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Intracortical brain-machine interfaces have shown promise for restoring function to people with paralysis, but their translation to portable and implantable devices is hindered by their high power consumption. Recent devices have drastically reduced power consumption compared to standard experimental brain-machine interfaces, but still require wired or wireless connections to computing hardware for feature extraction and inference. Here, we introduce a Neural Recording And Decoding (NeuRAD) application specific integrated circuit (ASIC) in 180 nm CMOS that can extract neural spiking features and predict two-dimensional behaviors in real-time. To reduce amplifier and feature extraction power consumption, the NeuRAD has a hardware accelerator for extracting spiking band power (SBP) from intracortical spiking signals and includes an M0 processor with a fixed-point Matrix Acceleration Unit (MAU) for efficient and flexible decoding. We validated device functionality by recording SBP from a nonhuman primate implanted with a Utah microelectrode array and predicting the one- and two-dimensional finger movements the monkey was attempting to execute in closed-loop using a steady-state Kalman filter (SSKF). Using the NeuRAD's real-time predictions, the monkey achieved 100% success rate and 0.82 s mean target acquisition time to control one-dimensional finger movements using just 581 μW. To predict two-dimensional finger movements, the NeuRAD consumed 588 μW to enable the monkey to achieve a 96% success rate and 2.4 s mean acquisition time. By employing SBP, ASIC brain-machine interfaces can close the gap to enable fully implantable therapies for people with paralysis.

This paper introduces an indoor-monitoring LiDAR sensor for patients with Alzheimer disease residing in long-term care facilities (LTCFs), and this sensor exploits an optoelectronic analog front-end (AFE) to detect light signals from targets by utilizing on-chip avalanche photodiodes (APDs) realized in a 180 nm CMOS process and a neural processing unit (NPU) used for motion detection and decisions, especially for incidents of falls occurring in LTCFs. The AFE consists of an on-chip CMOS P+/N-well APD, a linear-mode transimpedance amplifier, a post-amplifier, and a time-to-digital converter, whereas the NPU exploits network sparsity and approximate processing elements for low-power operation. This work provides a potential solution of low-cost, low-power, indoor-monitoring LiDAR sensors for patients with Alzheimer disease in LTCFs.

This paper presents an optimization of reflectarray-based RF sensors for detecting UAV and human presence. Our previous human detection radar system adapted a center-fed reflectarray antenna to a commercially available radar system, successfully increasing the gains of the transmit (TX) and receive (RX) antennas by 21.18 dB and the range for detecting human targets 3.4 times. However, because the TX and RX antennas were placed in the focal point of the reflectarray, the TX signal reflected by the reflectarray was directly propagated into the RX antenna, causing desensitization or damage to the receiving circuit if high powers were used. To reduce this direct reflection, we propose a novel radar antenna configuration in which the TX and RX antennas are placed back-to-back with each other. In this configuration, the RX antenna does not directly face the reflectarray, thus direct path between the TX to RX through the reflectarray is removed. The results demonstrate that this approach achieves the optimum isolation level of 51.3 dB. With the reflectarray, the TX antenna gain increases to 30.6 dBi, but the RX antenna gain remains at 16 dBi since the RX antenna does not utilize the reflectarray. The TX and RX gain difference (14.6 dB) is a trade-off for good isolation and may be reduced by utilizing a high-gain receiver amplifier.

Corticokinematic coherence (CKC) is computed between limb kinematics and cortical activity (e.g. MEG, EEG), and it can be used to detect, quantify and localize the cortical processing of proprioceptive afference arising from the body. EEG-based studies on CKC have been limited to lab environments due to bulky, non-portable instrumentations. We recently proposed a wireless and miniaturized EEG acquisition system aimed at enabling EEG studies outside the laboratory. The purpose of this work is to compare the EEG-based CKC values obtained with this device with a conventional wired-EEG acquisition system to validate its use in the quantification of cortical proprioceptive processing. Eleven healthy right-handed participants were recruited (six males, four females, age range: 24–40 yr). A pneumatic-movement actuator was used to evoke right index-finger flexion-extension movement at 3 Hz for 4 min. The task was repeated both with the wireless-EEG and wired-EEG devices using the same 30-channel EEG cap preparation. CKC was computed between the EEG and finger acceleration. CKC peaked at the movement frequency and its harmonics, being statistically significant (p < 0.05) in 8–10 out of 11 participants. No statistically significant differences (p < 0.05) were found in CKC strength between wireless-EEG (range 0.03-0.22) and wired-EEG (0.02-0.33) systems, that showed a good agreement between the recording systems (3 Hz: r = 0.57, p = 0.071, 6 Hz: r = 0.82, p = 0.003). As expected, CKC peaked in sensors above the left primary sensorimotor cortex contralateral to the moved right index finger. As the wired-EEG device, the tested wireless-EEG system has proven feasible to quantify CKC, and thus can be used as a tool to study proprioception in the human neocortex. Thanks to its portability, the wireless-EEG used in this study has the potential to enable the examination of cortical proprioception in more naturalistic conditions outside the laboratory environment. Clinical Relevance—Our study will contribute to provide innovative technological foundations for future unobtrusive EEG recordings in naturalistic conditions to examine human sensorimotor system. peerReviewed

Infraslow activity (ISA) is a biomarker that has recently become of interest in the characterization of seizure recordings. Recent data from a small number of studies have suggested that the epileptogenic zone may be identified by the presence of ISA. Investigation of low frequency activity in clinical seizure recordings, however, has been hampered by technical limitations. EEG systems necessarily include a high-pass filter early in the measurement chain to remove large artifactual drifts that can saturate recording elements such as the amplifier. This filter, unfortunately, attenuates legitimately seizure-related low frequencies, making ISA difficult to study in clinical EEG recordings. In this study, we present a deconvolution-based digital inverse filter that allows recovery of attenuated low frequency activity in intracranial recordings of temporal lobe epilepsy patients. First, we show that the unit impulse response (UIR) of an EEG system can be characterized by differentiation of the system’s step response. As proof of method, we present several examples that show that the low frequency component of a high-pass filtered signal can be restored by deconvolution with the UIR. We then demonstrate that this method can be applied to biologically relevant signals including clinical EEG recordings obtained from seizure patients. Finally, we discuss how this method can be applied to study ISA to identify and assess the seizure onset zone.

