Analog Signal Research Articles

Design and experimental demonstration of functional devices for energy manipulation in new analog acoustic computers

The development of new analog computers based on analog signals of electromagnetic or elastic waves has become a hot research topic in recent years. In this Letter, based on the concept of quantum state transfer, we study the process of energy transfer between acoustic cavities. Through the transformation from the temporal domain to the spatial domain, we further study the process of energy transfer between acoustic waveguides, and then we realize the applications of energy distributors and limiters in the acoustic system, which are the functional devices of wireless communication technology in new analog acoustic computers. We both simulationlly and experimentally demonstrate the effectiveness of the two applications. The energy distributor and limiter we proposed have the advantages of a relatively wide operating frequency range and adjustability. Our work shows the transfer mechanism of acoustic energy between different structures, which increases its potential application value in new analog acoustic computers.

Recent Advances in Artificial Sensory Neurons: Biological Fundamentals, Devices, Applications, and Challenges.

Spike-based neural networks, which use spikes or action potentials to represent information, have gained a lot of attention because of their high energy efficiency and low power consumption. To fully leverage its advantages, converting the external analog signals to spikes is an essential prerequisite. Conventional approaches including analog-to-digital converters or ring oscillators, and sensors suffer from high power and area costs. Recent efforts are devoted to constructing artificial sensory neurons based on emerging devices inspired by the biological sensory system. They can simultaneously perform sensing and spike conversion, overcoming the deficiencies of traditional sensory systems. This review summarizes and benchmarks the recent progress of artificial sensory neurons. It starts with the presentation of various mechanisms of biological signal transduction, followed by the systematic introduction of the emerging devices employed for artificial sensory neurons. Furthermore, the implementations with different perceptual capabilities are briefly outlined and the key metrics and potential applications are also provided. Finally, we highlight the challenges and perspectives for the future development of artificial sensory neurons.

Ultra‐Low Power Photosynaptic Devices Based on Persistent Photoconductivity‐Modulated SnO2

AbstractRealizing visual perception performance using robust metal oxide devices is highly desirable for brain‐inspired neuromorphic computing, visual prosthetics, and artificial intelligence. SnO2, a representative transparent semiconductor, has attracted considerable attention for its potential in ultraviolet photodetectors and photosynaptic devices operating in solar‐blind spectral ranges. This problem has been circumvented by lowering the electron concentration and reducing the persistent photoconductivity (PPC), one of the essential properties of photosynaptic devices. In this case, the electron concentration and PPC properties in SnO2 must be controlled independently. This paper reports the successful control of PPC in SnO2 by controllable addition of Zn and Sr ions, which act as acceptors to annihilate the charge carriers and as a reductant to form oxygen vacancies, respectively. Zn and Sr‐added SnO2 exhibits superior synaptic performance with low energy consumption, successfully imitating the biological synapses without a gate electrode. As a result, the PPC remains at about 40% even after 20 seconds of turning off the ultraviolet light, and energy consumption is reduced to 1–10 fJ, similar to energy consumption with biological synapses. A novel optical sensor that can receive analog signals is demonstrated using a photosynaptic device with Zn and Sr‐added SnO2 thin films.

Research on the design and application of Analog-to-Digital converters

Abstract. In the modern digital era, the conversion of analog signals to digital data is a crucial process for a wide range of applications in industries such as consumer electronics, medical devices, and telecommunications. The accelerated evolution of digital technology is driving a surge in demand for high-performance analog-to-digital converters (ADCs). The paper presents a comprehensive analysis of analog-to-digital converters, emphasizing their types, applications, performance measurements, and most recent developments. In the paper, emphasis is placed on the mechanisms of different ADC architectures, such as Successive Approximation Register (SAR), Sigma-Delta (-), and Flash ADCs, and their respective strengths and weaknesses are evaluated. In terms of technique, it synthesizes and summarizes research results from multiple academic sources and technical papers. In addition to underscoring the pivotal concerns of ADC design, including power consumption, resolution, and sample rate, the research delves into novel approaches that leverage the use of advanced materials and techniques. The research results demonstrate the continued development of ADC technology and its critical function in improving the effectiveness and performance of digital systems.

