Front-end Acquisition Research Articles

Skin Detection System Using Infrared Optoelectronic Technology and Its Application in Facial Recognition

Facial recognition technology has made significant progress. However, variable lighting conditions can affect its performance. Considering the need to scan facial skin for recognition purposes, this study proposes a miniature optoelectronic acquisition system for skin in the near-infrared range. The system utilizes the C11708MA photodetector from Hamamatsu Photonics’ MS series as the probe for spectral data acquisition. Other hardware components are designed accordingly. A three-stage amplification buffer circuit is employed as the front-end acquisition and preprocessing circuit. The AD7671 chip from Analog Devices Inc. is selected as the AD converter, and the communication module utilizes the CY7C68013 chip from Cypress’ EZ-USB FX2 series. The control and transmission module employs the EP2C5T144C8N FPGA chip from ALTERA’s Cyclone II generation. In order to address the power supply requirements of the CY7C68013 USB chip (3.3 V), the FPGA core (1.2 V), and the AD7671 and front-end preprocessing circuit (5 V), AMS1117 voltage regulator chips are designed for stable 5 V–1.2 V and 5 V–3.3 V power supplies. In the experiments, wavelength calibration and spectral preprocessing are performed on the system prior to data processing. Near-infrared reflectance spectra of different skin conditions (melanoma, vitiligo) are compared with normal skin. The results demonstrate the accurate assessment capability of the designed infrared optoelectronic skin detection system. Facial skin data obtained from the system are used to generate facial images, and the recognition performance of different detection systems is compared in an algorithmic environment, thereby demonstrating the promising application prospects of the infrared optoelectronic skin detection system in the field of facial recognition.

A power-efficient acquisition front end for the Li-ion battery management systems

This paper presents a Coulomb sensing method-based power-efficient acquisition front-end (AFE) for Li-ion battery management systems (BMSs). The AFE, based on two self-calibrated incremental analog-to-digital converters (ADCs), measures the instant current flows in and out of the Li-ion battery, the cell voltage, and the internal and external temperature of the chip to compensate for the state of charge. A switched-capacitor CMOS bandgap reference (BGR) is proposed to obtain stable reference voltage over an operating temperature range of the BMS. A wake-up comparator indicates that a large charging and discharging current occurs while the BMS is in sleeping mode. This solution significantly reduces the obtained samples and has better power efficiency than an always-acquisition method. A prototype was fabricated in the 110-nm CMOS process, achieving a DNL of −0.85/+1.7 LSB and an INL of −3.2/+4.2 LSB. The total current consumption of the normal operation and the sleeping mode from a battery is 23 μA and 1.26 μA, respectively.

Reservoir Aquaculture Anti-theft System based on Convolutional Neural Network

This paper proposes an Anti-theft system for reservoir aquaculture based on convolutional neural network, including front-end acquisition equipment cloud server and terminal monitoring equipment. The system mainly uses the convolutional neural network image processing unit of the infrared sensor module to find and identify the people who invaded the reservoir, and issue a warning through the audio input and output module, and warn the reservoir manager of the reservoir situation through the terminal monitoring equipment. The purpose is to use the convolutional neural network to flexibly feed back the real-time anti-intrusion dynamics of the reservoir to the reservoir manager, reduce its unnecessary monitoring time, and reduce the input of labor costs.

Design of Automatic Batch Calibration and Correction System for IMU

Thanks to its light weight, low power consumption, and low price, the inertial measurement units (IMUs) have been widely used in civil and military applications such as autopilot, robotics, and tactical weapons. The calibration is an essential procedure before the IMU is put in use, which is generally used to estimate the error parameters such as the bias, installation error, scale factor of the IMU. Currently, the manual one-by-one calibration is still the mostly used manner, which is low in efficiency, time-consuming, and easy to introduce mis-operation. Aiming at this issue, this paper designs an automatic batch calibration method for a set of IMUs. The designed automatic calibration master controller can control the turntable and the data acquisition system at the same time. Each data acquisition front-end can complete data acquisition of eight IMUs one time. And various scenarios of experimental tests have been carried out to validate the proposed design, such as the multi-position tests, the rate tests and swaying tests. The results illustrate the reliability of each function module and the feasibility automatic batch calibration. Compared with the traditional calibration method, the proposed design can reduce errors caused by the manual calibration and greatly improve the efficiency of IMU calibration.

