes outperform the non-linear analog coding over the whole CSNR region. Abstract It is now well-known that one can reconstruct sparse or compressible signals accurately from a very limited number of measurements, possibly contaminated with noise. This technique known as “compressed sensing” or “compressive sampling” relies on properties of the sensing matrix such as the restricted isometry property . In this Note, we establish new results about the accuracy of the reconstruction from undersampled measurements which improve on earlier estimates, and have the advantage of being more elegant. To cite this article: E.J. Candes, C. R. Acad. Sci. Paris, Ser. I 346 (2008). Preface Hearing-Aid Technology Types of Hearing Aids From Analog to Digital Digital Circuit Components Batteries Concluding Remarks References Signal Processing Basics Signal and System Properties Discrete Fourier Transform Filters and Filter Banks Block Processing Digital System Concerns Concluding Remarks References The Electroacoustic System Hearing Aid System Head and Ear Microphone and Receiver Vent Acoustics Occlusion Effect Concluding Remarks References Directional Microphones Hearing-Aid Microphones Directional Response Patterns Frequency Response Magnitude Frequency Response Microphone Mismatch Interaction with Vents Microphone Noise Microphones on the Head Microphone Performance Indices Rooms and Reverberation Benefit in the Real World Concluding Remarks References Adaptive and Multi-Microphone Arrays Two-Microphone Adaptive Array Delay-And-Sum Beamforming Adaptive Arrays Superdirective Arrays Widely-Spaced Arrays Array Benefits Concluding Remarks References Wind Noise Turbulence Hearing-Aid Measurements Signal Characteristics Wind-Noise Reduction Concluding Remarks References Feedback Cancellation The Feedback System Gain-Reduction Solutions Adaptive Feedback Cancellation Processing Limitations Concluding Remarks References Dynamic-Range Compression Does Compression Help? Algorithm Design Concerns Single-Channel Compression Multi-Channel Compression Frequency-Domain Compression Frequency Warping Concluding Remarks References Single-Microphone Noise Suppression Properties of Speech and Noise Signals Low-Level Expansion Envelope Valley Tracking Bandwidth Reduction Envelope Modulation Filters Concluding Remarks References Spectral Subtraction Noise Estimation Wiener Filter Spectral Subtraction Algorithm Effectiveness Concluding Remarks References Spectral Contrast Enhancement Auditory Filters in the Damaged Cochlea Spectral Valley Suppression Spectral Contrast Modification Excess Upward Spread of Masking F2 F1 Ratio Processing Comparison Combining Spectral Contrast Enhancement with Compression Concluding Remarks References Sound Classification The Rationale for Classification Signal Features Feature Selection Classifier Algorithms Classification Examples Concluding Remarks References Binaural Signal Processing The "Cocktail Party" Problem Signal Transmission Binaural Compression Binaural Noise Suppression Dichotic Band Splitting Concluding Remarks References Index A skip and fill algorithm is developed to digitally self-calibrate pipelined analog-to-digital converters (ADC's) in real time. The proposed digital calibration technique is applicable to capacitor-ratioed multiplying digital-to-analog converters (MDACs) commonly used in multistep or pipelined ADCs. This background calibration process can replace, in effect, a trimming procedure usually done in the factory with a hidden electronic calibration. Unlike other self-calibration techniques working in the foreground, the proposed technique is based on the concept of skipping conversion cycles randomly but filling in data later by nonlinear interpolation. This opens up the feasibility of digitally implementing calibration hardware and simplifying the task of self-calibrating multistep or pipelined ADCs. The proposed method improves the performance of the inherently fast ADCs by maintaining simple system architectures. To measure errors resulting from capacitor mismatch, of amp DC gain, offset, and switch feedthrough in real time, the calibration test signal is injected in place of the input signal using a split-reference injection technique. Ultimately, the missing signal within two-thirds of the Nyquist bandwidth is recovered with 16-b accuracy using a forty-fourth order polynomial interpolation, behaving essentially as an FIR filter,. Vision is one of the most useful sensory functions, but the real-time processing of the continuous, high-dimensional input signals provided by vision sensors is a major challenge in robot design. Conventional digital vision sensors tend to have excessive power consumption, size, and cost for useful applications. In this TechView, Indiveri and Douglas discuss an alternative strategy, namely neuromorphic vision sensors that are based on biological vision systems. In these sensors, specialized sensory processing functions inspired by biological