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  Subjects -> ELECTRONICS (Total: 207 journals)
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Frontiers in Electronics
Number of Followers: 7  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2673-5857
Published by Frontiers Media Homepage  [96 journals]
  • Research on optimal coil configuration scheme of insulator relay WPT
           system

    • Authors: Wei Wang, Mingrong Duan, Zhenwei Zeng, Huai Liu, Zhenya Ji
      Abstract: This paper presents the optimized structure of the multi-relay coils insulator of WPT system. With the rapid development of the smart grid, on-line monitoring devices in the transmission tower have been widely used. However, the power supply problem has become an important bottleneck in the development of transmission tower intelligent sensing technology. Hence, the multi-relay coils wireless power transfer technology has been proposed to supply for the tower monitoring equipment in this paper. Compared with traditional multi-relay coils, the effects of the number, arrangement position and turns of relay coils on the performance of WPT system are further explored. The simulation results show that the operation performance of WPT can be significantly improved by optimizing the coil arrangement position and turns. Moreover, there are multiple configuration schemes that the design indexes of the system could be achieved. The experiment results show that in the 110 kV high-voltage transmission with the insulator length of 1.015 m, the transmitting power and efficiency of the WPT system could be increased to 1.81 W and 60.11% respectively by parameters optimization, which ensures the continuous and stable work of the monitoring equipment.
      PubDate: 2023-01-26T00:00:00Z
       
  • CoFHE: Software and hardware Co-design for FHE-based machine learning as a
           service

    • Authors: Mengxin Zheng, Lei Ju, Lei Jiang
      Abstract: Introduction: Privacy concerns arise whenever sensitive data is outsourced to untrusted Machine Learning as a Service (MLaaS) platforms. Fully Homomorphic Encryption (FHE) emerges one of the most promising solutions to implementing privacy-preserving MLaaS. But prior FHE-based MLaaS faces challenges from both software and hardware perspectives. First, FHE can be implemented by various schemes including BGV, BFV, and CKKS, which are good at different FHE operations, e.g., additions, multiplications, and rotations. Different neural network architectures require different numbers of FHE operations, thereby preferring different FHE schemes. However, state-of-the-art MLaaS just naïvely chooses one FHE scheme to build FHE-based neural networks without considering other FHE schemes. Second, state-of-the-art MLaaS uses power-hungry hardware accelerators to process FHE-based inferences. Typically, prior high-performance FHE accelerators consume >160 Watt, due to their huge capacity (e.g., 512 MB) on-chip SRAM scratchpad memories.Methods: In this paper, we propose a software and hardware co-designed FHE-based MLaaS framework, CoFHE. From the software perspective, we propose an FHE compiler to select the best FHE scheme for a network architecture. We also build a low-power and high-density NAND-SPIN and SRAM hybrid scratchpad memory system for FHE hardware accelerators.Results: On average, under the same security and accuracy constraints, on average, CoFHE accelerates various FHE-based inferences by 18%, and reduces the energy consumption of various FHE-based inferences by 26%.Discussion: CoFHE greatly improves the latency and energy efficiency of FHE-based MLaaS.
      PubDate: 2023-01-12T00:00:00Z
       
  • Analytic circuit model for thermal drying behavior of electronic inks

    • Authors: Gabriel Maroli, Santiago Boyeras, Hernan Giannetta, Sebastian Pazos, Joel Gak, Alejandro Raúl Oliva, María Alicia Volpe, Pedro Marcelo Julian, Felix Palumbo
      Abstract: Understanding the sintering process of conductive inks is a fundamental step in the development of sensors. The intrinsic properties (such as thermal conductivity, resistivity, thermal coefficient, among others) of the printed devices do not correspond to those of the bulk materials. In the field of biosensors porosity plays a predominant role, since it defines the difference between the geometric area of the working electrode and its electrochemical surface area. The analysis reported so far in the literature on the sintering of inks are based on their DC characterization. In this work, the shape and distribution of the nanoparticles that make up the silver ink have been studied employing a transmission electron microscopy. Images of the printed traces have been obtained through a scanning electron microscope at different sintering times, allowing to observe how the material decreases its porosity over time. These structural changes were supported through electrical measurements of the change in the trace impedance as a function of drying time. The resistivity and thermal coefficient of the printed tracks were analyzed and compared with the values of bulk silver. Finally, this work proposes an analytical circuit model of the drying behavior of the ink based on AC characterization at different frequencies. The characterization considers an initial time when the spheric nanoparticles are still surrounded by the capping agent until the conductive trace is obtained. This model can estimate the characteristics that the printed devices would have, whether they are used as biosensors (porous material) or as interconnections (compact material) in printed electronics.
      PubDate: 2023-01-06T00:00:00Z
       