The Development of an Intelligent Zinc Oxide Lighting Arrester Tester

Zinc oxide arrester is often used as lightning protection device in 10KV distribution network. In order to check the reliable operation of the zinc oxide arrester, preventive tests are often carried out. In this project, the intelligent tester adopts high-precision clamp current mutual inductance technology as the front-end acquisition mode of current signals; Magnetic isolation technology is used to ensure the accuracy of current and voltage sampling and the anti-interference ability and testing accuracy of the instrument are improved by using fast Fourier transform for data processing. The integrated application of several technologies provides a basis for judging the operating state of the 10kV zinc oxide arrester. The charged measurement of operating parameters of 10kV zinc oxide arrester is realized and the measurement efficiency is improved.

Digital Suppression of EMI-Induced Errors in a Baseband Acquisition Front-End including Off-the-Shelf, EMI-Sensitive Operational Amplifiers

In this paper, the susceptibility to Electromagnetic Interference (EMI) of an analog signal acquisition front-end (AFE) due to EMI distortion in opamp-based pre-conditioning amplifiers is addressed. More specifically, the possibility to correct EMI-induced errors in the digital domain by post-processing the acquired digital waveforms is discussed and experimentally demonstrated for the first time with reference to an AFE based on EMI-sensitive, off-the-shelf operational amplifiers mounted on a specific EMI test PCB. Extensive experimental characterization in the presence of continuous wave and amplitude modulated EMI reveals the superior immunity to EMI of the proposed AFE and the robustness of the approach.

用电信息采集前置性能提升关键技术研究与应用

用电信息采集前置性能提升关键技术研究与应用

Design and Implementation of Wearable Dynamic Electrocardiograph Real-Time Monitoring Terminal

In order to detect Electrocardiograph (E.C.G.) signals in people's daily life accurately, in this study, a wearable real-time dynamic E.C.G. signal detection system based on the Internet of things technology was designed and implemented. Under the STM32 WeChat processor, a flexible fabric was used as the base of the sensor. It can process the collected E.C.G. signal through amplification and filtering of signal conditioning module, so as to satisfy the conversion of A/D. The E.C.G. signal acquisition front end and other hardware and software were designed based on AD8232 chip. And then an adaptive filter was designed based on standardized LMS algorithm (N.L.M.S.). E.C.G. signal in MIT-BIH database was used to detect the accuracy of R wave detection. In order to detect the restraining effect of baseline drift and motion artifact in E.C.G. after filtering, the accuracy of R wave detection in different movements of healthy personnel was tested. The results showed that after using the N.L.M.S. algorithm's adaptive filter to detect E.C.G. signals in MIT-BIH database, the accuracy (De%), sensitivity (Se%), and specificity (Sp%) calculated by the R-wave were all above 99%. It was then worn on the experimenter's chest and the E.C.G. signals were detected while experimenters sat still. It is found that it can restrain baseline drift in E.C.G. signal acquisition. In addition, when the experimenter did static standing, walking slowly, squatting and chest expansion, the detection rate of R wave was above 95%. Therefore, the designed system can monitor E.C.G. signals quickly and accurately.

An Energy-Efficient CMOS Dual-Mode Array Architecture for High-Density ECoG-Based Brain-Machine Interfaces.