A Low-Power 5-Bit Two-Step Flash Analog-to-Digital Converter with Double-Tail Dynamic Comparator in 90 nm Digital CMOS

Low-power portable devices play a major role in IoT applications, where the analog-to-digital converters (ADCs) are very important components for the processing of analog signals. In this paper, a 5-bit two-step flash ADC with a low-power double-tail dynamic comparator (DTDC) using the control switching technique is presented. The most significant bit (MSB) in the proposed design is produced by only one low-power DTDC in the first stage, and the remaining bits are generated by the flash ADC in the second stage with the help of an auto-control circuit. A control circuit produced reference voltages with respect to the control input and mid-point voltage (Vk). The proposed design and simulations are carried out using 90 nm CMOS technology. The result shows that the peak differential non-linearity (DNL) and integral non-linearity (INL) are +0.60/−0.69 and +0.66/−0.40 LSB, respectively. The signal-to-noise and distortion ratio (SNDR) for an input signal having a frequency of 1.75 MHz is found to be 30.31 dB. The total power consumption of the proposed design is significantly reduced, which is 439.178 μW for a supply voltage of 1.2 V. The figure of merit (FOM) is about 0.054 pJ/conversion step at 250 MS/s. The present design provides low power consumption and occupies less area compared to the existing works.

Non-contact monitoring of human cardiorespiratory activity during sleep using FMCW millimeter wave radar

Millimeter wave radar can be utilized for non-contact, long-term, and accurate health monitoring. This paper presents an application for vital signs detection including respiratory rate (RR) and heart rate (HR) during sleep, as well as facilitating rapid detection and extended recording of apnea syndrome. This paper employs frequency spectrum analysis for RR estimation and develops an algorithm of template matching estimation (TME) for real-time, accurate HR estimation by comparing radar signal fragments to a template with a frequency signature. The time required for HR estimation is reduced to 1 s. In addition, a neural network-based model was developed for detecting the apnea events with the accuracy of 93.60% and calculates the duration in seconds. The system underwent testing using irregular analog signals of vital signs. In the actual scene testing, the accuracy of the RR estimation is 90.58%, and 90.25% for HR estimation.

Design and implementation of a nondestructive testing system for magnetic field imaging based on machine learning

This study presents the design of a nondestructive testing (NDT) system for detecting metal defects using a magnetic field sensor array. The system integrates a hardware-software detection framework that includes parallel computation and processing cores, along with multiple anisotropic magnetoresistance (AMR) sensors. These AMR sensors capture subtle variations in the magnetic field, which serve as the foundation for defect identification. The system enables synchronized data acquisition from multiple AMR sensors, achieving multi-axis data fusion and parallel signal processing. Furthermore, the detection system gathers defect features to train an NDT system based on machine learning (ML). Subsequently, ML-generated defect imaging features are processed through an imaging framework, producing magnetic field imaging (MFI) for precise defect localization. The study tested six different metal samples with various defect sizes and types to validate the system. The results demonstrate the system’s ability to accurately detect and identify different defects, highlighting the high precision of magnetic field detection and the overall effectiveness of the proposed system for NDT.

Enhancing IoT data acquisition efficiency via FPGA-based implementation with OpenCL framework

The increasing demand for real-time data processing in Internet of Things (IoT) applications necessitates the development of efficient and flexible data acquisition systems capable of receiving and processing data from various sensor types. In conjunction with OpenCL, field-programmable gate arrays (FPGAs) have recently emerged as powerful platforms for accelerating data-intensive tasks. This study explored the implementation of an FPGA for data acquisition using OpenCL, aiming to design and implement an efficient data acquisition system tailored for IoT applications. Utilizing OpenCL for FPGA-based data acquisition offers several advantages that contribute to system efficiency, particularly in hardware interfaces between FPGA and external devices used in IoT applications. OpenCL abstracts the complexity of the FPGA hardware interface to external DDR memory for storing temporary data and a communication interface to the host CPU for transferring the collected data and enabling remote access, enabling developers to focus on algorithm design and functionality. To enable data reading from an external analog-to-digital converter (ADC) chip for IoT applications, we developed a component module that utilizes the Avalon-streaming interface and can stream the data to the OpenCL kernel. An experiment was conducted to demonstrate the performance of our proposed design. According to the findings of the experiments, a data acquisition implementation based on an FPGA and OpenCL can simultaneously read analog signals via a multichannel ADC. The proposed design provides a foundation for designing efficient data acquisition solutions, addressing the increasing needs of FPGA-based data acquisition in various IoT environments.

