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Loudspeaker arrays with high directivity are desirable in many acoustic and sound applications to direct sounds into a desired region. One way of designing such arrays is through the differential operator to maximize the directivity factor. However, this method generally works for linear arrays with endfire steering direction and its usage to generate a broadside radiation pattern is restricted to the second-order with three loudspeakers. This paper presents a general approach to the design of differential linear loudspeaker arrays with broadside radiation patterns of any-order. Three methods are presented to find the beamforming filter with design examples provided.

A capacitor transimpedance amplifier (CTIA) for wireless-powered wearable electrochemical sweat sensors is designed and tested. Correlative double sampling (CDS) technology is utilized to suppress offset voltage and noise. Dedicated low-power and low-voltage designs meet the requirements of wireless-powered wearable applications where the power supply is limited, and voltage is unstable. The proposed CDS-CTIA is fabricated in 0.18-µm complementary metal oxide semiconductor (CMOS) process, occupying an active area of 0.0285mm2. The measured low-frequency gain is 33.2MΩ/150.4dBΩ under a 1.8V supply voltage, with a total power consumption of 17.694µW (including 14.882µW static power and 2.812µW dynamic power). The input current ranges from -24nA to +19nA, and the input referred noise current is 7.76pArms in the 0.1-100 Hz frequency band. The proposed CDS-CTIA is capable of operating under a wide power supply ranging from 0.9V to 2.0V. In addition, its practicality is verified by measuring glucose concentration with an enzyme electrode.

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

This paper demonstrates hybrid sub-aperture beamforming (SAB) with time-division multiplexing (TDM) for massive interconnect reduction in ultrasound imaging systems. A single-chip front-end system prototype has been fabricated in 180-nm HV BCD technology that combines 5×1 SAB with 8×1 TDM to efficiently reduce the number of receive signal interconnects by a factor of 40. The system includes on-chip high-voltage (HV) pulsers capable of generating unipolar pulses up to 70 V in transmit (TX) mode. The receiver (RX) chain consists of a T/R switch, a variable-gain low-noise amplifier (VG-LNA) with 4-step gain control (15-32 dB) for time-gain compensation followed by a programmable switched-capacitor analog delay-and-sum beamformer. The proof-of-concept prototype operates at a 200-MHz clock frequency and the SAB provides 32-step fine delays with a maximum delay of 310 ns corresponding to better than λ/20 delay quantization at 5 MHz. With these specifications, the SAB is capable of beam steering from 0

Temporal interference stimulation has been suggested as a method to reach deep targets during transcutaneous electrical stimulation. Despite its growing use in transcutaneous stimulation therapies, the mechanism of its operation is not fully understood. Recent efforts to fill that gap have focused on computational modelling, in vitro and in vivo experiments relying on physical observations - e.g., sensation or movement. This paper expands the current range of experimental methods by demonstrating in vivo extraneural recordings from the ulnar nerve of a pig while applying temporal interference stimulation at a location targeting a distal part of the nerve. The main aim of the experiment was to compare neural activation using sinusoidal stimulation (100 Hz, 2 kHz, 4 kHz) and temporal interference stimulation (2 kHz and 4 kHz). The recordings showed a significant increase in the magnitude of stimulation artefacts at higher frequencies. While those artefacts could be removed and provided an indication of the depth of modulation, they resulted in the saturation of the amplifiers, limiting the stimulation currents and amplifier gains used. The results of the 100 Hz sine wave stimulation showed clear neural activity correlated to the stimulation waveform. However, this was not observed with temporal interference stimulation. The results suggest that, despite its greater penetration, higher currents might be required to observe a neural response with temporal interference stimulation, and more complex artefact rejection techniques may be required to validate the method.

The coil structure of the inductive wear debris sensor plays a significant role in the effect of wear debris detection. According to the characteristics of LC parallel resonance, the capacitor and coil are connected in parallel to make sensor coils in the LC parallel resonance state, which is beneficial to improve the ability to detect wear particles. In this paper, the mathematical model of output-induced electromotance of the detection coil is established to analyze the influence of the structure on the detection sensitivity and enhance the sensor’s current rate of change to the disturbance magnetic field, which is essential to resist noise interference. Based on the coherent demodulation principle, the AD630 lock-in amplifier is applied to the test platform to amplify and identify weak signals. In addition, experiments are designed to test the output signals of debris under the condition of different original output voltages of the sensor with a parallel structure. Meanwhile, the near-resonance state of the detection coil with LC parallel circuit is tested by output signal information. Results show that the sensor detection sensitivity will be effectively improved with the LC parallel coil structure. For the sensor structure parameters designed in this paper, the optimal raw output amplification voltage for abrasive particle detection is 4.49 V. The detection performance of ferromagnetic particles and non-ferromagnetic particles is tested under this condition, realizing the detection ability of 103.33 μm ferromagnetic abrasive particles and 320.74 μm non-ferromagnetic abrasive particles.

