Multichannel neural signal acquisition and processing play a pivotal role in advancing neuroscience research. This article proposes a 112-channel system-on-chip (SoC) design for neural signal acquisition and processing, comprising full-channel read-out circuits, neural signal-processing accelerators, and a 32-bit RISC-V core. A clock-domain-crossing (CDC) structure is devised to minimize data storage overhead in read-out circuits, facilitating comprehensive data acquisition and high-throughput simultaneous transmission from all channels. The channel-specific processing unit incorporates hardware-efficient designs for lossless compression, spike detection, and extraction of spike features. A multistage predictor module serves the dual purpose of narrowing data distribution during compression and signal augmentation during spike detection. The proposed design was fabricated in 40-nm technology with an area of 6.67 mm2. The acquisition of 112 channels achieves a peak data rate of 57.3 Mbps, with a total power consumption of 3.07 mW, wherein 0.87 mW is attributed to the read-out circuits. The processing accelerators feature an area consumption of 0.011 mm2/ch, and a minimal power consumption of only$0.2~{\mu }$W/ch under a 32-kHz clock. The effectiveness of the proposed design is validated through in vivo recording experiments conducted on rats by integrating with flexible implantable electrodes.

This paper proposed a miniature neural interface system. A single chip neural recording SoC was fabricated in 40nm CMOS process with an area of 3mm×3mm. It integrated a 16-channel analog front end (AFE), and a low power constant envelope polar transmitter. The general form of continuous phase modulation was used as the modulation scheme. Algorithms for receiver including frequency offset calibration, frame synchronization, and symbol demodulation were proposed and implemented on a software-defined radio platform. Simulation results showed that a bit error rate of $10^{-4}$ is achieved at the signal to noise ratio of 19 dB at high data rate mode of 971.4 kbps. A graphic user interface was designed for channel decoding and real-time display. Experimental results showed that the input referred noise of the AFE is 2.87$\mu V_{rms}$, and the energy efficiency of the transmitter is 2. 8nJ/bit. The proposed chip consumes 5. 47mW power in total in its maximum workload. The neural signal can be correctly decoded at least at a RSSI (Received Signal Strength Indicator) of -95dBm, and a working distance of 8 m. In-vivo tests on rat have been conducted, showing a good usability of the proposed system.

This paper proposes SaleNet - an end-to-end convolutional neural network (CNN) for sustained attention level evaluation using prefrontal electroencephalogram (EEG). A bias-driven pruning method is proposed together with group convolution, global average pooling (GAP), near-zero pruning, weight clustering and quantization for the model compression, achieving a total compression ratio of 183. 11x. The compressed SaleNet obtains a state-of-the-art subject-independent sustained attention level classification accuracy of 84.2% on the recorded 6-subject EEG database in this work. The SaleNet is implemented on a Artix-7 FPGA with a competitive power consumption of 0.11 W and an energy-efficiency of 8.19 GOps/w.

This paper proposed a fully integrated MBAN (2360–2400 MHz) continuous phase modulated transmitter (TX) with tunable less than 0dBm output power for medical band. A duty-cycle tuning strategy was proposed for the power amplifier (PA) featuring adaptive optimized efficiency for different output powers. A fully on-chip transformer-based match network was proposed to suppress the 2nd harmonic using a series$LC$resonator and to suppress the 3rd harmonic by introducing a transformer inter-winding capacitor feedback path. A fractional-N all-digital phase locked loop (ADPLL) with a transformer-based digitally controlled oscillator (DCO) is employed to reduce power consumption as well as improve modulation quality. The transmitter was fabricated in 40-nm CMOS technology, occupying an active area of 0.48mm2. Experimental results show a 26% drain efficiency with −10dBm PA output and 4dB tunable range. A 2mW total power consumption was measured with a TX efficiency of 5% and an energy efficiency of 2nJ/bit. The measured 2nd and 3rd harmonic distortion of the output were −44.3dBm and −57.2dBm, respectively, with on-chip matching network. The measured FSK error of CPM was 2.3% with an M of 2 and 1.57% with an M of 4.

We propose a complete hardware-based architecture of multilayer neural networks (MNNs), including electronic synapses, neurons, and periphery circuitry to implement supervised learning (SL) algorithm of extended remote supervised method (ReSuMe). In this system, complementary (a pair of n- and p-type) memtransistors (C-MTs) are used as an electrical synapse. By applying the learning rule of spike-timing-dependent plasticity (STDP) to the memtransistor connecting presynaptic neuron to the output one whereas the contrary anti-STDP rule to the other memtransistor connecting presynaptic neuron to the teacher one, extended ReSuMe with multiple layers is realized without the usage of those complicated supervising modules in previous approaches. In this way, both the C-MT-based chip area and power consumption of the learning circuit for weight updating operation are drastically decreased comparing with the conventional single memtransistor (S-MT)-based designs. Two typical benchmarks, the linearly nonseparable benchmark XOR problem and Mixed National Institute of Standards and Technology database (MNIST) recognition have been successfully tackled using the proposed MNN system while impact of the nonideal factors of realistic devices has been evaluated.

This paper proposed an epilepsy seizure detection ASIC design using only one channel EEG signal as input. The proposed design consists of 10 sub-bands FIR filters and band energy feature extraction engines. A MAC is utilized as a linear SVM classifier. The design was fabricated in TSMC 180nm technology with a power consumption of 3.09 μJ/Classification. According to experimental results, the average sensitivity is reduced by less than 10% while the area is reduced by 70%. With patient-specific configurable parameters, a higher than 90% dection sensitivity was achieved on half of the patients in the testing dataset.

