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1. Investigation and Design of a Towable Hydrophone Array for General Ocean Sensing

   https://doi.org/10.1109/OCEANSE.2019.8867239  

   Schinault, Matthew E.; Penna, Seth M.; Garcia, Heriberto A.; Ratilal, Purnima (June 2019, IEEE OCEANS 2019 - Marseille)

   An eight-element oil-filled hydrophone array is used to measure the acoustic field in littoral waters. This prototype array was deployed during an experiment between Jeffrey’s Ledge and the Stellwagen Bank region off the coast of Rockport, Massachusetts USA. During the experiment, several humpback whale vocalizations, distant ship tonals and high frequency conventional echosounder pings were recorded. Visual confirmation of humpback moving in bearing relative to the array verifies the directional sensing from array beamforming. During deployment, the array is towed at speeds varying from 4-7 kts in water depths of roughly 100 m with conditions at sea state 2 to 3. This array system consists of a portable winch with array, tow cable and 3 water-resistant boxes housing electronics. This system is deployed and operated by 2 crew members onboard a 13 m commercial fishing vessel during the experiment. Non-acoustic sensor (NAS) information is obtained to provide depth, temperature, and heading data using commercial off the shelf (COTS) components utilizing RS485/232 data communications. Acoustic data sampling was performed at 8 kHz, 30 kHz and 100 kHz with near real-time processing of data and enhanced Signal to Noise Ratio (SNR) from beamforming. The electrical system components are deployed with 3 stacked electronics boxes housing power, data acquisition and data processing components in water resistant compartments. A laptop computer with 8 TB of external storage and an independent Global Positioning System (GPS) antenna is used to run Passive Ocean Acoustic Waveguide Remote Sensing (POAWRS) software providing beamformed spectrogram data and live NAS data with capability of capturing several days of data. The acquisition system consists of Surface Mount Device (SMD) pre-amplifiers with filter to an analog differential pair shipboard COTS acquisition system. Pre-amplifiers are constructed using SMD technology where components are pressure tolerant and potting is not necessary. Potting of connectors, electronics and hydrophones via 3D printed molding techniques will be discussed. Array internal components are manufactured with Thermoplastic Polyurethane (TPU) 3D printed material to dampen array vibrations with forward and aft vibration isolation modules (VIM). Polyurethane foam (PUF) used to scatter breathing waves and dampen contact from wires inside the array without attenuating high frequencies and allowing for significant noise reduction. A single Tygon array section with a length of 7.5 m and diameter of 38 mm contains 8 transducer elements with a spacing of 75 cm (1 kHz design frequency). Pre- amplifiers and NAS modules are affixed using Vectran and steel wire rope positioned by swaged stops along the strength member. The tow cable length is 100 m with a diameter of 22 mm that is potted to a hose adapter to break out 12 braided copper wire twisted pair conductors and terminates the tow cable Vectran braid. This array in its current state of development is a low-cost alternative to obtain quality acoustic data from a towed array system. Used here for observation of whale vocalizations, this type of array also has many applications in military sonar and seismic surveying. Maintenance on the array can be performed without the use of special facilities or equipment for dehosing and conveniently uses castor oil as an environmentally safe pressure compensating and coupling fluid. Array development including selection of transducers, NAS modules, acoustic acquisition system, array materials and method of construction with results from several deployments will be discussed. We also present beamformed spectrograms containing humpback whale downsweep moans and underwater blowing (bubbles) sounds associated with feeding on sand lance (Ammodytes dubius). 

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2. A Low-power Reconfigurable Readout Circuit with Large DC Offset Reduction for Neural Signal Recording Applications

   https://doi.org/10.1109/MWSCAS48704.2020.9184526  

   Tasneem, Nishat; Mahbub, Ifana (August 2020, 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS))

   null (Ed.)

   This paper presents a fully reconfigurable readout circuit including a chopper-stabilized neural amplifier and a successive approximation register (SAR) analog-to-digital converter (ADC) for neural signal recording applications. Since the target neural signals - action potentials (APs) and local field potentials (LFPs) differ in the peak amplitude while occupying different frequency bandwidths, gain, and bandwidth reconfigurability would be advantageous in improving power and noise performance. The readout circuit is designed in 180 nm standard CMOS technology. It achieves the mid-band gain of 50.3 dB in the frequency band of 0.1 Hz - 250 Hz to detect the LFPs, and 63.4 dB in 267 Hz - 20.8 kHz for detecting the APs. The neural amplifier consumes a total power of 1.54 μW and 1.94 μW for LFP and AP configurations, respectively. The input-referred noises have been achieved as 0.97 μV rms (0.1 Hz - 250 Hz), and 0.44 μV rms (250 Hz - 5 kHz), leading to a noise efficiency factor (NEF) of 1.27 and 1.21, for the two configurations, respectively. It rejects the generated large DC offset up to 40 mV at the electrode-tissue interface, by implementing a DC servo loop (DSL). The offset voltage with the DSL becomes 0.23 mV, which is acceptable for the neural experiments. Enabling the impedance boosting loop, the DC input impedance is found to be within the range of 1.77 - 2.27 GΩ, introducing the reconfigurability in impedance for matching with the electrode impedance. The SAR-ADC having a varying sampling frequency ranging from 10 - 40 ksamples/s demonstrates to digitize the APs and the LFPs with the resolution from 8 - 10 bits. The entire AFE provides good compatibility to record the neural signal while lowering the large DC offset down to 0.23 mV. 