This work presents a third-order high-pass sigma-delta modulator (HPSDM) for biomedical signal acquisition. The operational amplifier (op-amp) sharing and noise-coupling techniques are adopted to reduce the required quantity of op-amps and add a noise-shaping order, which can achieve low power consumption and high resolution. A novel switched-capacitor architecture is proposed to suppress the increasing in-band noise and alleviate the circuit sensitivity to capacitor mismatch in the high-pass integrator. The proposed HPSDM was fabricated in a 0.18-μm standard CMOS process. Measurement results reveal that the proposed HPSDM has a signal-to-noise and distortion ratio (SNDR) of 75.26/74 dB in 200 Hz bandwidth and consumes 1.52/0.8 μW under 1.2/1 V supply voltage, which can achieve a peak Schreier Figure-of-Merit of 156.45/157.98 dB and a peak Walden FoM of 0.802/0.488 pJ/conv.

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.

The purpose of the current study was to implement unequal microstrip power splitters for parallel transmission at 7 T that are optimized for size and loss and that can be configured for a wide range of power ratios. The splitters will enable the use of more transmit coils without a corresponding increase in the number of transmit channels or amplifiers to control specific absorption rate, shorten RF pulses, and shim inhomogeneous RF fields. Wilkinson unequal power splitters based on a novel microstrip network design were optimized to minimize their size under 8 cm in length and 9 cm in width, enabling them to be included in coil housing or cascaded in multiple stages. Splitters were designed and constructed for a wide range of output power ratios at 298 MHz. Simulations and bench tests were performed for each ratio, and a methodology was established to adapt the designs to other ratios and frequencies. The designs and code are open source and can be reproduced as is or reconfigured. The single-stage designs achieved good matches and isolations between output ports (worst isolation -15.9 dB, worst match -15.1 dB). A two-stage cascaded (one input to four outputs) power splitter with 1:2.5, 1:10, 1:3, and 1:6 ratio outputs was constructed. The worst isolation between output ports was -19.7 dB in simulation and the worst match of the three ports was -17.8 dB. The measured ratios for one- and two-stage boards were within 10% of the theoretical ratios. The power-handling capability of the smallest trace was approximately 70 W. Power loss for the one- and two-stage boards ranged from 1% to 3% in simulation compared with 5.1% to 7.2% on the bench. It was concluded that Wilkinson unequal microstrip power splitters can be implemented with a small board size (low height) and low loss, and across a wide range of output power ratios. The splitters can be cascaded in multiple stages while maintaining the expected ratios and low loss. This will enable the construction of large fixed transmit array-compression matrices with low loss.

This paper presents a PVT-robust error-feedback (EF) noise-shaping SAR (NS-SAR) ADC for direct neural-signal recording. For closed-loop bidirectional neural interfaces enabling the next generation neurological devices, a wide-dynamic-range neural recording circuit is required to accommodate stimulation artifacts. A recording structure using an NS-SAR ADC can be a good candidate because the high resolution and wide dynamic range can be obtained with a low oversampling ratio and power consumption. However, NS-SAR ADCs require an additional gain stage to obtain a well-shaped noise transfer function (NTF), and a dynamic amplifier is often used as the gain stage to minimize power overhead at the cost of vulnerability to PVT variations. To overcome this limitation, the proposed work reutilizes the capacitive-feedback amplifier, which is the analog front-end of the neural recording circuit, as a PVT-robust gain stage to achieve a reliable NS performance. In addition, a new chopper-based implementation of a passive high-pass IIR filter is proposed, achieving an improved NTF compared to prior EF NS-SAR ADCs. Fabricated in a 180-nm CMOS process, the proposed NS-SAR ADC consumes 4.3-μW power and achieves a signal-to-noise-and-distortion ratio (SNDR) of 71.7 dB and 82.7 dB for a bandwidth of 5 kHz and 300 Hz, resulting in a Schreier figure of merit (FOM) of 162.4 dB and 162.1 dB, respectively. Direct neural recording using the proposed NS-SAR ADC is demonstrated successfully in vivo, and also its tolerance against stimulation artifacts is validated in vitro.