This paper presents an energy-efficient, duty-cycled, and spinning excitation bridge-to-digital converter (BDC) designed for implantable pressure sensing systems. The circuit provides the measure of the pulmonary artery pressure that is particularly relevant for the monitoring of heart failure and pulmonary hypertension patients. The BDC is made of a piezoresistive pressure sensor and a readout integrated circuit (IC) that comprises an instrumentation amplifier (IA) followed by an analog-to-digital converter (ADC). The proposed design spins both the bridge excitation and the ADC's sampling input voltages simultaneously and exploits duty cycling to reduce the static power consumption of the bridge sensor and IA while cancelling the IA's offset and 1/f noise at the same time. The readout IC has been designed and fabricated in a standard 180-nm CMOS process and achieves 8.4 effective number of bits (ENOB) at 1 kHz sampling rate while drawing 0.53 μA current from a 1.2 V supply. The BDC, built with the readout IC and a differential pressure sensor having 5 k Ω bridge resistances, achieves 0.44 mmHg resolution in a 270 mmHg pressure range at 1 ms conversion time. The current consumption of the bridge sensor by employing duty cycling is reduced by 99.8% thus becoming 0.39 μA from a 1.2 V supply. The total conversion energy of the pressure sensing system is 1.1 nJ, and achieves a figure-of-merit (FoM) of 3.3 pJ/conversion, which both represent the state of the art.

Wearable biomedical systems allow doctors to con- tinuously monitor their patients over longer periods, which is especially useful to detect rarely occurring events such as car- diac arrhythmias. Recent monitoring systems often embed signal processing capabilities to directly identify events and reduce the amount of data. This work is the first to document a complete beat- to-beat arrhythmia classification system implemented on a custom ultra-low-power microcontroller. It includes a single-channel ana- log front-end (AFE) circuit for electrocardiogram (ECG) signal acquisition, and a digital back-end (DBE) processor to execute the support vector machine (SVM) classification software with a Cortex-M4 CPU. The low-noise instrumentation amplifier in the AFE consumes 1.4 μW and has an input-referred noise of 0.9 μV RMS. The all-digital time-based ADC achieves 10-bit effective resolution over a 250-Hz bandwidth with an area of only 900 μm 2. The classification software reaches a sensitivity of 82.6% and 88.9% for supraventricular and ventricular arrhythmias respectively on the MIT-BIH arrhythmia database. The proposed system has been prototyped on the SleepRider SoC, a 28-nm fully-depleted silicon on insulator (FD-SOI) 3.1-mm2 chip. It consumes 13.1 μW on average from a 1.8-V supply.

In this study, we focus on automated optimization design methodologies to concurrently trade off between power gain, output power, efficiency, and linearity specifications in radio frequency (RF) high-power amplifiers (HPAs) through deep neural networks (DNNs). The RF HPAs are highly nonlinear circuits where characterizing an accurate and desired amplitude and phase responses to improve the overall performance is not a straightforward process. For this case, we propose a coarse and fine modeling approach based on firstly modeling the involved transistor and then selecting the best configuration of HAP along with optimizing the involved input and output termination networks through DNNs. In the fine phase, we firstly construct the equivalent modeling of the GaN HEMT transistor by using X-parameters. Then in the coarse phase, we utilize hidden layers of the modeled transistor and replace the HPA’s DNN to model the behavior of the selected HPA by using S-parameters. If the suitable accuracy of HPA modeling is not achieved, the hyperparameters of the fine model are improved and re-evaluated in the HPA model. We call the optimization process coarse and fine modeling since the evaluation process is performed from S-parameters to X-parameters. This stage of optimization can ensure modeling the nonlinear HPA design that includes a high number of parameters in an effective way. Furthermore, for accelerating the optimization process, we use the classification DNN for selecting the best topology of HPA for modeling the most suitable configuration at the coarse phase. The proposed modeling strategy results in relatively highly accurate HPA designs that generate post-layouts automatically, where multi-tone harmonic balance specifications are optimized once together without any human interruptions. To validate the modeling approach and optimization process, a 10 W HPA is simulated and measured in the operational frequency band of 1.8 GHz to 2.2 GHz, i.e., the L-band. The measurement results demonstrate a drain efficiency higher than 54% and linear gain performance more than 12.5 dB, with better than 50 dBc adjacent channel power ratio (ACPR) after DPD.

Conducting cells of the heart and nerve cells of the brain are having the ability to generate and transmit electrical signals. Recording of neural signals became an important research issue for better analysis and better control of neurological functions by using implantable devices. In neural recording systems, the most critical part is the power constraint neural amplifier. The major challenges of neural front ends are low power dissipation and low input-referred noise. This work describes a low-noise amplifier that uses Metal Oxide Semiconductor bipolar pseudo-resistor elements to amplify signals from 0.03 millihertz to 8.4 kilohertz. This design is suitable for neurodegenerative disorders like Parkinson's disease and Alzheimer's. This topology reduces major noise in low-frequency circuits. By choosing input devices as PMOS transistors and also by properly sizing the devices, flicker noise is reduced. Noise and power trade-off is quantified by calculating noise efficiency factor (NEF) which is improved by using the proposed design. The circuit is implemented in 180 nm technology and is operated with a dual power supply range of ±2.5 V.

Conventional glucose monitoring methods for the growing numbers of diabetic patients around the world are invasive, painful, costly and, time-consuming. Complications aroused due to the abnormal blood sugar levels in diabetic patients have created the necessity for continuous noninvasive glucose monitoring. This article presents a wearable system for glucose monitoring based on a single wavelength near-infrared (NIR) Photoplethysmography (PPG) combined with machine-learning regression (MLR). The PPG readout circuit consists of a switched capacitor Transimpedance amplifier with 1 MΩ gain and a 10-Hz switched capacitor LPF. It allows a DC bias current rejection up to 20 μA with an input-referred current noise of 7.3 pA/√Hz. The proposed digital processor eliminates motion artifacts, and baseline drifts from PPG signal, extracts six distinct features and finally predicts the blood glucose level using Support Vector Regression with Fine Gaussian kernel (FGSVR) MLR. A novel piece-wise linear (PWL) approach for the exponential function is proposed to realize the FGSVR on-chip. The overall system is implemented using a 180 nm CMOS process with a chip area of 4.0 mm2 while consuming 1.62 mW. The glucose measurements are performed for 200 subjects with R2 of 0.937. The proposed system accurately predicts the sugar level with a mean absolute relative difference (mARD) of 7.62%.

An 8-channel wearable wireless device for ambulatory surface EEG monitoring and analysis is presented. The entire multi-channel recording, quantization, and motion artifact removal circuitries are implemented on a 4-layer polyimide flexible substrate. The recording electrodes and active shielding are also integrated on the same substrate, yielding the smallest form factor compared to the state of the art. Thanks to the dry non-contact electrodes, the system is quickly mountable with minimal assistance required, making it an ideal ambulatory front- and temporal-lobe EEG monitoring device. The flexible main board is connected to a rechargeable battery on one end and to a 13 × 17 mm $^2$ rigid board on the other end. The mini rigid board hosts a low-power programmable FPGA and a BLE 5.0 transceiver, which add diagnostic capability and wireless connectivity features to the device, respectively. Design considerations for a wearable EEG monitoring and diagnostic device are discussed in details. The theory of the novel fully-analog method for motion artifact detection and removal is described and the detailed circuit implementation is presented. The device performance in terms of voltage gain (260 V/V), bandwidth (DC-300 Hz), motion artifact removal, and wireless communication throughput (up to 1 Mbps) is experimentally validated. The entire wearable solution with the battery weighs 9.2 grams.