  • Breakdown-limited endurance in HZO FeFETs: Mechanism and improvement under
           bipolar stress

    • Authors: Kasidit Toprasertpong, Mitsuru Takenaka, Shinichi Takagi
      Abstract: Breakdown is one of main failure mechanisms that limit write endurance of ferroelectric devices using hafnium oxide-based ferroelectric materials. In this study, we investigate the gate current and breakdown characteristics of Hf0.5Zr0.5O2/Si ferroelectric field-effect transistors (FeFETs) by using carrier separation measurements to analyze electron and hole leakage currents during time-dependent dielectric breakdown (TDDB) tests. Rapidly increasing substrate hole currents and stress-induced leakage current (SILC)-like electron currents can be observed before the breakdown of the ferroelectric gate insulator of FeFETs. This apparent degradation under voltage stress is recovered and the time-to-breakdown is significantly improved by interrupting the TDDB test with gate voltage pulses with the opposite polarity, suggesting that defect redistribution, rather than defect generation, is responsible for the trigger of hard breakdown.
      PubDate: 2022-12-21T00:00:00Z
       
  • XMA2: A crossbar-aware multi-task adaption framework via 2-tier masks

    • Authors: Fan Zhang, Li Yang, Jian Meng, Jae-sun Seo, Yu Cao, Deliang Fan
      Abstract: Recently, ReRAM crossbar-based deep neural network (DNN) accelerator has been widely investigated. However, most prior works focus on single-task inference due to the high energy consumption of weight reprogramming and ReRAM cells’ low endurance issue. Adapting the ReRAM crossbar-based DNN accelerator for multiple tasks has not been fully explored. In this study, we propose XMA2, a novel crossbar-aware learning method with a 2-tier masking technique to efficiently adapt a DNN backbone model deployed in the ReRAM crossbar for new task learning. During the XMA2-based multi-task adaption (MTA), the tier-1 ReRAM crossbar-based processing-element- (PE-) wise mask is first learned to identify the most critical PEs to be reprogrammed for essential new features of the new task. Subsequently, the tier-2 crossbar column-wise mask is applied within the rest of the weight-frozen PEs to learn a hardware-friendly and column-wise scaling factor for new task learning without modifying the weight values. With such crossbar-aware design innovations, we could implement the required masking operation in an existing crossbar-based convolution engine with minimal hardware/memory overhead to adapt to a new task. The extensive experimental results show that compared with other state-of-the-art multiple-task adaption methods, XMA2 achieves the highest accuracy on all popular multi-task learning datasets.
      PubDate: 2022-12-20T00:00:00Z
       
  • A review and analysis of current-mode biosensing front-end ICs for
           nanopore-based DNA sequencing

    • Authors: Xu Liu, Qiumeng Fan, Zhijie Chen, Peiyuan Wan, Wei Mao, Hao Yu
      Abstract: Bio-sensors connect the biological world with electronic devices, widely used in biomedical applications. The combination of microelectronic and medical technologies makes biomedical diagnosis more rapid, accurate, and efficient. In this article, the current-mode biosensing front-end integrated circuits (ICs) for nanopore-based DNA sequencing are reviewed and analyzed, aiming to present their operation theories, advantages, limitations, and performances including gain, bandwidth, noise, and power consumption. Because biological information and external interference are contained in extremely weak sensing current, usually at the pA or nA level, it is challenging to accurately detect and restore the desired signals. Based on the requirements of DNA sequencing, this paper shows three circuit topologies of biosensing front-end, namely, discrete-time, continuous-time, and current-to-frequency conversion types. This paper also makes an introduction to the current-mode sensor array for DNA sequencing. To better review and evaluate the research of the state-of-the-art, the most relevant published works are summarized and compared. The review and analysis would help the researchers be familiar with the requirements, constraints, and methods for current-mode biosensing front-end IC designs for nanopore-based DNA sequencing.
      PubDate: 2022-11-29T00:00:00Z
       