The MRI hybrid ablation system is an approach to use the MR (magnetic resonance) scanner's radiofrequency amplifier itself as power source for ablation. Hereby, an electrode is connected to the MR internal radiofrequency amplifier. An average RF power is provided through a train of short RF pulses, which is sufficient to thermally destroy tissue. However, ablation with too high power values can cause tissue carbonizations, thus impeding the ablation procedure. Therefore, monitoring of the energy and the power absorbed inside the tissue is necessary. For this purpose, a measurement system was designed to measure the energy applied to the tissue when using the concept of an MRI hybrid ablation system. The system consists of a dual-directional coupler, RF-to-RMS sensors, and a microcontroller. The gradient calculation of the measured energy curve provides information about the absorbed RF power in the tissue. Validation measurements of the system were performed and compared with measurements from the MR-internal power measurement system. The energy monitoring system was also tested in an ablation experiment with ex-vivo animal tissue using the MRI hybrid ablation system. The measurements showed that the applied RF power can be monitored in real-time. It has been shown that the mean RF power absorbed in the patient decreased during an ablation procedure due to an occurring impedance mismatch and tissue changes. In further work, the influence of the monitoring system on the quality of the MR images should be investigated. Clinical relevance- This paper demonstrates an energy monitoring system for an RF ablation system, which can be used inside an MR environment.

Microscale needle-like electrode technologies offer

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.

A high-pass sigma-delta modulator (HPSDM) is proposed for electrocardiography (ECG) signal acquisition system. The HPSDM is implemented using operational amplifier (op-amp) sharing and programmable feedforward coefficients. The op-amp sharing is adopted to reduce the quantity of amplifiers because they dominate the power consumption of the HPSDM. In addition, given that the magnitude of the ECG is dependent on different persons, programmable feedforward coefficients are utilized to extend the dynamic range of the HPSDM to fit the actual application. The proposed HPSDM is fabricated in a 0.18-μm standard CMOS process. Measurement results reveal that the proposed HPSDM has a signal-to-noise and distortion ratio (SNDR) of 54.5 dB and a power consumption of 2.25 μW under a 1.2 V supply voltage and achieves a figure of merit (FoM) of 12.96 pJ/conv. Moreover, the proposed HPSDM has an SNDR of 64.8 dB and a power consumption of 5.2 μW under a 1.8 V supply voltage and achieves a FoM of 9.15 pJ/conv due to the op-amp sharing technique. Under the 1.2 V and 1.8 V supply voltages, the dynamic range of the HPSDM is extended to approximately 12 dB due to the technique of programmable feedforward coefficients.

The temporal shape of a pulse in transcranial magnetic stimulation (TMS) influences which neuron populations are activated preferentially as well as the strength and even direction of neuromodulation effects. Furthermore, various pulse shapes differ in their efficiency, coil heating, sensory perception, and clicking sound. However, the available TMS pulse shape repertoire is still very limited to a few biphasic, monophasic, and polyphasic pulses with sinusoidal or near-rectangular shapes. Monophasic pulses, though found to be more selective and stronger in neuromodulation, are generated inefficiently and therefore only available in simple low-frequency repetitive protocols. Despite a strong interest to exploit the temporal effects of TMS pulse shapes and pulse sequences, waveform control is relatively inflexible and only possible parametrically within certain limits. Previously proposed approaches for flexible pulse shape control, such as through power electronic inverters, have significant limitations: The semiconductor switches can fail under the immense electrical stress associated with free pulse shaping, and most conventional power inverter topologies are incapable of generating smooth electric fields or existing pulse shapes. Leveraging intensive preliminary work on modular power electronics, we present a modular pulse synthesizer (MPS) technology that can, for the first time, flexibly generate high-power TMS pulses (one-side peak ∼4000 V, ∼8000 A) with user-defined electric field shape as well as rapid sequences of pulses with high output quality. The circuit topology breaks the problem of simultaneous high power and switching speed into smaller, manageable portions, distributed across several identical modules. In consequence, the MPS TMS techology can use semiconductor devices with voltage and current ratings lower than the overall pulse voltage and distribute the overall switching of several hundred kilohertz among multiple transistors. MPS TMS can synthesize practically any pulse shape, including conventional ones, with fine quantization of the induced electric field (⩽17% granularity without modulation and ∼300 kHz bandwidth). Moreover, the technology allows optional symmetric differential coil driving so that the average electric potential of the coil, in contrast to conventional TMS devices, stays constant to prevent capacitive artifacts in sensitive recording amplifiers, such as electroencephalography. MPS TMS can enable the optimization of stimulation paradigms for more sophisticated probing of brain function as well as stronger and more selective neuromodulation, further expanding the parameter space available to users.