systems such as fly eyes are integrated in parallel, asynchronous circuits that respond in real time. These sensors offer significant advantages over conventional vision sensors. Precoding combining and large antenna arrays are essential in millimeter wave (mmWave) systems. In traditional MIMO systems, precoding combining is usually done digitally at baseband with one radio frequency (RF) chain and one analog-to-digital converter (ADC) per antenna. The high cost and power consumption of RF chains and ADCs at mmWave frequencies make an all-digital processing approach prohibitive. When only a limited number of RF chains is available, hybrid architectures that split the precoding combining processing into the analog and digital domains are attractive. A previously proposed hybrid solution employs phase shifters and mixers in the RF precoding combining stage. It obtains near optimal spectral efficiencies with a reduced number of RF channels. In this paper we propose a different hybrid architecture, which simplifies the hardware at the receiver by replacing the phase shifters with switches. We present a new approach for compressed sensing based channel estimation for the hybrid architectures. Given the channel estimate, we propose a novel algorithm that jointly designs the antenna subsets selected and the baseband combining. Using power consumption calculations and achievable rates, we compare the performance of hybrid combining with antenna switching and phase shifting, showing that antenna selection is preferred in a range of operating conditions. To construct sophisticated biochemical circuits from scratch, one needs to understand how simple the building blocks can be and how robustly such circuits can scale up. Using a simple DNA reaction mechanism based on a reversible strand displacement process, we experimentally demonstrated several digital logic circuits, culminating in a four-bit square-root circuit that comprises 130 DNA strands. These multilayer circuits include thresholding and catalysis within every logical operation to perform digital signal restoration, which enables fast and reliable function in large circuits with roughly constant switching time and linear signal propagation delays. The design naturally incorporates other crucial elements for large-scale circuitry, such as general debugging tools, parallel circuit preparation, and an abstraction hierarchy supported by an automated circuit compiler. Convolutional Neural Network (CNN) is a powerful technique widely used in computer vision area, which also demands much more computations and memory resources than traditional solutions. The emerging met al-oxide resistive random-access memory (RRAM) and RRAM crossbar have shown great potential on neuromorphic applications with high energy efficiency. However, the interfaces between analog RRAM crossbars and digital peripheral functions, namely Analog-to-Digital Converters (AD-Cs) and Digital-to-Analog Converters (DACs), consume most of the area and energy of RRAM-based CNN design due to the large amount of intermediate data in CNN. In this paper, we propose an energy efficient structure for RRAM-based CNN. Based on the analysis of data distribution, a quantization method is proposed to transfer the intermediate data into 1 bit and eliminate DACs. An energy efficient structure using input data as selection signals is proposed to reduce the ADC cost for merging results of multiple crossbars. The experimental results show that the proposed method and structure can save 80 area and more than 95 energy while maintaining the same or comparable classification accuracy of CNN on MNIST. Embedded and cyber-physical systems are critically dependent on the integrity of input and output signals for proper operation. Input signals acquired from sensors are assumed to correspond to the phenomenon the system is monitoring and responding to. Similarly, when such systems issue an actuation signal it is expected that the mechanism being controlled will respond in a predictable manner. Recent work has shown that sensors can be manipulated through the use of intentional electromagnetic interference (IEMI). In this work, we demonstrate thatboth input and output signals, analog and digital, can be remotely manipulated via the physical layer---thus bypassing traditional integrity mechanisms. Through the use of specially crafted IEMI it is shown that the physical layer signaling used for sensor input to, and digital communications between, embedded systems may be undermined to an attacker's advantage. Three attack scenarios are analyzed and their efficacy demonstrated. In the first scenario the analog sensing channel is manipulated to produce arbitrary sensor readings, while in the second it is shown that an attacker may induce bit flips in serial communications. Finally, a commonly used actuation signal is shown to be vulnerable to IEMI. The attacks are effective over appreciable distances and at low power.