Direct Current to Digital Converter (DIDC): A Current Sensor.

This paper introduces a systematic approach to the design of Direct Current-to-Digital Converter (DIDC) specifically engineered to overcome the limitations of traditional current measurement methodologies in System-on-Chip (SoC) designs. The proposed DIDC addresses critical challenges such as high power consumption, large area requirements, and the need for intermediate analog signals. By incorporating a current mirror in a cascode topology and managing the current across multiple binary-sized branches with the Successive Approximation Register (SAR) logic, the design achieves precise current measurement. A simple comparator, coupled with an isolation circuit, ensures accurate and reliable sensing. Fabricated using the TSMC 180 nm process, the DIDC achieves 8-bit precision without the need for nonlinearity calibration, showcasing remarkable energy efficiency with an energy per conversion of 1.52 pJ, power consumption of 117 µW, and a compact area of 0.016 mm². This innovative approach not only reduces power consumption and area, but also provides a scalable and efficient solution for next-generation semiconductor technologies. The ability to conduct online measurements during both standard operations and in-field conditions significantly enhances the performance and reliability of SoCs, making this DIDC a promising advancement in the field.

Light Output Function and Pulse-Shape Discrimination Capability of p-Terphenyl Organic Scintillator in Wide Neutron Energy Range of 1.1 to 19 MeV

In this work, we studied the light-output properties, efficiency function, as well as the pulse-shape discrimination (PSD) capability of p-Terphenyl scintillator. The selected solid cylindrical scintillation detector has a thickness of 45 mm and a diameter of 45 mm. Recently presented studies of light-output functions have only been measured for low-neutron energies. Our motivation has been to determine the light output function for p-Terphenyl scintillator more accurately over a wider neutron energy range. The measurements have been carried out with mono-energetic neutron beams in the wide energy range from 1.1 to 19 MeV. The neutron–gamma spectrometric system which we developed has been used for the measurement. The input analog signal from the detector was digitized with a fast 12-bits analog to digital converter with a sampling frequency of 1 GHz. Measured data from the detector are processed into the gamma and neutron spectra. The accurate light output function for the p-Therphenyl scintillator has been calculated. The pulse-shape discrimination capability, as well as the detection efficiency, of a p-Terphenyl scintillator are lower in comparison with a NE-213 equivalent detector.

Magneto-acousto-electrical tomography based on frequency response compensated linearly frequency-modulated signal stimulation

Objective. In magneto-acousto-electrical tomography (MAET), linearly frequency-modulated (LFM) signal stimulation uses much lower peak voltage than the spike pulse stimulation, lengthening the operation life of the transducer. However, due to the uneven frequency responses of the transducer, the low-noise amplifier (LNA), and the bandpass filter (BPF), MAET using LFM signal stimulation suffers from longitudinal resolution loss. In this paper, frequency response compensated linearly frequency-modulated (FRC-LFM) signal stimulation is investigated to resolve the problem.Approach. The physical model of measurement of the frequency responses of the transducer and the cascading module of the detection electrodes, the LNA, and the BPF is constructed. The frequency responses are approximated by fitting a curve to the measurement data. The frequency response compensation function is set to the reciprocal of the product of the frequency responses. The digital FRC-LFM signal is generated in MATLAB and converted to analog signal through an arbitrary waveform generator. Two groups of MAET experiments are designed to confirm the performance of the FRC-LFM signal stimulation. Pure agar phantom with rectangular through-holes and agar phantom with pork tissue inclusion serve as the samples.Main results. The pulse-compressed magneto-acousto-electrical signal obtained using FRC-LFM stimulation has narrower main-lobe than that obtained using LFM excitation, although the signal to noise pulse interference ratio of the former is little lower than that of the latter, which is due to the limitation of the power amplifier. FRC-LFM also proves to be an effective method to utilize the frequency outside the working band of the transducer in MAET.Significance. The method in this study compensates for the longitudinal resolution loss due to the uneven frequency responses. Combining with high-capability power amplifier and high-performance LNA, the MAET using FRC-LFM signal stimulation can potentially achieve high longitudinal resolution and high sensitivity, advancing MAET toward the clinical application.