As the basic tools for neuroscience research, invasive neural recording devices can obtain high-resolution neuronal activity signals through electrodes connected to the subject’s brain. Existing wireless neural recording devices are large in size or need external large-scale equipment for wireless power supply, which limits their application. Here, we developed an ultra-low-noise, low power and miniaturized dual-channel wireless neural recording microsystem. With the full-differential front-end structure of the dual operational amplifiers (op-amps), the noise level and power consumption are notably reduced. The hierarchical microassembly technology, which integrates wafer-level packaged op-amps and the miniaturized Bluetooth module, dramatically reduces the size of the wireless neural recording microsystem. The microsystem shows a less than 100 nV/Hz ultra-low noise level, about 10 mW low power consumption, and 9 × 7 × 5 mm3 small size. The neural recording ability was then demonstrated in saline and a chronic rat model. Because of its miniaturization, it can be applied to freely behaving small animals, such as rats. Its features of ultra-low noise and high bandwidth are conducive to low-amplitude neural signal recording, which may help advance neuroscientific discovery.

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.

This article presents a direct digitizing neural recorder that uses a body-induced offset based DC servo loop to cancel electrode offset (EDO) on-chip. The bulk of the input pair is used to create an offset, counteracting the EDO. The architecture does not require AC coupling capacitors which enables the use of chopping without impedance boosting while maintaining a large input impedance of 238 M Ω over the whole 10 kHz bandwidth. Implemented in a 180 nm HV-CMOS process, the prototype occupies a silicon area of only 0.02 mm

In the development of implantable neural interfaces, the recording of signals from the peripheral nerves is a major challenge. Since the interference from outside the body, other biopotentials, and even random noise can be orders of magnitude larger than the neural signals, a filter network to attenuate the noise and interference is necessary. However, these networks may drastically affect the system performance, especially in recording systems with multiple electrode cuffs (MECs), where a higher number of electrodes leads to complicated circuits. This paper introduces formal analyses of the performance of two commonly used filter networks. To achieve a manageable set of design equations, the state equations of the complete system are simplified. The derived equations help the designer in the task of creating an interface network for specific applications. The noise, crosstalk and common-mode rejection ratio (CMRR) of the recording system are computed as a function of electrode impedance, filter component values and amplifier specifications. The effect of electrode mismatches as an inherent part of any multi-electrode system is also discussed, using measured data taken from a MEC implanted in a sheep. The accuracy of these analyses is then verified by simulations of the complete system. The results indicate good agreement between analytic equations and simulations. This work highlights the critical importance of understanding the effect of interface circuits on the performance of neural recording systems.

This paper presents the extremely low-voltage supply of the CMOS structure of a differential difference transconductance amplifier (DDTA). With a 0.3-volt supply voltage, the circuit offers rail-to-rail operational capability. The circuit is designed for low-frequency biomedical and sensor applications, and it consumes 357.4 nW of power. Based on two DDTAs and two grounded capacitors, a voltage-mode universal filter and quadrature oscillator are presented as applications. The universal filter possesses high-input impedance and electronic tuning ability of the natural frequency in the range of tens up to hundreds of Hz. The total harmonic distortion (THD) for the band-pass filter was 0.5% for 100 mVpp @ 84.47 Hz input voltage. The slight modification of the filter yields a quadrature oscillator. The condition and the frequency of oscillation are orthogonally controllable. The frequency of oscillation can also be controlled electronically. The THD for a 67 Hz oscillation frequency was around 1.2%. The circuit is designed and simulated in a Cadence environment using 130 nm CMOS technology from United Microelectronics Corporation (UMC). The simulation results confirm the performance of the designed circuits.

