An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Factors Contributing to Energy Consumption in Acoustic Sensing <s> It is argued analog circuits will remain irreplaceable for the implementation of the interface circuitry between digital processing and the external world. Most of their characteristics can be improved with scaled-down processes. Analog circuits will also compete with digital and will remain advantageous for low precision signal processing. Since little precision is required to carry out many cognitive tasks, analog VLSI (very-large-scale integration) will dominate in the implementation of many types of neural networks. > <s> BIB001 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Factors Contributing to Energy Consumption in Acoustic Sensing <s> Pervasive micro-sensing and actuation may revolutionize the way in which we understand and manage complex physical systems: from airplane wings to complex ecosystems. The capabilities for detailed physical monitoring and manipulation offer enormous opportunities for almost every scientific discipline, and it will alter the feasible granularity of engineering. We identify opportunities and challenges for distributed signal processing in networks of these sensing elements and investigate some of the architectural challenges posed by systems that are massively distributed, physically-coupled, wirelessly networked, and energy limited. <s> BIB002 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Factors Contributing to Energy Consumption in Acoustic Sensing <s> In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramer-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator. <s> BIB003 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Factors Contributing to Energy Consumption in Acoustic Sensing <s> A broad range of embedded networked sensor (ENS) systems for critical environmental monitoring applications now require complex, high peak power dissipating sensor devices, as well as on-demand high performance computing and high bandwidth communication. Embedded computing demands for these new platforms include support for computationally intensive image and signal processing as well as optimization and statistical computing. To meet these new requirements while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture, low power energy aware processing (LEAP), has been developed. The LEAP architecture integrates fine-grained energy dissipation monitoring and sophisticated power control scheduling for all subsystems including sensor subsystems. This paper also describes a new distributed node testbed demonstrating that by exploiting high high energy efficiency components and enabling proper on-demand scheduling, the LEAP architecture may meet both sensing performance and energy dissipation objectives for a broad class of applications. <s> BIB004
There are several primary contributors to the energy requirements of an acoustic sensor system, including: the analog front end, sampling, digital processing, storage, and network transmission. Analog Front End and Sampling. As a rule, the circuits required to amplify and sample the acoustic sensors draw a significant amount of power, at best between 10 and 100 mW, and often considerably more. 1 This is partly due to theoretical lower bounds on energy requirements BIB001 and partly due to inefficiencies imposed by practical implementations  . Achieving low power performance often requires custom designs, since off-the-shelf solutions are often not designed for energy saving. Some applications may only need to be active at certain specified times when the phenomenon of interest occurs, and can "duty cycle" by shutting the system down when it is not needed. But for those applications that require continuous digital monitoring, there is no way around this cost. In some cases, a continuously running low power analog filter can be used to trigger digital sampling and processing. However, if the trigger circuit incurs latency, this type of design will typically lose some part of the triggering event in the interim before the sampling process starts. Digital Processing. The energy requirements of a main processor board vary widely depending on the architecture, clock rate, and the amount and type of RAM. Processor architecture and feature sets have a wide-ranging impact, including bit width, virtual memory support, floating point support, "MMX" instruction sets, support for special digital signal processing (DSP) features, and voltage and clock scaling. Often specialized DSP chips can yield performance gains, although these solutions tend to be more customized and typically do not support a full-featured operating system, which makes building a sophisticated system more difficult. The cost of refreshing dynamic RAM (DRAM) is one of the most significant power costs after the CPU itself, and scales with the size of the RAM. This cost dominates in situations where the processor is idle or in "standby mode." Saving the RAM to persistent storage and shutting down the CPU completely eliminates that cost, but incurs a long startup latency. One promising new technology is "Unified Memory," in which a small, fast SRAM is used as a write cache for a larger flash memory. Such a solution can avoid both the power cost of DRAM and the limited speed and write cycles of flash. Persistent Storage. Persistent storage technologies include flash, hard drives, and new "hybrid" drives that are just starting to become available from Samsung. Flash has a low power cost and high mechanical robustness, but has many disadvantages; in particular, the slow speed of the erase cycle, the limited number of permitted write-erase cycles, and the smaller capacities relative to hard drives. Hard drives are very costly in power and must be protected against shock, although smaller drives tend to be more shock resistant. The lowest power and most durable hard drives currently available are "microdrives," which typically require about 45 mW standby power and 800 mW when writing. 2 Hybrid drives are a forthcoming technology that couple a hard drive to a large flash write buffer. Internal software writes new data to flash, only spinning up the drive when necessary to clear space in the flash. The flash write buffer eliminates the standby cost and optimizes write costs. Networking. Wireless networking is another significant energy cost. Since wireless systems must emit energy to communicate, there are theoretical lower bounds on the energy costs BIB002 . In addition, inter-node coordination, channel estimation and negotiation, and recovery from message loss introduce significant additional overhead. In general, wireless networks impose a significant energy cost when idle, waiting to receive, and higher costs to actually send and receive data. For example, the SMC 2532W 802.11B card draws 0.5 W in "power save" mode and 3 W when transmitting. BIB003 To avoid this idle cost, the system must completely shut down the radio, which requires additional coordination in the network to determine when to bring the network up. In some instances, a low-power paging channel can be used for coordination, as in the LEAP platform BIB004 and related work.