SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization

Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 182, July 1978

This bibliography lists 165 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1978

Distributed UAV Swarm Augmented Wideband Spectrum Sensing Using Nyquist Folding Receiver

Distributed unmanned aerial vehicle (UAV) swarms are formed by multiple UAVs with increased portability, higher levels of sensing capabilities, and more powerful autonomy. These features make them attractive for many recent applica-tions, potentially increasing the shortage of spectrum resources. In this paper, wideband spectrum sensing augmented technology is discussed for distributed UAV swarms to improve the utilization of spectrum. However, the sub-Nyquist sampling applied in existing schemes has high hardware complexity, power consumption, and low recovery efficiency for non-strictly sparse conditions. Thus, the Nyquist folding receiver (NYFR) is considered for the distributed UAV swarms, which can theoretically achieve full-band spectrum detection and reception using a single analog-to-digital converter (ADC) at low speed for all circuit components. There is a focus on the sensing model of two multichannel scenarios for the distributed UAV swarms, one with a complete functional receiver for the UAV swarm with RIS, and another with a decentralized UAV swarm equipped with a complete functional receiver for each UAV element. The key issue is to consider whether the application of RIS technology will bring advantages to spectrum sensing and the data fusion problem of decentralized UAV swarms based on the NYFR architecture. Therefore, the property for multiple pulse reconstruction is analyzed through the Gershgorin circle theorem, especially for very short pulses. Further, the block sparse recovery property is analyzed for wide bandwidth signals. The proposed technology can improve the processing capability for multiple signals and wide bandwidth signals while reducing interference from folded noise and subsampled harmonics. Experiment results show augmented spectrum sensing efficiency under non-strictly sparse conditions

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Strategies towards high performance (high-resolution/linearity) time-to-digital converters on field-programmable gate arrays

Time-correlated single-photon counting (TCSPC) technology has become popular in scientific research and industrial applications, such as high-energy physics, bio-sensing, non-invasion health monitoring, and 3D imaging. Because of the increasing demand for high-precision time measurements, time-to-digital converters (TDCs) have attracted attention since the 1970s. As a fully digital solution, TDCs are portable and have great potential for multichannel applications compared to bulky and expensive time-to-amplitude converters (TACs). A TDC can be implemented in ASIC and FPGA devices. Due to the low cost, flexibility, and short development cycle, FPGA-TDCs have become promising. Starting with a literature review, three original FPGA-TDCs with outstanding performance are introduced. The first design is the first efficient wave union (WU) based TDC implemented in Xilinx UltraScale (20 nm) FPGAs with a bubble-free sub-TDL structure. Combining with other existing methods, the resolution is further enhanced to 1.23 ps. The second TDC has been designed for LiDAR applications, especially in driver-less vehicles. Using the proposed new calibration method, the resolution is adjustable (50, 80, and 100 ps), and the linearity is exceptionally high (INL pk-pk and INL pk-pk are lower than 0.05 LSB). Meanwhile, a software tool has been open-sourced with a graphic user interface (GUI) to predict TDCs’ performance. In the third TDC, an onboard automatic calibration (AC) function has been realized by exploiting Xilinx ZYNQ SoC architectures. The test results show the robustness of the proposed method. Without the manual calibration, the AC function enables FPGA-TDCs to be applied in commercial products where mass production is required.Time-correlated single-photon counting (TCSPC) technology has become popular in scientific research and industrial applications, such as high-energy physics, bio-sensing, non-invasion health monitoring, and 3D imaging. Because of the increasing demand for high-precision time measurements, time-to-digital converters (TDCs) have attracted attention since the 1970s. As a fully digital solution, TDCs are portable and have great potential for multichannel applications compared to bulky and expensive time-to-amplitude converters (TACs). A TDC can be implemented in ASIC and FPGA devices. Due to the low cost, flexibility, and short development cycle, FPGA-TDCs have become promising. Starting with a literature review, three original FPGA-TDCs with outstanding performance are introduced. The first design is the first efficient wave union (WU) based TDC implemented in Xilinx UltraScale (20 nm) FPGAs with a bubble-free sub-TDL structure. Combining with other existing methods, the resolution is further enhanced to 1.23 ps. The second TDC has been designed for LiDAR applications, especially in driver-less vehicles. Using the proposed new calibration method, the resolution is adjustable (50, 80, and 100 ps), and the linearity is exceptionally high (INL pk-pk and INL pk-pk are lower than 0.05 LSB). Meanwhile, a software tool has been open-sourced with a graphic user interface (GUI) to predict TDCs’ performance. In the third TDC, an onboard automatic calibration (AC) function has been realized by exploiting Xilinx ZYNQ SoC architectures. The test results show the robustness of the proposed method. Without the manual calibration, the AC function enables FPGA-TDCs to be applied in commercial products where mass production is required