  • Kinetic energy harvesting based sensing and IoT systems: A review

    • Authors: Zijie Chen, Fei Gao, Junrui Liang
      Abstract: The rapid advance of the Internet of Things (IoT) has attracted growing interest in academia and industry toward pervasive sensing and everlasting IoT. As the IoT nodes exponentially increase, replacing and recharging their batteries proves an incredible waste of labor and resources. Kinetic energy harvesting (KEH), converting the wasted ambient kinetic energy into usable electrical energy, is an emerging research field where various working mechanisms and designs have been developed for improved performance. Leveraging the KEH technologies, many motion-powered sensors, where changes in the external environment are directly converted into corresponding self-generated electrical signals, are developed and prove promising for multiple self-sensing applications. Furthermore, some recent studies focus on utilizing the generated energy to power a whole IoT sensing system. These systems comprehensively consider the mechanical, electrical, and cyber parts, which lead a further step to truly self-sustaining and maintenance-free IoT systems. Here, this review starts with a brief introduction of KEH from the ambient environment and human motion. Furthermore, the cutting-edge KEH-based sensors are reviewed in detail. Subsequently, divided into two aspects, KEH-based battery-free sensing systems toward IoT are highlighted. Moreover, there are remarks in every chapter for summarizing. The concept of self-powered sensing is clarified, and advanced studies of KEH-based sensing in different fields are introduced. It is expected that this review can provide valuable references for future pervasive sensing and ubiquitous IoT.
      PubDate: 2022-10-06T00:00:00Z
       
  • Influence analysis of metal foreign objects on the wireless power
           transmission system

    • Authors: Jiacheng Li, Yijie Huang
      Abstract: The wireless power transmission (WPT) system through magnetic field coupling for energy transmission may have foreign objects in the transmission channel in the practical application process, which brings hidden dangers to the WPT system. In this article, a WPT system without and with foreign objects is constructed. The influence of foreign objects on self-inductance and mutual-inductance of coupling coils is studied from the aspects of foreign object height, radius, transmission distance, and coil turns. Then, by constructing the circuit topology of the series structure, the influence of foreign objects on the transmission efficiency and the phase difference between voltage and current of the system is studied, and finally the influence law of metal foreign objects on the performance of the WPT system is summarized.
      PubDate: 2022-10-03T00:00:00Z
       
  • Reliability of pulse photoplethysmography sensors: Coverage using
           different setups and body locations

    • Authors: Pablo Armañac-Julián, Spyridon Kontaxis, Andrius Rapalis, Vaidotas Marozas, Pablo Laguna, Raquel Bailón, Eduardo Gil, Jesús Lázaro
      Abstract: Pulse photoplethysmography (PPG) is a simple and economical technique for obtaining cardiovascular information. In fact, PPG has become a very popular technology among wearable devices. However, the PPG signal is well-known to be very vulnerable to artifacts, and a good quality signal cannot be expected for most of the time in daily life. The percentage of time that a given measurement can be estimated (e.g., pulse rate) is denoted coverage (C), and it is highly dependent on the subject activity and on the configuration of the sensor, location, and stability of contact. This work aims to quantify the coverage of PPG sensors, using the simultaneously recorded electrocardiogram as a reference, with the PPG recorded at different places in the body and under different stress conditions. While many previous works analyzed the feasibility of PPG as a surrogate for heart rate variability analysis, there exists no previous work studying coverage to derive other cardiovascular indices. We report the coverage not only for estimating pulse rate (PR) but also for estimating pulse arrival time (PAT) and pulse amplitude variability (PAV). Three different datasets are analyzed for this purpose, consisting of a tilt-table test, an acute emotional stress test, and a heat stress test. The datasets include 19, 120, and 51 subjects, respectively, with PPG at the finger and at the forehead for the first two datasets and at the earlobe, in addition, for the latter. C ranges from 70% to 90% for estimating PR. Regarding the estimation of PAT, C ranges from 50% to 90%, and this is very dependent on the PPG sensor location, PPG quality, and the fiducial point (FP) chosen for the delineation of PPG. In fact, the delineation of the FP is critical in time for estimating derived series such as PAT due to the small dynamic range of these series. For the estimation of PAV, the C rates are between 70% and 90%. In general, lower C rates have been obtained for the PPG at the forehead. No difference in C has been observed between using PPG at the finger or at the earlobe. Then, the benefits of using either will depend on the application. However, different C rates are obtained using the same PPG signal, depending on the FP chosen for delineation. Lower C is reported when using the apex point of the PPG instead of the maximum flow velocity or the basal point, with a difference from 1% to even 10%. For further studies, each setup should first be analyzed and validated, taking the results and guidelines presented in this work into account, to study the feasibility of its recording devices with respect to each specific application.
      PubDate: 2022-09-29T00:00:00Z
       
