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This paper reports a non-invasive sub-Terahertz glucose concentration measurement system consisting of Sensor Unit (SU) and Processing Unit (PU). The SU. uses waveguide probe sensors to obtain the ${S}$ parameters of glucose samples of different concentrations. These ${S}$ parameters depend on the dielectric properties of glucose samples. The frequency-dependent permittivity values of various glucose samples are theoretically estimated using the double–Debye model for sensitivity and uncertainty investigations. The glucose sample concentration is used in the range 70–145 mg/dl to mimic healthy human bodies’ blood glucose levels, ranging from 70 to 140 mg/dl. The ${S}$ parameters obtained through SU are converted to the time domain to obtain real-valued impulse responses, which are normalized in PU, making the input data suitable for analysis using Levenberg–Marquardt (LM) algorithm-based Back Propagation Neural Network. The proposed SU. provides a sensitivity of 2 dB for 15 mg/dl change in glucose concentration, and PU exhibits an accuracy of ±5%, which falls within the clinical range specified for non-invasive based monitoring systems for diabetes. Overall, SU provides high sensitivity towards blood glucose measurement, and PU enhances the measurement system’s readability by forming a non–linear relationship between ${S}$ parameters and glucose concentration values [ABSTRACT FROM AUTHOR]

In this work, we demonstrate a compressed time-domain, pooling-aware convolution (COMPAC) convolutional neural network (CNN) engine for energy-efficient edge AI computing by performing multi-bit input and multi-bit weight multiply-and-accumulate (MAC) operations in the time domain. The multi-bit inputs are compactly represented as a single pulsewidth encoded input. This translates into reduced switching capacitance ($C_{\text {DYN}}$), compared with the baseline digital implementation, and can enable low-power neural network computing in an edge device. COMPAC CNN engine employs a novel and an improved version of the memory delay line (MDL) supporting the time residue scaling to perform the signed accumulation of multi-bit input and multi-bit weight products in the time domain. The compressed time-domain (CTD) approach is proposed to improve the throughput in time encoding of the input activations. The simulation results of the proposed CTD approach on the AlexNet CNN over 1000 ImageNet images show that 14.71 and 7.15 input clock cycles are consumed to time-encode an 8-bit input activation in two different CTD modes, improving the throughput by 88.60% and 94.46%, respectively, compared with the conventional pulsewidth modulation-based time-domain encoding. Furthermore, a pooling-aware convolution (PAC) technique is proposed to reduce the number of redundant MAC computations for the convolution layers that are followed by the max-pooling layer. The simulation results on the AlexNet CNN over 1000 ImageNet images show up to 31.47% (21.79%) reduction in the number of non-zero input activations MACs with a top-five classification accuracy loss of 0.60% (0.90%) with an on-chip access overhead of 60.53% (8.03%) for the PAC modes 1 (2) respectively. Finally, energy-efficient data flow for optimal on-/off-chip memory accesses for the time-domain MAC computation is proposed. COMPAC data flow the results in 86.97% reduced on-chip accesses and 29.74% reduced off-chip accesses compared with the Eyeriss approach, at iso-bit precision. COMPAC CNN engine implemented in 65-nm CMOS test chip demonstrates an energy efficiency of 1.044 TOPS/W and the throughput of 0.1278 GOPS at 720 mV for the AlexNet. The top-five classification accuracy of 76.90% measured over 1000 ImageNet images and 77.15% by simulating over 50 000 ImageNet images is achieved. The simulation results comprehending MDL circuit non-idealities for the AlexNet over 50 000 ImageNet validation set images show a classification accuracy loss within 1% compared with the 8-bit fixed-point software implementation. [ABSTRACT FROM AUTHOR]

Next-generation wireless communication requires phased-array systems with large modulated bandwidths and high energy efficiency, ensuring Gb/s data communication. Conventional phase-shifter-based arrays result in frequency-dependent processing and, therefore, beam-squinting in an array. This work demonstrates a four-element 500-MHz modulated bandwidth true-time-delay-based ADC-enabled spatial signal processor (SSP) with frequency-uniform beamforming, wideband beam-nulling, and multiple independent interference filterings using the Kronecker decomposition. This processor can augment conventional phased-array RF front ends to implement a complete antenna-to-digital solution. The proposed baseband delay-compensating solution in the SSP uses scalable time-domain circuits comprising of time-interleaved voltage-to-time converters followed by asynchronous 6-bit pipeline time-to-digital converters and consumes only 40 mW with a total area of 0.31 mm2 in 65-nm CMOS technology. [ABSTRACT FROM AUTHOR]