This paper presents a low-power high-input-impedance analog-front-end (AFE) design including an instrumentational-amplifier (IA) and a neural-signal-specific ADC (NSS-ADC) for continuous acquisition of electroencephalography (EEG) signals. In the proposed AFE, low-voltage low-power design techniques are used to reduce the power consumption of the whole system. Furthermore, by utilizing the proposed NSS-ADC, high-amplitude EEG spikes, which convey more important information, are converted with higher resolutions, while the background-noise (B-Noise) of the EEG signal is converted with the lowest resolution. Hence, when the NSS-ADC enters the inactive region, the resolution- and DAC- controlling-units (RCU and DCU) set the analog and digital components of the NSS-ADC into off mode, which leads to power reduction. Based on measurement results, the AFE consumes a power of 3.7 μW under the sampling rate of 20 KS/s. In the proposed AFE, to avoid signal attenuation, active-electrodes (AEs) are utilized to enhance the input impedance of the AFE up to 102 GΩ and 5.2 GΩ at 1 Hz and 20 Hz, respectively. In addition, by using circuit-design techniques the input-referred-noise is reduced as low as 1.5 μV[Formula: see text] over 0.5-1.2 kHz. Finally, by using a transconductance-driven-right-leg (TDRL) and a common-mode-feedback (CMFB) blocks, a common-mode-rejection-ratio (CMRR) of 108 dB is achieved.

Photoplethysmography (PPG) enables wearable vitals monitoring. Nevertheless, it is still limited by the few mA of the LEDs driving current. We present a PPG sensor integrating an array of dedicated pinned-photodiodes (PPD) with a full readout chain integrated in a 0.18 $\mu$ m CMOS Image Sensor (CIS) process. The sensor features a total input referred noise of 0.68 e-rms per PPD, independently of the input light, and achieves a 4.6 $\mu$ W total power consumption, including the 2 $\mu$ W LED power, at 1.38 bpm heart rate average error.

Monolithic integration of photoacoustic (PA) sensor with compact size, lightweight, and low power consumption is attractive to be implemented on wearable medical devices for in vivo blood metabolic sensing and imaging. This work presents a miniaturized chip-scale mixed-signal photoacoustic sensor system which can achieve coherent lock-in function to detect weak target PA signals noninvasively at in vivo scenarios of poor signal to noise ratio (SNR) and strong interferences. A low-noise amplifier (LNA), a 3rd order Butterworth low-pass filter (LPF), and a variable-gain amplifier (VGA) chain with 10 MHz cutoff frequency are implemented on-chip to attain a high-quality sensing performance with 50-dB dynamic range. A Gilbert-cell type multiplier is integrated on-chip to fulfill the coherent lock-in process on acquired PA signals in a closed-loop process with an embedded FPGA system. Fabricated in 65-nm CMOS technology, the prototype PA sensor system demonstrated 50 μV sensitivity. The functions of the chip-scale PA sensor system enhanced by coherent lock-in process were validated through the experiments on temperature monitoring and vessel imaging. The PA receiver chip occupies an area of 0.6 mm2 and consumes 20 mW at a 1.8-V supply.

Photothermal microscopy has enabled highly sensitive label-free imaging of absorbers, from metallic nanoparticles to chemical bonds. Photothermal signals are conventionally detected via modulation of excitation beam and demodulation of probe beam using lock-in amplifier. While convenient, the wealth of thermal dynamics is not revealed. Here, we present a lock-in free, mid-infrared photothermal dynamic imaging (PDI) system by MHz digitization and match filtering at harmonics of modulation frequency. Thermal-dynamic information is acquired at nanosecond resolution within single pulse excitation. Our method not only increases the imaging speed by two orders of magnitude but also obtains four-fold enhancement of signal-to-noise ratio over lock-in counterpart, enabling high-throughput metabolism analysis at single-cell level. Moreover, by harnessing the thermal decay difference between water and biomolecules, water background is effectively separated in mid-infrared PDI of living cells. This ability to nondestructively probe chemically specific photothermal dynamics offers a valuable tool to characterize biological and material specimens., Photothermal microscopy is limited for imaging of thermal dynamics. Here, the authors introduce a lock-in free, mid-infrared photothermal dynamic imaging system, which significantly increases SNR and imaging speed, and demonstrate metabolism analysis at single-cell level and background removal.

CMOS microelectronics design has evolved tremendously during the last two decades. The evolution of CMOS devices to short channel designs where the feature size is below 1000 nm brings a great deal of uncertainty in the way the microelectronics design cycle is completed. After the conceptual idea, developing a thinking model to understand the operation of the device requires a good “ballpark” evaluation of transistor sizes, decision making, and assumptions to fulfill the specifications. This design process has iterations to meet specifications that exceed in number of the available degrees of freedom to maneuver the design. Once the thinking model is developed, the simulation validation follows to test if the design has a good possibility of delivering a successful prototype. If the simulation provides a good match between specifications and results, then the layout is developed. This paper shows a useful open science strategy, using the Excel software, to develop CMOS microelectronics hand calculations to verify a design, before performing the computer simulation and layout of CMOS analog integrated circuits. The full methodology is described to develop designs of passive components, as well as CMOS amplifiers. The methods are used in teaching CMOS microelectronics to students of electronic engineering with industrial partner participation. This paper describes an exhaustive example of a low-voltage operational transconductance amplifier (OTA) design which is used to design an instrumentation amplifier. Finally, a test is performed using this instrumentation amplifier to implement a front-end signal conditioning device for CMOS-MEMS biomedical applications.

Medical ultrasound has become a crucial part ofmodern society and continues to play a vital role in the diagnosis and treatment of illnesses. Over the past decades, the development of medical ultrasound has seen extraordinary progress as a result of the tremendous research advances in microelectronics, transducer technology and signal processing algorithms. However, medical ultrasound still faces many challenges including power efficient driving of transducers, low-noise recording of ultrasoundechoes, effective beamforming in a non-linear, high-attenuation medium (human tissues) and reduced overall form factor. This paper provides a comprehensive review of the design of integrated circuits for medical ultrasound applications. The most important and ubiquitous modules in a medical ultrasound system are addressed, i) transducer driving circuit, ii) low-noise amplifier, iii) beamforming circuit and iv) analog-digital converter. Within each ultrasound module, some representative research highlights are described followed by a comparison of the state-of-the-art. Thispaper concludes with a discussion and recommendations for future research directions.