  • Energy-efficient neural network design using memristive MAC unit

    • Authors: Shengqi Yu, Thanasin Bunnam, Sirichai Triamlumlerd, Manoch Pracha, Fei Xia, Rishad Shafik, Alex Yakovlev
      Abstract: Artificial intelligence applications implemented with neural networks require extensive arithmetic capabilities through multiply-accumulate (MAC) units. Traditional designs based on voltage-mode circuits feature complex logic chains for such purposes as carry processing. Additionally, as a separate memory block is used (e.g., in a von Neumann architecture), data movements incur on-chip communication bottlenecks. Furthermore, conventional multipliers have both operands encoded in the same physical quantity, which is either low cost to update or low cost to hold, but not both. This may be significant for low-energy edge operations. In this paper, we propose and present a mixed-signal multiply-accumulate unit design with in-memory computing to improve both latency and energy. This design is based on a single-bit multiplication cell consisting of a number of memristors and a single transistor switch (1TxM), arranged in a crossbar structure implementing the long-multiplication algorithm. The key innovation is that one of the operands is encoded in easy to update voltage and the other is encoded in non-volatile memristor conductance. This targets operations such as machine learning which feature asymmetric requirements for operand updates. Ohm’s Law and KCL take care of the multiplication in analog. When implemented as part of a NN, the MAC unit incorporates a current to digital stage to produce multi-bit voltage-mode output, in the same format as the input. The computation latency consists of memory writing and result encoding operations, with the Ohm’s Law and KCL operations contributing negligible delay. When compared with other memristor-based multipliers, the proposed work shows an order of magnitude of latency improvement in 4-bit implementations partly because of the Ohm’s Law and KCL time savings and partly because of the short writing operations for the frequently updated operand represented by voltages. In addition, the energy consumption per multiplication cycle of the proposed work is shown to improve by 74%–99% in corner cases. To investigate the usefulness of this MAC design in machine learning applications, its input/output relationships is characterized using multi-layer perceptrons to classify the well-known hand-writing digit dataset MNIST. This case study implements a quantization-aware training and includes the non-ideal effect of our MAC unit to allow the NN to learn and preserve its high accuracy. The simulation results show the NN using the proposed MAC unit yields an accuracy of 93%, which is only 1% lower than its baseline.
      PubDate: 2022-09-26T00:00:00Z
       
  • Biodegradable polymeric materials for flexible and degradable electronics

    • Authors: Zhiqiang Zhai, Xiaosong Du, Yin Long, Heng Zheng
      Abstract: Biodegradable electronics have great potential to reduce the environmental footprint of electronic devices and to avoid secondary removal of implantable health monitors and therapeutic electronics. Benefiting from the intensive innovation on biodegradable nanomaterials, current transient electronics can realize full components’ degradability. However, design of materials with tissue-comparable flexibility, desired dielectric properties, suitable biocompatibility and programmable biodegradability will always be a challenge to explore the subtle trade-offs between these parameters. In this review, we firstly discuss the general chemical structure and degradation behavior of polymeric biodegradable materials that have been widely studied for various applications. Then, specific properties of different degradable polymer materials such as biocompatibility, biodegradability, and flexibility were compared and evaluated for real-life applications. Complex biodegradable electronics and related strategies with enhanced functionality aimed for different components including substrates, insulators, conductors and semiconductors in complex biodegradable electronics are further researched and discussed. Finally, typical applications of biodegradable electronics in sensing, therapeutic drug delivery, energy storage and integrated electronic systems are highlighted. This paper critically reviews the significant progress made in the field and highlights the future prospects.
      PubDate: 2022-09-06T00:00:00Z
       
  • Maximum efficiency control and predictive-speed controller design for
           interior permanent magnet synchronous motor drive systems