This study addresses the high‐resolution time delay estimation (TDE) via sparse parameter estimation methods. Two representative algorithms, Sparse Asymptotic Minimum Variance (SAMV) and SParse Iterative Covariance‐based Estimation are devised in both the time and frequency domains for application to the TDE of spread‐spectrum signals and their performances are analysed in various multipath environments. The authors also proposes the combined approach of SAMV and weighted RELAX, referred to as SAMV‐WRELAX, to reduce the computational load. Numerical examples demonstrate that the frequency‐domain approaches with a proper type of snapshots not only outperform the corresponding time‐domain approaches but also mitigate the problem of the noise correlation encountered in time‐domain processing. They also show that the computational load of SAMV‐WRELAX with a grid size of 0.1Tc decreases up to a few tenths of that of SAMV with a fine grid, e.g. a size of 0.01Tc, without any performance degradations. [ABSTRACT FROM AUTHOR]

In this article, we demonstrate an energy efficient convolutional neural network (CNN) engine by performing multiply-and-accumulate (MAC) operations in the time domain. The multi-bit inputs are compactly represented as a single pulse width encoded input. This translates into reduced switching capacitance ($C_{\mathrm{ DYN}}$), compared to baseline digital implementation, and can enable low power neural network computing in an edge device. The time-domain CNN engine employs a novel bi-directional memory delay line (MDL) unit to perform signed accumulation of input and weight products. The proposed MDL design leverages standard digital circuits and does not require any capacitors and complex analog-to-digital converters (ADCs) to realize the convolution operation, thereby enabling easy scaling across the process technology nodes. Four speed-up modes and a configurable MDL length are supported to address throughput versus accuracy trade-off of the time-domain computing approach. Delay calibration units have been accommodated to mitigate the process variation induced delay mismatch among concurrently operating MDL units. The proposed time-domain MDL design implements a LeNet-5 CNN engine in a commercial 40-nm CMOS process achieving an energy efficiency of 12.08 TOPS/W, a throughput of 0.365 GOPS at 537 mV in the 16 $\times $ speed-up mode. 40-nm CMOS test-chip measurements over 100 MNIST images show 97% classification accuracy. Simulation results over the entire 10 000 MNIST validation dataset images taking into account the circuit non-ideal effects of the MDL-based time-domain approach show a classification accuracy of 98.42%. The test-chip is operational down to the near-threshold voltage (up to 375 mV) while maintaining the classification accuracy over 90% in the 1 $\times $ speed-up mode. Furthermore, two methods of scaling MDLs to multi-bit weights are proposed. Simulation results for 1000-class AlexNet over 50 000 ImageNet validation dataset images show classification accuracy loss within 1% when compared with software implementation. The proposed MDL based time-domain approach performing 1-bit/8-bit weight and 8-bit input MAC operations when compared with the corresponding baseline digital implementations shows 2.09 $\times $ −2.32 $\times $ higher energy efficiency and 2.22 $\times $ −3.45 $\times $ smaller area. [ABSTRACT FROM AUTHOR]

This paper proposes a novel peak to average power ratio (PAPR) reduction scheme that requires no side information in orthogonal frequency division multiplexing (OFDM) systems. Unlike the selective mapping (SLM) or the partial transmit sequence (PTS) scheme, the proposed scheme deals with post inverse fast Fourier transform (IFFT) symbols and thus requires only a single IFFT processor in the transmitter. Compared to other conventional schemes implemented with a single IFFT processor, such as the circularly shifted phase sequences (CSPS) or the optimised circularly shifted phase sequences (OCSPS) method, the proposed scheme achieves an even lower complexity since only phase rotation and cyclic shifting of OFDM symbols are performed. More importantly, the proposed scheme significantly outperforms the CSPS and the OCSPS methods in reducing PAPR as shown in simulation results. An added benefit of the proposed scheme is that it employs a linear receiver, such as a maximal likelihood (ML) detector, a minimum mean square error (MMSE) estimator, or a zero forcing (ZF) estimator, to demap quadrature amplitude modulation (QAM) symbols. Especially the ML detector demaps the QAM symbols with no side information. Simulation results also show that the bit error rate (BER) of the proposed scheme has no loss when the ML detector, the ZF or the MMSE estimator is used with hard-decision compared to that of the conventional OFDM system without any PAPR reduction scheme over Rayleigh fading channel. [ABSTRACT FROM PUBLISHER]

Time-domain algorithms for blind separation of audio sources can be classified as being based either on a partial or complete decomposition of an observation space. The decomposition, especially the complete one, is mostly done under a constraint to reduce the computational burden. However, this constraint potentially restricts the performance. The authors propose a novel time-domain algorithm that is based on a complete unconstrained decomposition of the observation space. The observation space may be defined in a general way, which allows application of long separating filters, although its dimension is low. The decomposition is done by an appropriate independent component analysis (ICA) algorithm giving independent components that are grouped into clusters corresponding to the original sources. Components of the clusters are combined by a reconstruction procedure after estimating microphone responses of the original sources. The authors demonstrate by experiments that the method works effectively with short data, compared to other methods. [ABSTRACT FROM PUBLISHER]

In this paper, a time-domain signal processing system is designed based on LabWindows/CVI software development platform. This system realizes signal superimposed , integration/differential, correlation and convolution processing. The practical applications show that this system is reliable by comparing with mature software Matlab. [ABSTRACT FROM AUTHOR]