This work presents an 8-channel closed-loop neuromodulation chipset with 2-level seizure classification. The power-consuming fine classifier is only enabled when the coarse classifier in the frontend chip judges the patient's status as “suspected seizure”. This scheme can reduce the overall power consumption extensively since seizure usually occurs with very low possibility. In the capacitive-coupled instrument amplifier (CCIA) of the front-end IC, a feedback based common-mode (CM) cancellation circuit is proposed to suppress large-scale CM interferences and the stimulation artifacts are suppressed by a mixed-signal loop with fast response. An auto-zero based pre- charge path is adopted to boost the input impedance, while the electrode DC offset is canceled by a DC servo loop with very-large and accurate time constant. The 2.32-mm2 front-end chip and 3.51-mm2 DSP chip implemented in 0.18 μm CMOS are applied in a deep-brain stimulation (DBS) neuromodulator. Measurement results show that the CCIA can suppress 1.5-Vpp CM interference, and achieve an accurate high-pass corner frequency as low as 0.1 Hz and an input impedance greater than 2.2 GΩ. The overall classifier achieves 97.8% sensitivity and consumes only 1.16-μW average power for the CHB-MIT database test. The chipset has been verified by in vivo measurement, showing that the stimulation artifact can be suppressed by 35 dB within 0.5 ms.

Objective. Hyperscanning is an emerging technology that concurrently scans the neural dynamics of multiple individuals to study interpersonal interactions. In particular, hyperscanning with electroencephalography (EEG) is increasingly popular owing to its mobility and its ability to allow studying social interactions in naturalistic settings at the millisecond scale. Approach. To align multiple EEG time series with sophisticated event markers in a single time domain, a precise and unified timestamp is required for stream synchronization. This study proposes a clock-synchronized method that uses a custom-made RJ45 cable to coordinate the sampling between wireless EEG amplifiers to prevent incorrect estimation of interbrain connectivity due to asynchronous sampling. In this method, analog-to-digital converters are driven by the same sampling clock. Additionally, two clock-synchronized amplifiers leverage additional radio frequency channels to keep the counter of their receiving dongles updated, which guarantees that binding event markers received by the dongle with the EEG time series have the correct timestamp. Main results. The results of two simulation experiments and one video gaming experiment reveal that the proposed method ensures synchronous sampling in a system with multiple EEG devices, achieving near-zero phase lag and negligible amplitude difference between the signals. Significance. According to all of the signal-similarity metrics, the suggested method is a promising option for wireless EEG hyperscanning and can be utilized to precisely assess the interbrain couplings underlying social-interaction behaviors.

This paper presents a compact DDA-based fully-differential CMOS instrumentation amplifier dedicated for micro-power ECG monitoring. Only eight transistors are employed to realize a power-efficient current-sharing DDA. A RC network (using MOS pseudo resistors and poly capacitors) forms feedback loops around the DDA creating an ac-only amplification. The proposed amplifier is dc-coupled via gate terminals of the p-channel input transistors. It thus achieves sufficiently high input impedance over the entire ECG frequency range. Fabricated in a 0.35-μm CMOS process, the proposed amplifier occupies 0.0712 mm

Recent technological advances have led to a rapid increase in alternative listening devices to conventional hearing aids. The aim was to systematically review the existing evidence to assess the effectiveness of alternative listening devices in adults with mild and moderate hearing loss. A systematic search strategy of the scientific literature was employed, reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) checklist. Eleven studies met eligibility for inclusion: two studies evaluated personal sound amplification products, and nine studies assessed remote microphone systems (frequency modulation, Bluetooth, wireless). The evidence in this review suggests that alternative listening devices improve behavioural measures of speech intelligibility relative to unaided and/or aided conditions. Evidence for whether alternative listening devices improve self-reported outcomes is inconsistent. The evidence was judged to be of poor to good quality and subject to bias due to limitations in study design. Our overall recommendation is that high-quality evidence (i.e. randomised controlled trials) is required to demonstrate the effectiveness of alternative listening devices. Such evidence is not currently available and is necessary to guide healthcare commissioners and policymakers when considering new service delivery models for adults with hearing loss. Review registration: Prospective Register of Systematic Reviews (PROSPERO), CRD42015029582.

The recording of biopotential signals using techniques such as electroencephalography (EEG) and electrocardiography (ECG) poses important challenges to the design of the front-end readout circuits in terms of noise, electrode DC offset cancellation and motion artifact tolerance. In this paper, we present a 2

This paper presents a 0.5 V fifth-order Butterworth low-pass filter based on multiple-input operational transconductance amplifiers (OTA). The filter is designed for electrocardiogram (ECG) acquisition systems and operates in the subthreshold region with nano-watt power consumption. The used multiple-input technique simplifies the overall structure of the OTA and reduces the number of active elements needed to realize the filter. The filter was designed and simulated in the Cadence environment using a 0.18 µm Complementary Metal Oxide Semiconductor (CMOS) process from Taiwan Semiconductor Manufacturing Company (TSMC). Simulation results show that the filter has a bandwidth of 250 Hz, a power consumption of 34.65 nW, a dynamic range of 63.24 dB, attaining a figure-of-merit of 0.0191 pJ. The corner (process, voltage, temperature: PVT) and Monte Carlo (MC) analyses are included to prove the robustness of the filter.

Biopotential sensing technology with electrodes has a great future in medical treatment and human&mdash, machine interface, whereas comfort and longevity are two significant problems during usage. Noncontact electrode is a promising alternative to achieve more comfortable and long term biopotential signal recordings than contact electrode. However, it could pick up a significantly higher level of common-mode (CM) noise, which is hardly solved with passive filtering. The impedance imbalance at the electrode&ndash, body interface is a limiting factor of this problem, which reduces the common mode rejection ratio (CMRR) of the amplifier. In this work, we firstly present two novel CM noise reduction circuit designs. The circuit designs are based on electrode-body impedance imbalance cancellation. We perform circuit analysis and circuit simulations to explain the principles of the two circuits, both of which showed effectiveness in CM noise rejection. Secondly, we proposed a practical approach to detect and monitor the electrode-body impedance imbalance change. Compared with the conventional approach, it has certain advantages in interference immunity, and good linearity for capacitance. Lastly, we show experimental evaluation results on one of the designs we proposed. The results indicated the validity and feasibility of the approach.

A bio-potential recorder working under 0.8 V supply voltage with a tunable low-pass filter is proposed in this paper. The prototype is implemented in TSMC 180 nm CMOS technology, featuring a power consumption of 2.27 $\mu$ W, while preserving a high tolerance of power-line interference (PLI) up to 600 m $V_{pp}$ , a common-mode rejection ratio (CMRR) of higher than 100 dB, a THD of −65.5 dB, and a noise density of 50 nV/ $\sqrt{Hz}$ by employing four new techniques, including 1) low noise chopper modulator, 2) feedback loop based common-mode cancellation loop (CMCL), 3) offset cancellation loop (OCL) with PMOS backgate control scheme, and 4) a very-lower transconductance (VLT) operational transconductance amplifier (OTA) using in the DC-servo-loop (DSL). The measured mid-band gain is 43.3 dB with a high-pass cut-off frequency of 1.2 Hz. The low-pass cut-off frequency can be configured from 650 Hz to 7.5 kHz. The measured input-referred integrated noise is 1.2 uVrms in the frequency band of 1-650 Hz and 4.1 uVrms in the 1 Hz–7.5 kHz frequency band, respectively, leading to a power efficiency factor (PEF) of 7.49 and 7.59.