The excessive tonic inhibition of the peri-infarct cortex depresses low gamma rhythm power during post-stroke recovery.

The cortex immediately surrounding a brain ischemic lesion, the peri-infarct cortex, harbors a large part of the potential to recover lost functions. However, our understanding of the neurophysiological conditions in which synaptic plasticity operates, remains limited. Here we hypothesized that the chronic imbalance between excitation and inhibition of the peri-infarct cortex prevents the normalization of the gamma rhythm, a waveband of neural oscillations thought to orchestrate action potential trafficking. Probing the local field potential activity of the forelimb primary sensory cortex (S1FL) located in the peri-infarct cortex of male adult mice, we found a constant, deep reduction of low-gamma oscillation power (L-gamma; 30-50 Hz) precisely during the critical time window for recovery (1 to 3 weeks after stroke). The collapse of L-gamma power negatively corelated with behavioral progress in affected forelimb use. Mapping astrocyte reactivity and GABA-like immunoreactivity in the peri-infarct cortex revealed a parallel high signal, which gradually increased when approaching the lesion. Increasing tonic inhibition with local infusion of GABA or by blocking its recapture reduced L-gamma oscillation power in a magnitude similar to stroke. Conversely, the negative allosteric modulation of tonic GABA conductance using L655,708 or the gliopeptide ODN rescued the L-gamma power of the peri-infarct cortex. Altogether the present data point-out that the chronic excess of ambient GABA in the peri-infarct cortex limits the generation of L-gamma oscillations in the repairing cortex and suggests that rehabilitative interventions aimed at normalizing low-gamma power within the critical period of stroke recovery could optimize the restitution of lost functions.Significance Statement After a stroke, the recovery of lost motor function depends on the reorganization of surviving neural networks. However, the excitation/inhibition balance in the repairing area is suboptimal as it leans excessively towards inhibition. In this work, using an in vivo approach, we demonstrate here that this imbalance, occurring during the critical window for recovery, leads to the collapse of gamma oscillations, a crucial cerebral rhythm for organizing neural communication. This study reinforces the concept of timely therapeutic interventions aimed at correcting the pathological oscillatory regimen of stroke recovery to enhance plasticity.

Noise-resilient designs and analysis for optical neural networks

Abstract All analog signal processing is fundamentally subject to noise, and this is also the case in next generation implementations of Optical Neural Networks (ONNs). Therefore, we propose the first hardware-based approach to mitigate noise in ONNs. A tree-like and an accordion-like design are constructed from a given Neural Network (NN) that one wishes to implement. Both designs have the capability that the resulting ONNs give outputs close to the desired solution. To establish the latter, we analyze the designs mathematically. Specifically, we investigate a probabilistic framework for the tree-like design that establishes the correctness of the design, i.e., for any feed-forward NN with Lipschitz continuous activation functions, an ONN can be constructed that produces output arbitrarily close to the original. ONNs constructed with the tree-like design thus also inherit the universal approximation property of NNs. For the accordion-like design, we restrict the analysis to NNs with linear activation functions and characterize the ONNs’ output distribution using exact formulas. Finally, we report on numerical experiments with LeNet ONNs that give insight into the number of components required in these designs for certain accuracy gains. The results indicate that adding just a few components and/or adding them only in the first (few) layers in the manner of either design can already be expected to increase the accuracy of ONNs considerably. To illustrate the effect we point to a specific simulation of a LeNet implementation, in which adding one copy of the layers components in each layer reduces the Mean Squared Error (MSE) by 59.1% for the tree-like design and by 51.5% for the accordion-like design. In this scenario, the gap in accuracy of prediction between the noiseless NN and the ONNs reduces even more: 93.3% for the tree-like design and 80% for the accordion-like design.