An 8-channel AFE with a group-chopping instrumentation amplifier (GCIA) is proposed for bio-potential recording applications. The group-chopping technique cascades chopper switches to progressively swap channels and dynamically removes gain mismatch among all channels. An 8-phase non-overlapping clocking scheme is developed and achieves excellent between-channel gain mismatch characteristics. The dynamic offsets among all channels are mitigated by the GCIA as well. The GCIA is the first work that minimizes the gain mismatch across more than two channels. With the help of the group-chopping, combined with an area-efficient open-loop structure, the GCIA shows0.04% between-channel gain mismatch, the lowest mismatch reported to date. The chip is fabricated in 0.18µm 1P6M CMOS, occupies only 0.017 mm

Proteins are useful biomarkers for a wide range of applications such as cancer detection, discovery of vaccines, and determining exposure to viruses and pathogens. Here, we present a low-noise front-end analog circuit interface towards development of a portable readout system for the label-free sensing of proteins using Nanowell array impedance sensing with a form factor of approximately 35cm2. The electronic interface consists of a low-noise lock-in amplifier enabling reliable detection of changes in impedance as low as 0.1% and thus detection of proteins down to the picoMolar level. The sensitivity of our system is comparable to that of a commercial bench-top impedance spectroscope when using the same sensors. The aim of this work is to demonstrate the potential of using impedance sensing as a portable, low-cost, and reliable method of detecting proteins, thus inching us closer to a Point-of-Care (POC) personalized health monitoring system. We have demonstrated the utility of our system to detect antibodies at various concentrations and protein (45 pM IL-6) in PBS, however, our system has the capability to be used for assaying various biomarkers including proteins, cytokines, virus molecules and antibodies in a portable setting.

Purpose In simultaneous transmission and reception (STAR) MRI, along with the coupling of the excitation pulse to the received signal, noise, and undesired distortions (spurs) coming from the transmit chain also leak into the acquired signal and degrade image quality. Here, properties of this coupled noise and its relationship with the transmit amplifier gain, transmit chain noise density, isolation performance, and imaging bandwidth are analyzed. It is demonstrated that by utilizing a recently proposed STAR technique, the transmit noise can be reduced. The importance of achieving high isolation and careful selection of the corresponding parameters are demonstrated. Theory and Methods A cancellation algorithm, together with a vector modulator, is used for transmit-receive isolation. The scanner is modeled as a pipeline of blocks to demonstrate the noise contribution from each block. With higher isolation, coupled transmit noise can be reduced to the point that the dominant noise source becomes acquisition noise, as in the case for pulsed MRI. Amplifiers with different gain and noise properties are used in the experiments to verify the derived noise-transmit parameter relation. Results With the proposed technique, more than 80 dB isolation in the analog domain is achieved. The leakage noise and the spurs coupled from the transmit chain, are reduced. It is shown that the transmit gain plays the most critical role in determining sufficient isolation, whereas the amplifier noise figure does not contribute as much. Conclusion The transmit noise and the spurs in STAR imaging are analyzed and mitigated by using a vector modulator.

This paper presents the noise optimization of a novel switched-capacitor (SC) based neural interface architecture, and its circuit demonstration in a 0.13 $\mu\text{m}$ CMOS process. To reduce thermal noise folding ratio, and suppress $kT/C$ noise, several noise optimization techniques are developed in the proposed architecture. First, one parasitic capacitance suppression scheme is developed to block noise charge transfer from parasitic capacitors to amplifier output. Second, one recording path-splitting scheme is proposed in the input sampling stage to selectively record local field potentials (LFPs), extracellular spikes, or both for reducing input noise floor, and total power consumption. Third, an auto-zero noise cancellation scheme is developed to suppress $kT/C$ noise in the neural amplifier stage. A prototype neural interface chip was fabricated, and also verified in both bench-top, and In-Vivo experiments. Bench-top testings show the input-referred noise of the designed chip is 4.8 $\mu\text{V}$ from 1 $\text{Hz}$ to 300 $\text{Hz}$ , and 2.3 $\mu\text{V}$ from 300 $\text{Hz}$ to 8 $kHz$ respectively, and In-Vivo experiments show the peak-to-peak amplitude of the total noise floor including neural activity, electrode interface noise, and the designed chip is only around 20 $\mu\text{V}$ . In comparison with conventional architectures through both circuit measurement and animal experiments, it is well demonstrated that the proposed noise optimization techniques can effectively reduce circuit noise floor, thus extending the application range of switched-capacitor circuits.