In recent years, high-density surface electromyography (HD-sEMG) has shown promising advantages in many robotics applications. Using HD-sEMG can not only reduce the sensitivity of the sensor position on the muscle belly but can also facilitate the acquisition of more muscle activity information due to spatial sampling. As current commercial HD-EMG systems use stationary amplifiers, leading to bulky measurements and poor portability, the interest in developing an HD-EMG sensor has increased. However, the insufficient electrode density and complicated fabrication process are challenges to overcome. In this paper, we propose a flexible HD-EMG sensor with an on-board amplifier capable of a density level of 0.53 channel/cm2, higher than those in previous works. First, we investigated the effects of different sensor parameters (i.e., the electrode material, the inter-electrode distance (IED) and the size of the electrode) on the measured signal quality. Second, a low-cost, easily fabricated, easily customized HD-EMG fabrication method was proposed based on the selected sensor parameters with a signal-to-noise ratio (SNR) comparable to those of commercial sensors. Finally, we applied a muscle activation estimation algorithm to validate the feasibility of the designed HD-EMG sensor, showing higher estimation accuracy levels. The results here demonstrate that the designed HD-EMG sensor can be used as an effective human-machine interface for robotics applications.

Although not manufactured to be used under X-ray photons, the commercial bipolar junction transistor (BJT) is an electronic device that can be used as an ionizing radiation sensor. In this article an overview on the BJT and its principle of operation were made for the purpose of better understanding how such a semiconductor device behaves when under diagnostic X-ray beam. Therefore, it addresses some topics such as the structure of the device, the bias configuration when operating in active mode, and so on. Even knowing that the most complete theory to describe the “transistor effect” is based on quantum theory (the energy band theory of solids), here it is preferable to take a simpler experimental approach to clearly understand the operation of the BJT. In electronics, the BJT is used as a current amplifier, and depending on the bias and point of view it also becomes a voltage amplifier. In the analysis of BJT under an X-ray beam, in addition to its operation as a sensor to measure the dose or some diagnostic X-ray tube parameter, it has also led to technological innovation in the technique of digital data storage based on the effect of radiation.

An automatic non-contact cough detector designed especially for night audio recordings that can distinguish coughs from snores and other sounds is presented. Two different classifiers were implemented and tested: a Gaussian Mixture Model (GMM) and a Deep Neural Network (DNN). The detected coughs were analyzed and compared in different sleep stages and in terms of severity of Obstructive Sleep Apnea (OSA), along with age, Body Mass Index (BMI), and gender. The database was composed of nocturnal audio signals from 89 subjects recorded during a polysomnography study. The DNN-based system outperformed the GMM-based system, at 99.8% accuracy, with a sensitivity and specificity of 86.1% and 99.9%, respectively (Positive Predictive Value (PPV) of 78.4%). Cough events were significantly more frequent during wakefulness than in the sleep stages (p < 0.0001) and were significantly less frequent during deep sleep than in other sleep stages (p < 0.0001). A positive correlation was found between BMI and the number of nocturnal coughs (R = 0.232, p < 0.05), and between the number of nocturnal coughs and OSA severity in men (R = 0.278, p < 0.05). This non-contact cough detection system may thus be implemented to track the progression of respiratory illnesses and test reactions to different medications even at night when a contact sensor is uncomfortable or infeasible.

Several attempts that employed dynamic compression to compensate for cochlear impairment in combination with recruitment have failed to improve speech intelligibility. One reason is the attenuation of dynamic intensity modulations in the range of 1 to 50 Hz which contribute significantly to speech discrimination. In the approach presented here, these modulations were enhanced rather than attenuated by an envelope highpass filter operating in the loudness domain as follows: the output of a filterbank was lowpass filtered to extract the envelope, converted to loudness in accordance with normal hearing, highpass-filtered and converted back to the 'desired' envelope by the loudness characteristic of each individual hearing-impaired subject. The output signal was finally obtained by summing the outputs of the filterbank channels after correcting their amplitudes. The algorithm was implemented by a computer to simulate a digital hearing aid fitted to the loudness-scaling function of each individual. Loudness scaling and speech-discrimination data in quiet and in noisy conditions are presented for 2 impaired subjects with and without highpass filtering in the loudness domain. The results indicate that loudness compensation without highpass filtering improves speech intelligibility for a wide range of input levels by achieving an optimum gain characteristic at all input levels. The highpass filter applied to the envelope has no noticeable effect on speech discrimination either in quiet or in noise.

This article presents an energy-efficient electrocorticography (ECoG) array architecture for fully-implantable brain machine interface systems. A novel dual-mode analog signal processing method is introduced that extracts neural features from high- $\gamma$ band (80–160 Hz) at the early stages of signal acquisition. Initially, brain activity across the full-spectrum is momentarily observed to compute the feature weights in the digital back-end during full-band mode operation. Subsequently, these weights are fed back to the front-end and the system reverts to base-band mode to perform feature extraction. This approach utilizes a distinct optimized signal pathway based on power envelope extraction, resulting in 1.72× power reduction in the analog blocks and up to 50× potential power savings for digitization and processing (implemented off-chip in this article). A prototype incorporating a 32-channel ultra-low power signal acquisition front-end is fabricated in 180 nm CMOS process with 0.8 V supply. This chip consumes 1.05 $\mu$ W (0.205 $\mu$ W for feature extraction only) power and occupies 0.245 ${\text{mm}}^{\text{2}}$ die area per channel. The chip measurement shows better than 76.5-dB common-mode rejection ratio (CMRR), 4.09 noise efficiency factor (NEF), and 10.04 power efficiency factor (PEF). In-vivo human tests have been carried out with electroencephalography and ECoG signals to validate the performance and dual-mode operation in comparison to commercial acquisition systems.

© 2019 The Author(s). Published by Elsevier B.V. on behalf of International Federation of Clinical Neurophysiology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)., Standardization of Electromyography (EMG) instrumentation is of particular importance to ensure high quality recordings. This consensus report on "Standards of Instrumentation of EMG" is an update and extension of the earlier IFCN Guidelines published in 1999. First, a panel of experts in different fields from different geographical distributions was invited to submit a section on their particular interest and expertise. Then, the merged document was circulated for comments and edits until a consensus emerged. The first sections in this document cover technical aspects such as instrumentation, EMG hardware and software including amplifiers and filters, digital signal analysis and instrumentation settings. Other sections cover the topics such as temporary storage, trigger and delay line, averaging, electrode types, stimulation techniques for optimal and standardised EMG examinations, and the artefacts electromyographers may face and safety rules they should follow. Finally, storage of data and databases, report generators and external communication are summarized.