СИСТЕМА "АЛЬТРА" ДІАГНОСТУВАННЯ СТАНУ ІЗОЛЯЦІЇ ЕЛЕКТРИЧНОЇ МЕРЕЖІ ТА ЗАХИС-ТУ ЇЇ ВІД ОДНОФАЗНИХ ЗАМИКАНЬ

One of the problems in an electrical network with an isolated (compensated) neutral is the selective determina-tion of a damaged element during a single-phase earth fault. Taking into account the negative impact of single-phase earth faults on the equipment of electrical networks with an isolated or compensated neutral, as well as improving the electrical safety conditions of people and animals, it is necessary to install selective protections with the of turning off of damaged element. Selective operation of protections against such damage can be en-sured on the basis of the analysis of zero-sequence currents, phase voltages and zero-sequence voltage of the bus section from which these feeders are powered. As operational experience has shown, selective operation of pro-tection against single-phase earth faults in such networks can be ensured only with the use of digital devices. The work considers the "Altra" system, designed for diagnosing the state of insulation, protecting feeders of 6-35 kV from single-phase earth fault, recording analog and binary signals of electrical installations. Altra digital protec-tion simultaneously covers up to 28 feeders, which provides comprehensive protection against single-phase earth faults of all substation feeders. The working algorithm of the developed protection against single-phase earth faults is based on the analysis of the transient process immediately after the occurrence of damage, which provides selective identification of the damaged feeder both in networks with an isolated neutral and in networks with a compensated. The system organizes the recording of oscillograms of emergency processes and infor-mation about damage by "Altra" devices, followed by their transfer to the dispatcher's automated workplace. This makes it possible to carry out a detailed analysis of accidents and diagnose the insulation of the electrical net-work.

Compact, Energy Efficient Simultaneous Transmission of Analog and Different Digital Signals With a Single Ring Modulator

Compact, Energy Efficient Simultaneous Transmission of Analog and Different Digital Signals With a Single Ring Modulator

Exploiting Spatial Ionic Dynamics in Solid-State Organic Electrochemical Transistors for Multi-Tactile Sensing and Processing.

The human nervous system inspires the next generation of sensory and communication systems for robotics, human-machine interfaces (HMIs), biomedical applications, and artificial intelligence. Neuromorphic approaches address processing challenges; however, the vast number of sensors and their large-scale distribution complicate analog data manipulation. Conventional digital multiplexers are limited by complex circuit architecture and high supply voltage. Large sensory arrays further complicate wiring. An 'in-electrolyte computing' platform is presented by integrating organic electrochemical transistors (OECTs) with a solid-state polymer electrolyte. These devices use synapse-like signal transport and spatially dependent bulk ionic doping, achieving over 400 times modulation in channel conductance, allowing discrimination of locally random-access events without peripheral circuitry or address assignment. It demonstrates information processing from 12 tactile sensors with a single OECT output, showing clear advantages in circuit simplicity over existing all-electronic, all-digital implementations. This self-multiplexer platform offers exciting prospects for circuit-free integration with sensory arrays for high-quality, large-volume analog signal processing.

Cortically Disparate Visual Features Evoke Content-Independent Load Signals during Storage in Working Memory.

It is well established that holding information in working memory (WM) elicits sustained stimulus-specific patterns of neural activity. Nevertheless, here we provide evidence for a distinct class of neural activity that tracks the number of individuated items in working memory, independent of the type of visual features stored. We present two EEG studies of young adults of both sexes that provide robust evidence for a signal tracking the number of individuated representations in working memory, regardless of the specific feature values stored. In Study 1, subjects maintained either colors or orientations across separate blocks in a single session. We found near-perfect generalization of the load signal between these two conditions, despite being able to simultaneously decode which feature had been voluntarily stored. In Study 2, participants attended to two features with very distinct cortical representations: color and motion coherence. We again found evidence for a neural load signal that robustly generalized across these distinct visual features, even though cortically disparate regions process color and motion coherence. Moreover, representational similarity analysis provided converging evidence for a content-independent load signal, while simultaneously showing that unique variance in EEG activity tracked the specific features that were stored. We posit that this load signal reflects a content-independent "pointer" operation that binds objects to the current context while parallel but distinct neural signals represent the features that are stored for each item in memory.