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3. Towards quantum belief propagation for LDPC decoding in wireless networks

   https://doi.org/10.1145/3372224.3419207  

   Kasi, Srikar; Jamieson, Kyle (September 2020, Proceedings of the 26th Annual International Conference on Mobile Computing and Networking (ACM MobiCom))

   We present Quantum Belief Propagation (QBP), a Quantum Annealing (QA) based decoder design for Low Density Parity Check (LDPC) error control codes, which have found many useful applications in Wi-Fi, satellite communications, mobile cellular systems, and data storage systems. QBP reduces the LDPC decoding to a discrete optimization problem, then embeds that reduced design onto quantum annealing hardware. QBP's embedding design can support LDPC codes of block length up to 420 bits on real state-of-the-art QA hardware with 2,048 qubits. We evaluate performance on real quantum annealer hardware, performing sensitivity analyses on a variety of parameter settings. Our design achieves a bit error rate of 10--8 in 20 μs and a 1,500 byte frame error rate of 10--6 in 50 μs at SNR 9 dB over a Gaussian noise wireless channel. Further experiments measure performance over real-world wireless channels, requiring 30 μs to achieve a 1,500 byte 99.99% frame delivery rate at SNR 15-20 dB. QBP achieves a performance improvement over an FPGA based soft belief propagation LDPC decoder, by reaching a bit error rate of 10--8 and a frame error rate of 10--6 at an SNR 2.5--3.5 dB lower. In terms of limitations, QBP currently cannot realize practical protocol-sized (e.g., Wi-Fi, WiMax) LDPC codes on current QA processors. Our further studies in this work present future cost, throughput, and QA hardware trend considerations. 

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4. Neural Network-based Channel Estimation for 2x2 and 4x4 MIMO Communication in Noisy Channels

   Yang, Kanghyeok; Vuran, Mehmet C.; Scott, Stephen; Guo, Fujuan; Ahn, Changbum R. (May 2018, International Balkan Conference on Communications and Networking)

   With increasing needs of fast and reliable commu- nication between devices, wireless communication techniques are rapidly evolving to meet such needs. Multiple input and output (MIMO) systems are one of the key techniques that utilize multiple antennas for high-throughput and reliable communication. However, increasing the number of antennas in communication also adds to the complexity of channel esti- mation, which is essential to accurately decode the transmitted data. Therefore, development of accurate and efficient channel estimation methods is necessary. We report the performance of machine learning-based channel estimation approaches to enhance channel estimation performance in high-noise envi- ronments. More specifically, bit error rate (BER) performance of 2 × 2 and 4 × 4 MIMO communication systems with space- time block coding model (STBC) and two neural network-based channel estimation algorithms is analyzed. Most significantly, the results demonstrate that a generalized regression neural network (GRNN) model matches BER results of a known-channel communication for 4 × 4 MIMO with 8-bit pilots, when trained in a specific signal to noise ratio (SNR) regime. Moreover, up to 9dB improvement in signal-to-noise ratio (SNR) for a target BER is observed, compared to least square (LS) channel estimation, especially when the model is trained in the low SNR regime. A deep artificial neural network (Deep ANN) model shows worse BER performance compared to LS in all tested environments. These preliminary results present an opportunity for achieving better performance in channel estimation through GRNN and highlight further research topics for deployment in the wild. 

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5. Single-Frequency Impedance Studies on an Ionic Liquid-Based Miniaturized Electrochemical Sensor toward Continuous Low-Temperature CO ₂ Monitoring

   https://doi.org/10.1021/acssensors.2c02040  

   Sridhar, Arun Siddarth; Chen, Xiaoyu; Glossmann, Tobias; Yang, Ziming; Xu, Yong; Lai, Wei; Zeng, Xiangqun (January 2023, ACS Sensors)

   Continuous greenhouse gas monitoring at sub-zero temperatures is needed for monitoring greenhouse gas emission in cold environments such as the Arctic tundra. This work reports a single-frequency electrochemical impedance sensing (SF-EIS) method for real-time continuous monitoring of carbon dioxide (CO2) at a wide range of temperatures (−15 to 40 °C) by using robust ionic liquid (IL) sensing materials and noninvasive, low-power, and low-cost impedance readout mechanisms since they cause minimal changes in the sensing interface, avoiding the baseline change for long-term continuous sensing. In addition, a miniaturized planar electrochemical sensor was fabricated that incorporates a hydrophobic 1-butyl-1-methylpyrrolidinium bis(trifluromethylsulfonyl)imide ([Bmpy][NTf2]) IL electrolyte and Pt black electrode materials. The high viscosity of the ILs facilitates the formation of thin, ordered, and concentrated layers of ionic charges, and the inverse relationship of IL viscosity with temperature makes them especially suited for impedance sensing at low temperatures. The unique low-temperature properties of ILs together with EIS transduction mechanisms are shown to be sensitive and selective for continuously monitoring CO2 at a −15 to 40 °C temperature range via impedance changes at a specifically selected frequency at the open circuit potential (OCP). Molecular dynamics simulations revealed insights into the structure and dynamics of the IL at varying temperatures in the presence of methane and CO2 and provided potential explanations for the observed sensing results. The miniaturized and flexible planar electrochemical sensor with the [Bmpy][NTf2] electrolyte was tested repeatedly at subzero temperatures over a 58-day period, during which good stability and repeatability were obtained. The CO2 impedance sensor was further tested for sensing CO2 from soil samples and shows promising results for their use in real-time monitoring of greenhouse gas emissions in cold temperatures such as permafrost soils. 

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