High-frequency oscillations (HFO) are promising EEG biomarkers of epileptogenicity. While the evidence supporting their significance derives mainly from invasive recordings, recent studies have extended these observations to HFO recorded in the widely accessible scalp EEG. Here, we investigated whether scalp HFO in drug-resistant focal epilepsy correspond to epilepsy severity and how they are affected by surgical therapy. In eleven children with drug-resistant focal epilepsy that underwent epilepsy surgery, we prospectively recorded pre- and postsurgical scalp EEG with a custom-made low-noise amplifier (LNA). In four of these children, we also recorded intraoperative electrocorticography (ECoG). To detect clinically relevant HFO, we applied a previously validated automated detector. Scalp HFO rates showed a significant positive correlation with seizure frequency (R

Objective. When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact removal. Considering this, current study aims to use correlation-driven mapping to improve artifact detection performance. Approach. A framework is proposed here for mapping signals from multichannel space (regardless of the number of EEG channels) into two-dimensional RGB space, in which the correlation of all EEG channels is simultaneously taken into account, and a deep convolutional neural network (CNN) model can then learn specific patterns in generated 2D representation related to specific artifact. Main results. The method with a classification accuracy of 92.30% (AUC = 0.96) in a leave-three-subjects-out cross-validation procedure was evaluated using data including 2310 EEG sequences contaminated by artifacts and 2285 artifact-free EEG sequences collected with BrainStatus self-adhesive electrode and wireless amplifier from 15 intensive care patients. For further assessment, several scenarios were also tested including performance variation of proposed method under different segment lengths, different numbers of isoline and different numbers of channel. The results showed outperformance of CNN fed by correlation coefficients data over both spectrogram-based CNN and EEGNet on the same dataset. Significance. This study showed the feasibility of utilizing correlation image of EEG channels coupled with deep learning as a promising tool for dimensionality reduction, channels fusion and capturing various artifacts patterns in temporal-spatial domains. A simplified version of proposed approach was also shown to be feasible in real-time application with latency of 0.0181 s for making real-time decision.

This tutorial is aimed primarily to non-engineers, using or planning to use surface electromyography (sEMG) as an assessment tool for muscle evaluation in the prevention, monitoring, assessment and rehabilitation fields. The main purpose is to explain basic concepts related to: (a) signal detection (electrodes, electrode-skin interface, noise, ECG and power line interference), (b) basic signal properties, such as amplitude and bandwidth, (c) parameters of the front-end amplifier (input impedance, noise, CMRR, bandwidth, etc.), (d) techniques for interference and artifact reduction, (e) signal filtering, (f) sampling and (g) A/D conversion, These concepts are addressed and discussed, with examples. The second purpose is to outline best practices and provide general guidelines for proper signal detection, conditioning and A/D conversion, aimed to clinical operators and biomedical engineers. Issues related to the sEMG origin and to electrode size, interelectrode distance and location, have been discussed in a previous tutorial. Issues related to signal processing for information extraction will be discussed in a subsequent tutorial.

Ions are ubiquitous biological regulators playing a key role for vital processes in animals and plants. The combined detection of ion concentration and real-time monitoring of small variations with respect to the resting conditions is a multiscale functionality providing important information on health states. This multiscale functionality is still an open challenge for current ion sensing approaches. Here we show multiscale real-time and high-sensitivity ion detection with complementary organic electrochemical transistors amplifiers. The ion-sensing amplifier integrates in the same device both selective ion-to-electron transduction and local signal amplification demonstrating a sensitivity larger than 2300 mV V−1 dec−1, which overcomes the fundamental limit. It provides both ion detection over a range of five orders of magnitude and real-time monitoring of variations two orders of magnitude lower than the detected concentration, viz. multiscale ion detection. The approach is generally applicable to several transistor technologies and opens opportunities for multifunctional enhanced bioelectronics., Though organic electrochemical transistor (OECT)-based ion sensors are attractive for highly sensitive ion detection and monitoring, its limited sensitivity hinders its practical applicability. Here, the authors report real-time, high sensitivity ion detection with complementary OECT amplifiers.

A multi-channel biopotential recording analog front-end (AFE) with a fully integrated area-efficient driven-right-leg (DRL) circuit is presented in this paper. The proposed AFE includes 10 channels of low-noise capacitive coupled instrumentation amplifier (CCIA), one shared 10-bit SAR ADC and a fully integrated DRL to enhance the system-level common-mode rejection ratio (CMRR). The proposed DRL circuit senses the common-mode at the CCIA output so that the AFE gain is reused as the DRL loop gain. Therefore, area efficient unit-gain buffer with small averaging capacitors can be used in DRL circuit to reduce the circuit area significantly. The proposed AFE has been implemented in a standard 0.18-μm CMOS process. The DRL circuit achieved more than 85% chip area reduction compared to the state-of-art on-chip DRL circuits and maximum 60 dB enhancement to system-level CMRR. Measurement results show high/low AFE gain of 60 dB/54 dB respectively with 1 μA/channel current consumption under 1.0 V power supply. The measured AFE input-referred noise in 1 Hz - 10k Hz range is 4.2 μVrms and the maximum system-level CMRR is 110 dB. Agency for Science, Technology and Research (A*STAR) This work was supported by A∗STAR (Agency for Science, Technology and Research) BMRC (Biomedical Research Council), Singapore, under Grant IAF311022. This paper was recommended by Associate Editor Y. Zheng. (Corresponding author: Yuan Gao.)

This paper presents the design of a low-power discrete-time signal-folding amplifier intended for use in place of programmable-gain amplifiers (PGA) in electrocardiogram (ECG) acquisition systems. The amplifier provides a fixed high gain while preventing output signal saturation even with rail-to-rail inputs, thanks to the proposed discrete-time signal folding technique; the fixed gain eliminates the need of gain-control circuitry while the high gain helps relax the resolution requirement of the analog-to-digital converter (ADC) that follows, thus resulting in lower power consumption and design complexity for the ADC. Fabricated in a standard 0.18- $\mu$ m CMOS process, the amplifier occupies an active area of 0.254 $\mathrm{mm}^2$ and consumes 2.64 $\mu$ W from a 1.2-V supply voltage. While amplifying a rail-to-rail input (2.4 $V_\mathrm{pp}$ differential) with a gain of 17.8 V/V, the amplifier achieves a signal-to-noise-plus-distortion ratio (SNDR) of 71 dB, thus making it very attractive for high-fidelity ECG recording amid large input interferences.

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works This paper presents a 1.2 V 36 µW reconfigurable analog front-end (R-AFE) as a general-purpose low-cost IC for multiple-mode biomedical signals acquisition. The R-AFE efficiently reuses a reconfigurable preamplifier, a current generator (CG), and a mixed signal processing unit, having an area of 1.1 mm2 per R-AFE while supporting five acquisition modes to record different forms of cardiovascular and respiratory signals. The R-AFE can interface with voltage-, current-, impedance-, and light-sensors and hence can measure electrocardiography (ECG), bio-impedance (BioZ), photoplethysmogram (PPG), galvanic skin response (GSR), and general-purpose analog signals. Thanks to the chopper preamplifier and the low-noise CG utilizing dynamic element matching, the R-AFE mitigates 1/f noise from both the preamplifier and the CG for improved measurement sensitivity. The IC achieves competitive performance compared to the state-of-the-art dedicated readout ICs of ECG, BioZ, GSR, and PPG, but with approximately 1.4×-5.3× smaller chip area per channel.