A plant based electrochemical device with transistor like behavior

Since its invention, transistor has become the fundamental building block of virtually all of today’s integrated circuits which constitute the electronics technology. In the present letter, we report the vegetal version of a self-powered electrochemical transistor, i.e., a device with an inherent energy source which exhibits energy-converting and amplifying-modulating properties. Surprisingly, at the interface constituted by the surface of an n-type germanium sample and a vegetal slice it become apparent that the surface of the n-type germanium passing anodic current acts as a multiplying collector for holes. Experimental transconductance characteristics show well-defined the cut-off, active and saturation regions. Likewise, since the collector is itself part of an electrolytic cell, device incorporates its own power supply. Thereby, it is able to reproduce an analogical AC signal via the DC electrolytic cell voltage. Furthermore, one of the most outstanding properties of the transistors is their DC current gain (hFE), which for commercially available small-signal transistors typically ranges 50–350, and for medium power transistors 15–70. The proposed device presents an hFE of 70 and 40 for the potato and apple based transistors respectively. Genuinely, this approach opens an alternative route for the development of a novel green electronics based on self-powered vegetative devices. Genuinely, this approach opens an alternative route for the development of a novel green electronics based on self-powered vegetative devices which could works as sensing devices for monitoring living plants during sow and harvest.

A Review of Current Differencing Buffered Amplifiers: Performance Metrics and Technological Advances

Current Differencing Buffered Amplifiers (CDBAs) are a critical class of analog circuit components capable of handling both current and voltage signals with minimal power consumption. Due to their low impedance voltage output, they play a significant role in modern electronics for developing high-performance, high-precision analog and mixed-signal circuits. But, designing and characterizing CDBAs pose several challenges, such as ensuring stability at high frequencies, minimizing noise impact for high-precision applications, and enhancing adaptability. Integrating CDBAs with other analog components to create multifunctional integrated circuits opens many opportunities in the analog signal-processing domain. This paper reviews the evolution and applications of CDBAs in analog signal processing. Various implementation schemes, including those using commercial Current Feedback Amplifiers (CFAs) and novel CMOS configurations, are analyzed for their performance metrics such as supply voltage, power dissipation, input/output impedances, and technology node. Future trends and challenges in advancing CDBA technology towards higher integration and lower-voltage operation are discussed, highlighting potential applications in next-generation electronics.

A cognitive communication jamming strategy based on Transformer and Deep Reinforcement Learning

The advent of sophisticated communication technologies, such as cognitive radio and anti-jamming techniques, has significantly elevated the challenge of disrupting enemy communications. Nevertheless, the inherent openness of wireless communications remains a vulnerability that can be exploited to interfere with them. Some contemporary Reinforcement Learning (RL)-based jamming strategies examine methods for rapidly identifying the optimal jamming strategy for a specific modulated signal. However, such algorithms lack the flexibility and responsiveness required to effectively counter the enemy’s evolving communication strategies. To address this issue, we propose a Transformer and Deep Reinforcement Learning (DRL)-based jamming strategy that can be trained to identify jamming methods for multiple digital and analog signals. In particular, the Transformer Encoder is employed as a network for DRL to process the state information pertaining to the enemy communication. Subsequently, the decision module of the Double Deep Q Network (DDQN) is utilized to select the jamming action based on the processed information. Furthermore, we have devised a reward function and constructed an invalid jamming list, with the objective of selecting an action that requires low power consumption and enhances the convergence speed of the algorithm. The experimental results demonstrate that the algorithm proposed in this paper exhibits notable performance advantages in comparison to other networks and DRL algorithms.