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

Ultra‐low power digital front‐end for single lead ECG acquisition integrated with a time‐to‐digital converter

A low power single lead electrocardiogram front-end acquisition system in 0.18 μm CMOS operating at 0.5 V is presented here. The analogue blocks in low noise amplifier (LNA), filters and passive elements that perform amplification and DC offset cancellation are replaced by a moving average voltage to time converter (MA-VTC) to get amplification and anti-aliasing in the time domain. A digital feedback algorithm is used to cancel out the DC offset. The front-end structure is designed in the sub-threshold region of MOS to reduce the power consumption in the circuit. The proposed architecture consumes 50 nW of power with a gain of 670 μs/V. The output of the front-end is fed to an all digital time-to-digital converter (TDC) that operates in the near threshold region with a resolution of 586.4 ps and 32.5 μW power consumption.

A object detection algorithm based on pyramid Convolutional Neural Networks (CNN) and feature map fusion model

Abstract. The aim of this paper is to solve two problems: object detection of small objects and multi-view scenes. First, in practical applications, the collected traffic video is affected by the resolution, viewing angle, focal length and model of the front-end acquisition device. The object size, shape and attitude of the video to be detected are different, resulting in the overall detection performance of the algorithm recognition. In particular, for traffic intersections, the size of the vehicle is related to the distance between the vehicle and the camera, and the object resolution of the vehicle near the intersection is relatively high. As the relative distance increases, the resolution of the object gradually decreases, resulting in feature extraction of the detection object to be detected. And identification becomes more and more difficult, and the probability of the object being detected is greatly reduced. Secondly, there are usually many ways to collect traffic data, such as fixed-position camera, high-altitude camera, and cruising UAV (Unmanned Aerial Vehicle). These video sources collected at different viewing angles and locations pose challenges to the stability, robustness, and generalization capabilities of the detection algorithms. Therefore, design a new algorithm and optimizing model parameters and training samples of different source data is extremely important for multi-view object detection.An object detection algorithm based on pyramid Convolutional Neural Networks (CNN) and feature map fusion method was proposed, and the deep learning technology and the object detection algorithm are used to detect and identify the video objects of multiple viewing angles and different resolution scenes in traffic field. By mixing the lower and deeper feature map model, the algorithm can detect a smaller object in multiple viewing angles and different resolution scenes. Meanwhile, an image block and multi-threading technology was used to avoid scale limit of input image. The experiments show that it can be more efficient and accurate in practical applications of traffic detection filed.The method can be used for the existing network model (VGG16, ResNet101, etc.) to build the skeleton of the object detection algorithm. A new object detection algorithm is developed for these goals, which contain small object recognition and multi-view recognition of traffic video, and it can enable it to extract the lower features of the object and effectively realize multi-object recognition of different scenes. Using the pyramid CNN model, it is possible to effectively combine low-level features and high-level features to achieve feature extraction and fusion of the object, and to solve the problem of small object recognition accuracy to a certain extent. Meanwhile, in view of the shortcomings of the existing object detection algorithm to re-compress the image size, the image block and multi-threading technology are used to restore the original resolution of the image. By using this technology, the accuracy of image object to be detected can be improved.

Breaking the boundaries between analogue and digital

breaking the boundaries between analogue and digital

The MARTA (Muon Array With RPCs for Tagging Air Showers) Front-End Acquisition System