Due to the large number of individuals with Unilateral Hearing Loss (UHL) and the recommendation to use hearing assistive devices, studies are required to define possibilities of intervention for this population. Objective: To evaluate the performance of the Remote Microphone System (RMS) in children with UHL. Methodology: Prospective clinical study with a convenience sample. Eleven children (mean age of 9.2 years) with severe and profound sensorineural UHL, hearing aid users and enrolled in regular schools participated in the study. They were evaluated using the Hearing in Noise Test (HINT), the Classroom Participation Questionnaire (CPQ), and the Sustained Auditory Attention Ability Test (SAAAT) with RMS. Results: HINT results were analyzed using variance to three criteria of repeated measures, which revealed differences between intervention, position, and time factors and significant interaction between these three factors. The comparative analysis of the results from CPQ showed significant differences in the statistical t-test (p=

Heart-sound auscultation is a rapid and fundamental technique used for examining the cardiovascular system. The main components of heart sounds are the first and second heart sounds. Discriminating these heart sounds under the presence of additional heart sounds and murmurs will be difficult. To recognize these signals efficiently, this study proposes a monitoring system with phonocardiogram and electrocardiogram. This system has two key points. The first is chip implementation, including capacitor coupled amplifier, transimpedance amplifier, high-pass sigma–delta modulator, and digital signal processing block. The chip in the system is fabricated in 0.18 μm standard complementary metal–oxide–semiconductor process. The second is a software application on smartphones for heart-related physiological signal recording, display, and identification. A wavelet-based QRS complex detection algorithm verified by MIT/BIH Arrhythmia Database is also proposed. The overall measured positive prediction, sensitivity, and error rate of the proposed algorithm are 99.90%, 99.82%, and 0.28%, respectively. During auscultation, doctors may refer to these physiological signals displayed on the smartphone and simultaneously listen to the heart sounds to diagnose the potential heart disease. By taking advantage of signal visualization and keeping the original diagnosis procedure, the uncertainty existing in heart sounds can be eliminated, and the training period to acquire auscultation skills can be reduced.

This paper presents a novel two-wired active electrode that achieves ultrahigh input impedance using power supply bootstrapping. The proposed circuit reduces the input capacitance of a buffer amplifier while enabling measurements using leads with only two wires, providing a low-complexity and low-cost solution for interference rejection and artifact reduction in dc-coupled dry-contact biopotential measurements. An implemented prototype shows that, even using standard operational amplifiers, an input capacitance as low as 71 fF can be obtained, maintaining a high impedance in a 0-1 kHz bandwidth, sufficient for ECG, EEG, and EMG measurements. The circuit has a simple and easily replicable implementation that requires no individual adjustment. A common mode rejection ratio (CMRR) above 103 dB at 50 Hz was achieved and the increased rejection to interference due to the potential divider effect was experimentally tested maintaining a 92-dB CMRR at 50 Hz with a 1.2-M Ω source impedance unbalance. ECG measurements were conducted to validate the active electrode against a traditional alternative, and a test with dry-contact EEG electrodes was successfully conducted. Although the proposed circuit is intended to acquire superficial electrophysiological signals using dry electrodes, it can be used for measurement from other high-impedance sources, such as micropipette electrodes. Fil: Guerrero, Federico Nicolas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina Fil: Spinelli, Enrique Mario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina

OBJECTIVE: This research assessed the reduction of peak levels, equivalent energy and sound power of firearm suppressors. DESIGN: The first study evaluated the effect of firearm suppressors at several positions around the firearm for four firearms. The second study assessed the reduction of sound power with a 3-meter hemispherical array of microphones for two firearms. RESULTS: The suppressors reduced exposures at the ear between 17- and 24-dB peak sound pressure level and reduced the 8-hour equivalent A-weighted energy between 9 and 21 dB depending upon the firearm and ammunition. Noise reductions observed for the instructor’s position about a meter behind the shooter were between 20- and 28-dB peak sound pressure and between 11 and 26 dB L(Aeq,8h). Firearm suppressors reduced the measured sound power levels between 2 and 23 dB. Sound power reductions were greater for the low-velocity ammunition than for the same weapons fired with high-velocity ammunition due to the effect of N-waves produced by a supersonic projectile. CONCLUSIONS: Whereas firearms suppressors are an effective engineering control to reduce firearm noise exposures, firearm users should always wear hearing protection whenever using weapons. The cumulative exposures of suppressed firearms can still present a significant hearing risk.

The development of giant magnetoresistive (GMR) sensors has demonstrated significant advantages in nanomedicine, particularly for ultrasensitive point-of-care diagnostics. To this end, the detection system is required to be compact, portable, and low power consuming at the same time that a maximum signal to noise ratio is maintained. This paper reports a CMOS front-end with integrated magnetoresistive sensors for biomolecular recognition detection applications. Based on the characterization of the GMR sensor's signal and noise, CMOS building blocks (i.e., current source, multiplexers, and preamplifier) were designed targeting a negligible noise when compared with the GMR sensor's noise and a low power consumption. The CMOS front-end was fabricated using AMS $\mathrm{{\text{0.35}}\;\mu{\text{m}}}$ technology and the magnetoresistive sensors were post-fabricated on top of the CMOS chip with high yield ( ${\text{97.9}}\%$ ). Due to its low circuit noise (16 $\mathrm{{\text{nV}}/\sqrt{{\text{Hz}}}}$ ) and overall equivalent magnetic noise ( $\mathrm{{\text{11.5}}\;{\text{nT}}/\sqrt{{\text{Hz}}}}$ ), the full system was able to detect 250 nm magnetic nanoparticles with a circuit imposed signal-to-noise ratio degradation of only $-$ 1.4 dB. Furthermore, the low power consumption (6.5 mW) and small dimensions ( $\mathrm{{\text{7.59: mm}}^{2}}$ ) of the presented solution guarantees the portability of the detection system allowing its usage at the point-of-care.

Objective: Conventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI. Methods: The BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen. The function of the amplifier was first validated against a commercial bioamplifier, followed by a head-to-head comparison between the custom BCI (using four EEG channels) and a conventional 32-channel BCI. Specifically, five able-bodied subjects were cued to alternate between hand opening/closing and remaining motionless while the BCI decoded their movement state in real time and provided visual feedback through a light emitting diode. Subjects repeated the above task for a total of 10 trials, and were unaware of which system was being used. The performance in each trial was defined as the temporal correlation between the cues and the decoded states. Results: The EEG data simultaneously acquired with the custom and commercial amplifiers were visually similar and highly correlated ( $\rho$ = 0.79). The decoding performances of the custom and conventional BCIs averaged across trials and subjects were 0.70 $\pm$ 0.12 and 0.68 $\pm$ 0.10, respectively, and were not significantly different. Conclusion: The performance of our portable, low-cost BCI is comparable to that of the conventional BCIs. Significance: Platforms, such as the one developed here, are suitable for BCI applications outside of a laboratory.