    • Authors: Tian-Hua Liu, Yu-Hang Zhuang
      Abstract: Improving the efficiency of home appliances is an important area of research these days, especially for global warming and climate change. To achieve this goal, in this paper, a new method to improve the maximum efficiency control of an interior permanent magnet synchronous motor (IPMSM) drive system, which includes an IPMSM and an inverter, is investigated. By suitably controlling the d-axis current, the IPMSM drive system can quickly reach its maximum efficiency. A steepest ascent method is used to obviously reduce the searching steps of the maximum efficiency tracking control for an IPMSM. According to experimental results, by using the traditional fixed step method, 14 steps are required to reach the maximum efficiency operating point. By using the proposed steepest ascent method, however, only 4 steps are needed to reach the maximum efficiency operating point. In addition, according to the experimental results, during the transient dynamics, the predictive controller obtains faster responses and 2% lower overshoot than the PI controller. Moreover, during adding external load, the predictive controller has only a 10 r/min speed drop and 0.1 s recover time; however, the PI controller has a 40 r/min speed drop and 0.3 s recover time. Experimental results can validate theoretical analysis. Several measured results show when compared to the fix-step searching method with a PI controller, the proposed methods provide quicker searching maximum efficiency ability, quicker and better dynamic transient responses, and lower speed drop when an external load is added.
      PubDate: 2022-09-01T00:00:00Z
       
  • Quantization and sparsity-aware processing for energy-efficient NVM-based
           convolutional neural networks

    • Authors: Han Bao, Yifan Qin, Jia Chen, Ling Yang, Jiancong Li, Houji Zhou, Yi Li, Xiangshui Miao
      Abstract: Nonvolatile memory (NVM)-based convolutional neural networks (NvCNNs) have received widespread attention as a promising solution for hardware edge intelligence. However, there still exist many challenges in the resource-constrained conditions, such as the limitations of the hardware precision and cost and, especially, the large overhead of the analog-to-digital converters (ADCs). In this study, we systematically analyze the performance of NvCNNs and the hardware restrictions with quantization in both weight and activation and propose the corresponding requirements of NVM devices and peripheral circuits for multiply–accumulate (MAC) units. In addition, we put forward an in situ sparsity-aware processing method that exploits the sparsity of the network and the device array characteristics to further improve the energy efficiency of quantized NvCNNs. Our results suggest that the 4-bit-weight and 3-bit-activation (W4A3) design demonstrates the optimal compromise between the network performance and hardware overhead, achieving 98.82% accuracy for the Modified National Institute of Standards and Technology database (MNIST) classification task. Moreover, higher-precision designs will claim more restrictive requirements for hardware nonidealities including the variations of NVM devices and the nonlinearities of the converters. Moreover, the sparsity-aware processing method can obtain 79%/53% ADC energy reduction and 2.98×/1.15× energy efficiency improvement based on the W8A8/W4A3 quantization design with an array size of 128 × 128.
      PubDate: 2022-08-12T00:00:00Z
       
  • AI-PiM—Extending the RISC-V processor with Processing-in-Memory
           functional units for AI inference at the edge of IoT

    • Authors: Vaibhav Verma, Mircea R. Stan
      Abstract: The recent advances in Artificial Intelligence (AI) achieving “better-than-human” accuracy in a variety of tasks such as image classification and the game of Go have come at the cost of exponential increase in the size of artificial neural networks. This has lead to AI hardware solutions becoming severely memory-bound and scrambling to keep-up with the ever increasing “von Neumann bottleneck”. Processing-in-Memory (PiM) architectures offer an excellent solution to ease the von Neumann bottleneck by embedding compute capabilities inside the memory and reducing the data traffic between the memory and the processor. But PiM accelerators break the standard von Neumann programming model by fusing memory and compute operations together which impedes their integration in the standard computing stack. There is an urgent requirement for system-level solutions to take full advantage of PiM accelerators for end-to-end acceleration of AI applications. This article presents AI-PiM as a solution to bridge this research gap. AI-PiM proposes a hardware, ISA and software co-design methodology which allows integration of PiM accelerators in the RISC-V processor pipeline as functional execution units. AI-PiM also extends the RISC-V ISA with custom instructions which directly target the PiM functional units resulting in their tight integration with the processor. This tight integration is especially important for edge AI devices which need to process both AI and non-AI tasks on the same hardware due to area, power, size and cost constraints. AI-PiM ISA extensions expose the PiM hardware functionality to software programmers allowing efficient mapping of applications to the PiM hardware. AI-PiM adds support for custom ISA extensions to the complete software stack including compiler, assembler, linker, simulator and profiler to ensure programmability and evaluation with popular AI domain-specific languages and frameworks like TensorFlow, PyTorch, MXNet, Keras etc. AI-PiM improves the performance for vector-matrix multiplication (VMM) kernel by 17.63x and provides a mean speed-up of 2.74x for MLPerf Tiny benchmark compared to RV64IMC RISC-V baseline. AI-PiM also speeds-up MLPerf Tiny benchmark inference cycles by 2.45x (average) compared to state-of-the-art Arm Cortex-A72 processor.
      PubDate: 2022-08-11T00:00:00Z
       