The design and performance of a new front-end acquisition system is presented in this paper. It was developed to be used with muon array with resistive plate chamber (RPCs) for tagging air showers (MARTA’s) RPC detectors that measure the muonic component of cosmic ray air showers. The system is hybrid and capable of counting active channels and performing a charge measurement. It is low power (1.43 W per 64 channels), compact ( $21.6 \times 14.4\,\,\mathrm {cm^{2}}$ per 64 channels), stable, and reliable to comply with the strict demands of field operation associated with experiments that measure air showers. The front-end is based on an application-specified integrated circuit [Multi Anode Read-Out Chip (MAROC)] with 64 input channels, which is responsible for the digitization of the RPC signals. While a simple threshold measurement is applied after a fast shaper to count particles, the charge measurement is performed by an analog to digital converter that reads the peak of a slow shaper. An field-programmable gate array is responsible for control of all the digital part which includes storing the MAROC outputs and sending them to a Central Unit [via low voltage differential signal (LVDS)] or PC (via USB). Both the count and charge measurement performance are assessed. The threshold efficiency curve is measured for different temperatures. For all the conditions tested, the efficiency is 100% for charges higher than 52 fC. The overall efficiency to detect muons is studied by placing an RPC between a hodoscope and comparing the efficiency of the front-end with another established data acquisition system. The efficiency results are consistent within the statistical uncertainties of the measurements showing that the MARTA front-end does not introduce unwanted inefficiencies in the setup. The charge measurement is tested in the laboratory using a signal generator. Different shaping conditions and input charges are used to study the waveform of the slow shaper. A linear dependence between input charge and the value measured by the MAROC was found and taken as the calibration curve for the particular channel tested.

Design and Research of Power Lithium Battery Management System

Due to the problems of overcharge, over discharge, internal and external short circuit and thermal runaway in the use of lithium battery, a low-power lithium battery management system was designed for these problems. This system takes MSP430 as the core, using BQ76925 chip of TI company as the front-end acquisition chip can detect 3 to 6 string lithium battery with voltage, current, temperature and other information collection, overcharge and over discharge protection, balance management and other functions. The test shows that the system is good for the management of lithium battery and has good accuracy and stability.

CMOS Ultralow Power Brain Signal Acquisition Front-Ends: Design and Human Testing.

Two brain signal acquisition (BSA) front-ends incorporating two CMOS ultralow power, low-noise amplifier arrays and serializers operating in mosfet weak inversion region are presented. To boost the amplifier's gain for a given current budget, cross-coupled-pair active load topology is used in the first stages of these two amplifiers. These two BSA front-ends are fabricated in 130 and 180 nm CMOS processes, occupying 5.45 mm 2 and 0.352 mm 2 of die areas, respectively (excluding pad rings). The CMOS 130-nm amplifier array is comprised of 64 elements, where each amplifier element consumes 0.216 μW from 0.4 V supply, has input-referred noise voltage (IRNoise) of 2.19 μV[Formula: see text] corresponding to a power efficiency factor (PEF) of 11.7, and occupies 0.044mm 2 of die area. The CMOS 180 nm amplifier array employs 4 elements, where each element consumes 0.69 μW from 0.6 V supply with IRNoise of 2.3 μV[Formula: see text] (corresponding to a PEF of 31.3) and 0.051 mm 2 of die area. Noninvasive electroencephalographic and invasive electrocorticographic signals were recorded real time directly on able-bodied human subjects, showing feasibility of using these analog front-ends for future fully implantable BSA and brain- computer interface systems.

Silicon-Integrated High-Density Electrocortical Interfaces

Recent demand and initiatives in brain research have driven significant interest toward developing chronically implantable neural interface systems with high spatiotemporal resolution and spatial coverage extending to the whole brain. Electroencephalography-based systems are noninvasive and cost efficient in monitoring neural activity across the brain, but suffer from fundamental limitations in spatiotemporal resolution. On the other hand, neural spike and local field potential (LFP) monitoring with penetrating electrodes offer higher resolution, but are highly invasive and inadequate for long-term use in humans due to unreliability in long-term data recording and risk for infection and inflammation. Alternatively, electrocorticography (ECoG) promises a minimally invasive, chronically implantable neural interface with resolution and spatial coverage capabilities that, with future technology scaling, may meet the needs of recently proposed brain initiatives. In this paper, we discuss the challenges and state-of-the-art technologies that are enabling next-generation fully implantable high-density ECoG interfaces, including details on electrodes, data acquisition front-ends, stimulation drivers, and circuits and antennas for wireless communications and power delivery. Along with state-of-the-art implantable ECoG interface systems, we introduce a modular ECoG system concept based on a fully encapsulated neural interfacing acquisition chip (ENIAC). Multiple ENIACs can be placed across the cortical surface, enabling dense coverage over wide area with high spatiotemporal resolution. The circuit and system level details of ENIAC are presented, along with measurement results.