The active headrest is an important application of active noise control (ANC) where the occupant gets a quiet zone during use. However, its performance degrades due to head movement, which is an important issue. Unlike conventional active headrest algorithm, in the present paper, the two error microphones are attached to a head attachment such as a hat or band so that both error microphones can move along with the head, keeping the relative distance of ears and the microphones intact. The two secondary loudspeakers are kept near to the head, facing the ears. The performance of the active headrest was studied through real-time experimentation considering head movement. The innovation in this paper is that the ANC algorithm uses signals of moving error microphones to tune the controller without re-estimating the secondary path transfer functions. Since, the phase error of secondary path estimates at different head positions remains within ±90°, the secondary paths estimated once, when the head is positioned at the center, can be used for other head positions. This is established through real-time experiments using dSpace 1104 ACE system (dSpace, Germany).

Purpose To implement and validate low-loss ratio-adjustable power splitters (RAPS) for array-compressed parallel transmission (acpTx). Methods In acpTx, a small number of transmit channels drive a larger number of transmit coils, which are connected via an array compression network that implements optimized coil-to-channel combinations. Previous networks comprised a bank of power splitters, followed by attenuators to implement the amplitudes of the compression weights for each coil, but this resulted in high power dissipation in the network. Recognizing that an acpTx network need only implement relative attenuations between outputs, a RAPS circuit was developed which combines power splitting and relative attenuation, and has low insertion loss. RAPS circuits were experimentally validated and used to build an array compression network for a one-channel-to-four-coil spiral acpTx excitation experiment. Results Bench tests showed that the RAPS circuits came within 0.05 dB of the desired output ratios, and power dissipation was approximately 0.5 dB (10%). The spiral excitation experiment showed that the ability to optimally drive four coils with a single channel reduced excitation error by 46% compared to driving one coil, without using attenuators in the array compression network. Conclusion RAPS circuits enable the construction of low-loss array compression networks for parallel transmission. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Having intravascular ultrasound (IVUS) imaging capability on guide wires used in cardiovascular interventions may eliminate the need for separate IVUS catheters and expand the use of IVUS in a larger portion of the vasculature. High frequency capacitive micro machined ultrasonic transducer (CMUT) arrays should be integrated with interface electronics and placed on the guide wire for this purpose. Besides small size, this system-on-a-chip (SoC) front-end should connect to the back-end imaging system with a minimum number of wires to preserve the critical mechanical properties of the guide wire. We present a 40 MHz CMUT array interface SoC, which will eventually use only two wires for power delivery and transmits image data using a combination of analog-to-time conversion (ATC) and an impulse radio ultra-wideband (IR-UWB) wireless link. The proof-of-concept prototype ASIC consumes only 52.8 mW and occupies 4.07 $\text{mm}^{2}$ in a 0.35- $\mu\text{m}$ standard CMOS process. A rectifier and regulator power the rest of the SoC at 3.3 V from a 10 MHz power carrier that is supplied through a 2.4 m micro-coax cable with an overall efficiency of 49.1%. Echo signals from an 8-element CMUT array are amplified by a transimpedance amplifier (TIA) array and down-converted to baseband by quadrature sampling using a 40 MHz clock, derived from the power carrier. The ATC generates pulse-width-modulated (PWM) samples at 2 $\times$ 10 MS/s with 6 bit resolution, while the entire system achieved 5.1 ENOB. Preliminary images from the prototype system are presented, and alternative data transmission and possible future directions towards practical implementation are discussed.

Fully digitized 2D ultrasound transducer arrays require one ADC per channel with a beamforming architecture consuming low power. We give design considerations for per-channel digitization and beamforming, and present the design and measurements of a continuous time delta-sigma modulator (CTDSM) for cardiac ultrasound applications. By integrating a mixer into the modulator frontend, the phase and frequency of the input signal can be shifted, thereby enabling both improved conversion efficiency and narrowband beamforming. To minimize the power consumption, we propose an optimization methodology using a simulated annealing framework combined with a C++ simulator solving linear electrical networks. The 3rd order single-bit feedback type modulator, implemented in a 65 nm CMOS process, achieves an SNR/SNDR of 67.8/67.4 dB across 1 MHz bandwidth consuming 131 [Formula: see text] of power. The achieved figure of merit of 34.2 fJ/step is comparable with state-of-the-art feedforward type multi-bit designs. We further demonstrate the influence to the dynamic range when performing dynamic receive beamforming on recorded delta-sigma modulated bit-stream sequences.

To design a test battery and conduct a proof-of-concept experiment of a test method that can be used to measure the detection performance afforded by military advanced hearing protection devices (HPDs) and tactical communication and protective systems (TCAPS).The detection test was conducted with each of the four loudspeakers located at front, right, rear and left of the participant. Participants wore 2 in-ear-type TCAPS, 1 earmuff-type TCAPS, a passive Combat Arms Earplug in its "open" or pass-through setting and an EB-15LE™ electronic earplug. Devices with electronic gain systems were tested under two gain settings: "unity" and "max". Testing without any device (open ear) was conducted as a control.Ten participants with audiometric requirements of 25 dBHL or better at 500, 1000, 2000, 4000, 8000 Hz in both ears.Detection task performance varied with different signals and speaker locations. The test identified performance differences among certain TCAPS and protectors, and the open ear.A computer-controlled detection subtest of the Detection-Recognition/Identification-Localisation-Communication (DRILCOM) test battery was designed and implemented. Tested in a proof-of-concept experiment, it showed statistically-significant sensitivity to device differences in detection effects with the small sample of participants (10). This result has important implications for selection and deployment of TCAPS and HPDs on soldiers and workers in dynamic situations.

Hypothesis An implantable acousto-mechanical transducer will yield a higher output if its mass can be virtually increased through the use of a secondary actuator. Background Current hearing aids and implants rely on feedback compensation to prevent instability (e.g. howling), usually in the form of a digital or analogue filter. We examine the effect of mechanically stabilizing a piezo-driven mechanical amplifier inserted into the incudostapedial joint gap. The aim of this study is to determine whether this is possible and discern the advantages and disadvantages of the design. Methods We examine a 10:1 scale model of a prospective implantable hearing aid comprising one piezoelectric sensor and two independent piezoelectric actuators in a single-titanium housing. As expected, the maximum gain of the device is limited by feedback between sensor input and the output of the primary actuator. The secondary actuator is used to provide a counter force to the recoil of the primary output piezo. This adds a virtual mass to the device, effectively reducing feedback in the mechano-acoustic path. The compensation unit (CU) described here is driven by a real-time adaptive control algorithm. Results Using the above approach, we observe an added stable gain of >30 dB, and report a functional hearing gain of up to 40 dB. Physical and digital feedback compensation can be employed in parallel for best results. The experimental data is backed by computer simulations. Conclusion These results compare favorably with previous studies of a two-piezo transducer with digital feedback control and show the potential for the transducer as a hearing aid for high-frequency hearing loss.