  • Oscillation-Based Spectroscopy for Cell-Culture Monitorization

    • Authors: Pablo Pérez, Juan A. Serrano-Viseas, Santiago Fernández-Scagliusi, Daniel Martín-Fernández, Gloria Huertas, Alberto Yúfera
      Abstract: Biological Impedance is a physical property related to the state and inherent evolution of biological samples. Among the existing impedance measurement methods, Oscillation-Based (OB) tests are a simple and smart solution to indirectly measure impedance correlated with the amplitude and frequency of the generated oscillation which are proportional to the sample under test. An OB test requires tuning of the system blocks to specifications derived from every measurement problem. The OB setup must be done to obtain the optimum measurement sensitivity for the specific constraints imposed by the system under test, electronic interfaces, and electrodes employed for test. This work proposes the extension of OB measurement systems to spectroscopy test, enabling a completely new range of applications for this technology without the restrictions imposed by setting a fixed frequency on the electrical oscillator. Some examples will be presented to the measurement of cell cultures samples, considering the corresponding circuit interfaces and electric models for the electrode-cell system. The proposed analysis method allows the selection of the best oscillator elements for optimum sensitivity range in amplitude and frequency oscillation values, when a specific cell culture is monitored for the OB system.
      PubDate: 2022-07-22T00:00:00Z
       
  • Dual Ascent Algorithm-Based Improved Droop Control for Efficient Operation
           of AC Microgrid

    • Authors: Yajie Jiang, Yun Yang
      Abstract: In this work, the loss (including wire loss and converter loss) of island three-phase AC microgrid is modeled as a quadratic function of the current distribution coefficient, that is, a concave function with equality and inequality constraints. On the basis of the concave optimization principle, the optimal current distribution coefficient of the distributed energy unit (DEU) is calculated online by the double ascent optimization method (DAOM) to minimize the distribution loss. It is proven that the concave function with multi-variables can be optimized by the DAOM. Using the average reactive power distribution scheme, the optimal active power distribution coefficient with the minimum distribution loss of the AC microgrid can be obtained in real time. In addition, given the high R/X ratio in the short-distance AC microgrid, the active power–frequency (P-ω) droop control and reactive power–voltage amplitude (Q-E) droop control are not suitable for power distribution among DEUs. Thus, an advanced strategy comprising active power–voltage amplitude (P-E) droop control and reactive power-frequency (Q-ω) droop control is proposed to dispatch the output active powers and reactive powers of DEUs. Simulation examples are provided to verify the convexity of the proposed model and the effectiveness of the control strategy.
      PubDate: 2022-07-13T00:00:00Z
       
  • Detection of Atrial Fibrillation in Compressively Sensed Electrocardiogram
           for Remote Monitoring