Big traffic data processing framework for intelligent monitoring and recording systems

In recent years, intelligent monitoring and recording system (IMRS) has been widely used in many cities. It is a networked system which includes the front-end image acquisition system and the back-end data processing platform. In the front-end acquisition system, after recording the image data the front-end processing system analyzes the image data and automatically extracts some information of the passing vehicles, such as time, location, direction, the number and color of license plate, etc. After that, the information is sent to the back-end data processing platform for deeper analysis, such as vehicle trajectory tracking and traffic state estimation. However, as the data scales to a great amount, it becomes difficult for traditional analysis framework and tools to meet the requirement of big data analysis. HBase is the column-oriented database based on Hadoop, along with Map-Reduce programming paradigm. It can deal with the problems in processing big data that intelligent monitoring and recording system faces nowadays. In this paper, a big traffic data processing framework using HBase to analyze the data of IMRS is proposed. The performance of the proposed framework is compared with the Oracle strategy on three different kinds of applications, and the experimental results show that the proposed strategy is with promising performance in computation latency.

Universal Real-time Monitoring System Based on Ethernet in Test and Measurement Domain

In test and measurement systems, real-time monitoring is the effective method for obtaining the running condi- tions and making fault diagnosis in testing system. In this paper, a universal real-time monitoring system based on User Datagram Protocol (UDP) is discussed. Firstly, the component structure of monitoring system is introduced, and compari- sons of various kinds of data transmission modes between the front-end acquisition module and the back-end monitoring module are made. Secondly, the characteristics and data communication mode of UDP are discussed, and the general pro- cess of real-time data analysis and curve plotting are studied. Meanwhile, a multi-channel data acquisition and monitoring system application instance for aerospace testing is designed and analyzed. It is demonstrated that the proposed design method can satisfy the test requirements, and because of the universality, which can be applied to other test fields by means of appropriate transformation or revise.

The JEM-EUSO instrument

In this paper we describe the main characteristics of the JEM-EUSO instrument. The Extreme Universe Space Observatory on the Japanese Experiment Module (JEM-EUSO) of the International Space Station (ISS) will observe Ultra High-Energy Cosmic Rays (UHECR) from space. It will detect UV-light of Extensive Air Showers (EAS) produced by UHECRs traversing the Earth’s atmosphere. For each event, the detector will determine the energy, arrival direction and the type of the primary particle. The advantage of a space-borne detector resides in the large field of view, using a target volume of about 1012 tons of atmosphere, far greater than what is achievable from ground. Another advantage is a nearly uniform sampling of the whole celestial sphere. The corresponding increase in statistics will help to clarify the origin and sources of UHECRs and characterize the environment traversed during their production and propagation. JEM-EUSO is a 1.1 ton refractor telescope using an optics of 2.5 m diameter Fresnel lenses to focus the UV-light from EAS on a focal surface composed of about 5,000 multi-anode photomultipliers, for a total of ≃3⋅105 channels. A multi-layer parallel architecture handles front-end acquisition, selecting and storing valid triggers. Each processing level filters the events with increasingly complex algorithms using FPGAs and DSPs to reject spurious events and reduce the data rate to a value compatible with downlink constraints.

Research of Intelligent Building Security Automatic Monitoring Alarm System

In this study by analyzing the types and characteristics of intelligent building, this paper expounds the intelligent building security automatic monitoring alarm system of layered structure: the presentation layer, control layer, processing layer, transport layer and execution layer, support layer, collected layer, describes the system composition: front-end information acquisition system, information transmission control system, information management system, automatic alarm system. The system can improve the efficiency of the intelligent building security.