Purpose: Providing accurate gradient currents is challenging due to the gradient chain nonlinearities, arising from gradient power amplifiers and power supply stages. This work introduces a new characterization approach that takes the amplifier and power supply into account, resulting in a nonlinear model that compensates for the current droop. Methods: The gradient power amplifier and power supply stage were characterized by a modified state-space averaging technique. The resulting nonlinear model was inverted and used in feedforward to control the gradient coil current. A custom-built two-channel z-gradient coil was driven by high-switching (1 MHz), low-cost amplifiers (

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A sub-1GHz transmitter (TX) integrated chip (IC) with ultra-low power consumption and moderately high adjacent channel power rejection (ACPR) is presented for in-body bio-sensing applications. The 400 MHz 12-phase digital power amplifier (DPA) is implemented with the proposed 16QAM modulation scheme to improve the energy efficiency. The TX IC also contains a 900 MHz FSK TX realized with a symmetrical edge-combiner, which can be used in the low accuracy mode. A fully digital modulator with band shaping is integrated on the chip for the improvement of ACPR performance. Fabricated in 65-nm CMOS process, the chip occupies an active area of 0.75 mm

In this work, we present an 8-channel reconfigurable multimodal neural-recording IC, which provides improved availability and usability of recording channels in various experiment scenarios. Each recording channel changes its configuration depending on whether the channel is assigned to record voltage or current signal. As a result, although the total number of channels is fixed by design, the channels utilized for voltage and current recording can be set freely and optimally for given experiment targets, scenarios, and circumstances, maximizing the availability and usability of recording channels.The proposed concept was demonstrated by fabricating the IC using a standard 180-nm CMOS process.Using the IC, we successfully performed an in vivo experiment from the hippocampal area of a mouse brain. The measured input noise of the reconfigurable front-end is 4.75 μV

Technical details associated with a novel relative motion sensor system are elaborated in the paper. By utilizing the Doppler effect, the optical sensor system estimates the relative motion rates between the sensor and the moving object equipped with modulating light sources and relatively inexpensive electrical components. A transimpedance amplifier (TIA) sensing circuit is employed to measure the Doppler shift exhibited by the amplitude modulated light sources on the moving platform. Implementation details associated with the amplitude modulation and photo-detection processes are discussed using representative hardware elements. A heterodyne mixing process with a reference signal is shown to improve the signal-to-noise ratios of the Doppler shift estimation processing pipeline. Benchtop prototype experiments are used to demonstrate the utility of the proposed technology for relative motion estimation applications.

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.

This paper presents an envelope linearization technique to compensate for the nonlinear distortion of envelope tracking (ET) power amplifiers (PAs) for 5G new radio (NR) mobile terminals. The proposed envelope optimization (EOPT) method is agnostic of the nonlinear distortion generated in the envelope supply path and can compensate for the nonlinear distortion at the ET PA output without the need to monitor the output at the envelope tracking modulator (ETM). The linearization system in the envelope path is based on the envelope generalized memory polynomial (EGMP) behavioral model. Since the ETM output is not available, an iterative nonlinear least squares solution inspired in the deep deterministic policy gradient (DDPG) algorithm is proposed to extract the coefficients of the EGMP model. The EOPT method is validated on a system-on-chip (SoC) ET PA board designed for mobile terminal applications. Experimental results show the suitability of the proposed method to guarantee the linearity requirements (i.e., adjacent channel power ratio below -36 dBc) with 16.8% of power efficiency when operating the ET PA with 5G new radio test signals of 60 MHz bandwidth operating at 2.55 GHz (band 7). The linearization performance of the proposed EOPT method is comparable to the envelope leakage cancellation (ELC) approach (but saving the need for an analog to digital converter to monitor the ETM output), and can outperform a conventional I-Q digital predistorter based on the generalized memory polynomial (GMP) behavioral model. This research was funded by Huawei Technologies from July 2020 to August 2021; and supported in part by the project PID2020-113832RB-C21 funded by MCIN/AEI/10.13039/50110001103 and in part by the Government of Catalonia and the European Social Fund under Grant 2021-FI-B-137.