    • Authors: Mohamed Abdelazez, Sreeraman Rajan, Adrian D. C. Chan
      Abstract: The objective of this paper is to develop an optimized system to detect Atrial Fibrillation (AF) in compressively sensed electrocardiogram (ECG) for long-term remote patient monitoring. A three-stage system was developed to 1) reject ECG of poor signal quality, 2) detect AF in compressively sensed ECG, and 3) detect AF in selectively reconstructed ECG. The Long-Term AF Database (LTAFDB), sampled at 128 Hz using a 12-bit ADC with a range of 20 mV, was used to validate the system. The LTAFDB had 83,315 normal and 82,435 AF rhythm 30 s ECG segments. Clean ECG from the LTAFDB was artificially contaminated with motion artifact to achieve −12 to 12 dB Signal-to-Noise Ratio (SNR) in steps of 3 dB. The contaminated ECG was compressively sensed at 50% and 75% compression ratio (CR). The system was evaluated using average precision (AP), the area under the curve (AUC) of the receiver operator characteristic curve, and the F1 score. The system was optimized to maximize the AP and minimize ECG rejection and reconstruction ratios. The optimized system for 50% CR had 0.72 AP, 0.63 AUC, and 0.58 F1 score, 0.38 rejection ratio, and 0.38 reconstruction ratio. The optimized system for 75% CR had 0.72 AP, 0.63 AUC, and 0.59 F1 score, 0.40 rejection ratio, and 0.35 reconstruction ratio. Challenges for long-term AF monitoring are the short battery life of monitors and the high false alarm rate due to artifacts. The proposed system improves the short battery life through compressive sensing while reducing false alarms (high AP) and ECG reconstruction (low reconstruction ratio).
      PubDate: 2022-07-01T00:00:00Z
       
  • A Procedural Method to Predictively Assess Power-Quality Trade-Offs of
           Circuit-Level Adaptivity in IoT Systems

    • Authors: Jaro De Roose, Martin Andraud, Marian Verhelst
      Abstract: The constant miniaturization of IoT sensor nodes requires a continuous reduction in battery sizes, leading to more stringent needs in terms of low-power operation. Over the past decades, an extremely large variety of techniques have been introduced to enable such reductions in power consumption. Many involve some form of offline reconfigurability (OfC), i.e., the ability to configure the node before deployment, or online adaptivity (OnA), i.e., the ability to also reconfigure the node during run time. Yet, the inherent design trade-offs usually lead to ad hoc OnA and OfC, which prevent assessing the varying benefits and costs each approach implies before investing in implementation on a specific node. To solve this issue, in this work, we propose a generic predictive assessment methodology that enables us to evaluate OfC and OnA globally, prior to any design. Practically, the methodology is based on optimization mathematics, to quickly and efficiently evaluate the potential benefits and costs from OnA relative to OfC. This generic methodology can, thus, determine which type of solution will consume the least amount of power, given a specific application scenario, before implementation. We applied the methodology to three adaptive IoT system studies, to demonstrate the ability of the introduced methodology, bring insights into the adaptivity mechanics, and quickly optimize the OfC–OnA adaptivity, even under scenarios with many adaptivity variables.
      PubDate: 2022-06-27T00:00:00Z
       
  • Editorial: Wearable and Implantable Electronics for the next Generation of
           Human-Machine Interactive Devices

    • Authors: Yu Wu, Shiwei Wang, Boyang Shen, Hubin Zhao, Haichang Lu, Shuo Gao
      PubDate: 2022-06-15T00:00:00Z
       
  • Fabrication of a Flexible Aqueous Textile Zinc-Ion Battery in a Single
           Fabric Layer

    • Authors: Sheng Yong, Nicholas Hillier, Stephen Beeby
      Abstract: Zinc-ion batteries (ZIB), with various manganese oxide-based cathodes, provide a promising solution for textile-based flexible energy storage devices. This paper demonstrates, for the first time, a flexible aqueous ZIB with manganese-based cathode fabricated in a single woven polyester cotton textile. The textile was functionalized with a flexible polymer membrane layer that fills the gaps between textile yarns, enabling fine control over the depth of penetration of the spray deposited manganese oxide cathode and zinc anode. This leaves an uncoated region in the textile-polymer network that acts as the battery’s separator. The textile battery cell was vacuum impregnated with the aqueous electrolyte, achieving good wettability of the electrodes with the electrolyte. Additionally, the choice of cathodic material and its influence over the electrochemical performance of the zinc ion battery was investigated with commercially available Manganese (IV) oxide and Manganese (II, III) oxide. The textile ZIB with Manganese (II, III) oxide cathode (10.9 mAh g−1 or 35.6 µA h.cm−2) achieved better performance than the textile ZIB with Manganese (IV) oxide (8.95 mAh g−1 or 24.2 µAh cm−2) at 1 mA cm−2 (0.3 A g−1). This work presents a novel all-textile battery architecture and demonstrates the capability of using manganese oxides as cathodes for a full textile-based flexible aqueous ZIB.
      PubDate: 2022-06-06T00:00:00Z
       
 
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