ARShop is a one-stop solution for shopping in the cyber-physical world with the help of crowd knowledge and augmented reality. Its ultimate goal is to improve customers' shopping experience. When a customer enters a physical shop and snaps a shot, the enriched cyber information of the surroundings will pop up and be augmented on the screen. ARShop can also be the customer's personal shopping assistant who can show routes to the shops that the customer is interested in. In addition, ARShop provides merchants with a web-based interface to manage their shops and promote their business to customers, and provides customers with an Android App to query using images.

We consider using the conjugate gradient (CG) algorithm to equalize a time-varying channel in an orthogonal frequency division multiplexing (OFDM) system. Preconditioning technique to accelerate the convergence of the CG algorithm is discussed, where we show that when the Doppler spread becomes higher, the commonly used diagonal preconditioner, despite its simpleness, can perform even worse than without preconditioner. In such a case, a preconditioner with a more complex structure is proposed.

In this paper, we propose a clock skew calibration method for ranging applications using an ultra-wideband (UWB) signal. The clock skew is one of the main error sources in time-of-arrival (TOA) based UWB ranging, since a long ranging signal is required to obtain a sufficiently high signal-to-noise ratio (SNR). Therefore, the clock skew calibration is essential for accurate TOA ranging. We propose to estimate the clock skew in the frequency domain to take full advantage of the periodic property of the ranging signal, which allows the proposed method to reach super-resolution. Simulation results corroborate the efficiency of the proposed method.

OFDM suffers from inter-carrier interferences in the presence of the time variation. This paper seeks to quantify the amount of interferences resulting from wideband channels which assumed to follow the multi-scale/multi-lag (MSML) model. Due to the fact that the mobility in wideband channels induces scale effects, Doppler is revealed in a manner distinct from the frequency shifts experienced in narrowband systems. The MSML channel model results in full channel matrices both in the frequency and time domains. However, banded approximations are still possible, leading to significant reduction in the equalization complexity. Herein, measures for determining whether time-domain or frequency-domain should be undertaken are provided based on the amount of the resulting interference.

Mobile OFDM refers to OFDM systems with fast moving transceivers, contrastive to traditional OFDM systems whose transceivers are stationary or have a low velocity. In this paper, we use Basis Expansion Models (BEM) to model the time-variation of channels, based on which two least-squares (LS) channel estimators are presented. The first channel estimator allows for a general BEM assumption, and thus is called as the general implementation, whereas the second one is particularly tailored for a specific Critically-sampled Complex Exponential (CCE) BEM assumption, leading to a simplified architecture. The experimental results show that the simplified estimator is an appealing alternative, which achieves roughly a reduction of 59% for the ASIC core area, 89% for the ROM size and 53% of the processing latency at the cost of a slight estimation accuracy penalty compared to the general implementation method.

The effect of spectrum sensing errors on the performance of cognitive radio transmission based on orthogonal frequency division multiplexing is evaluated by deriving analytical expressions for the average capacity and the average bit error rate as functions of different spectrum sensing parameters and data transmission parameters in the Rayleigh fading channels. Both the case with carrier frequency offset and the case without carrier frequency offset are considered. Numerical results are presented to show that spectrum sensing errors cause significant performance degradation and that the amount of performance degradation depends on the specific values of sensing and transmission parameters. In particular, the performance degradation caused by the primary user interference is more sensitive to the interfering amplitude and the number of subcarriers than to the probability of detection in spectrum sensing, the interfering frequency and the availability of the licensed band. Numerical results also show that the primary user interference caused by the sensing errors is dominant for small to medium values of the operating signal-to-noise ratio, while the inter-carrier interference caused by carrier frequency offset is dominant for large values of the operating signal-to-noise ratio.

In this paper, we present a single-carrier transceiver for rapidly time-varying channels, where the equalization step is implemented in the frequency domain. When the channel abides with both fast fading and severe inter-block interference, our equalizer relies on a band approximation of the frequencydomain channel matrix to maintain low complexity. We will show that the band approximation error can be associated in the time domain to a critically-sampled complex exponential basis expansion modeling error. Based on this property, we propose a novel receiver architecture that extends the original data model by inserting zeros at the receiver. The resulting effective channel can be characterized by an oversampled complex exponential basis expansion model, which has a considerably reduced modeling error compared to the critically-sampled one. In other words, the band assumption that is essential to the equalizer will be made more accurate and thus the equalization performance can be improved.

We consider a single-carrier transceiver, which abides with both fast channel fading and severe inter-block interference. To enable a low-complexity frequency-domain equalizer, it is desired that 1) the channel matrix be approximately banded; and 2) the inter-block interference be reduced. In this paper, we propose an extended data model, which incorporates a receiver window to enforce these two conditions.

Pilot-assisted channel estimation is considered in this letter, where the channel is assumed to be time-selective and can be accurately fit by a basis expansion model. The position and power of the pilots are crucial to the mean square error of the channel estimator. In this paper, we present nonlinear integer programming algorithms to optimize the position and power of the pilots. In comparison with the traditional equi-distant/powered pilot structure, the solution obtained from the proposed algorithms yields a better performance.

In this paper, we deal with channel estimation for Orthogonal Frequency-Division Multiplexing (OFDM) systems. The channels are assumed to be Time-Varying (TV) and approximated by a Basis Expansion Model (BEM). Due to the time-variation, the resulting channel matrix in the frequency domain is no longer diagonal, but can be approximated as banded. Based on this band approximation, we propose novel channel estimators to combat both the noise and the out-of-band interference. Our claims are supported by simulation results, which are obtained based on realistic TV channels with a fairly high Doppler spread.