Miracle-dz/UniFAR-FTUs huggingface dataset:
A 55nm, 0.4V 5526-TOPS/W Compute-in-Memory Binarized CNN Accelerator for AIoT Applications

Binarized convolutional neural network (BCNN) is a promising and efficient technique toward the landscape of Artificial Intelligence of Things (AIoT) applications. In-Memory Computing (IMC) has widely been studied to accelerate the inference task of BCNN to maximize both throughput and energy efficiency. However, existing IMC circuits and architectures are only optimized for a fixed kernel size and nominal voltage operation, which poses practical limitations on optimal network architecture exploration and additional energy efficiency benefits. In this brief, we present a reconfigurable, near-threshold IMC-based BCNN accelerator design. The IMC-based accelerator architecture is scalable for different kernels sizes (<inline-formula> <tex-math notation="LaTeX">$3 \times 3 \times d$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$5 \times 5 \times d$ </tex-math></inline-formula>) and achieves high resource utilization for both cases. Moreover, the IMC bitcell is optimized for reliable near-threshold operation. Implemented in a 55-nm CMOS process, our proposed reconfigurable IMC-based BCNN accelerator achieves 5526 TOPS/W energy efficiency at 0.4V, which is <inline-formula> <tex-math notation="LaTeX">$6.38\times $ </tex-math></inline-formula> higher compared to the state-of-the-art designs. The inference accuracies of our proposed design are 97.73%, 82.56%, and 92.61% across three datasets (MNIST, CIFAR-10, and SVHN), respectively.
Application of Two-Phase Simplex Method (TPSM) for an Efficient Home Energy Management System to Reduce Peak Demand and Consumer Consumption Cost

Superabundant utilization of electricity in the residential sector is one of the major reasons for frequent peak demand. Hence, power sector necessitates an appropriate solution to control and monitor the peak demand. In this regard, implementation of an appropriate home energy management system becomes mandatory at customer premises to have an effective control over peak demand. Thus, in this research a simple home energy management using Two-Phase Simplex Method (TPSM) is proposed with an objective to (i) reduce peak demand, (ii) reduce consumer consumption cost, and (iii) conserve consumer comfort level. Further, the research proposes detailed investigations on the smart energy-home management model monitored by IoT. For simulations, different load scenarios are considered and the results are compared with the existing benchmarks available in the literature. On validations, the proposed TPSM method is found simple, reliable and efficient. More importantly, the multipurpose objectives has certainly given better results in consumer consumption cost that can give better control to peak demand. Furthermore, the usage of lucid simplex method has almost reduced the computational complexity to fasten the response time. In this regard, consumer comfort is served here is considered as a major accomplishment with the proposed work.
Content-Based Management of Human Motion Data: Survey and Challenges

Digitization of human motion using skeleton representations offers exciting possibilities for a large number of applications but, at the same time, requires innovative techniques for their effective and efficient processing. Content-based processing of skeleton data has developed rapidly in recent years, focusing mainly on specialized prototypes with limited consideration of generic data management possibilities. In this survey article, we synthesize and categorize the existing approaches and outline future research challenges brought by the increasing availability of human motion data. In particular, we first discuss the problems of suitable representation and segmentation of continuous skeleton data obtained from various sources. Then, we concentrate on comparison models for assessing the similarity of time-restricted pieces of motions, as required by any content-based management operation. Next, we review the techniques for evaluating similarity queries over collections of motion sequences and filtering query-relevant parts from continuous motion streams. Finally, we summarize the usability of existing techniques in perspective application domains and discuss the new challenges related to current technological and infrastructural developments. We especially assess the existing techniques from the perspective of scalability and propose future research directions for dealing with large and diverse volumes of skeleton data.
Effects of exogenous platelet activating factor and its antagonist gink-golide B on ischemia-reperfusion injury of myocardium in rats

AIM Exogenous platelet-activating factor(PAF) was used to investigate if it can mimic the protective effects of ischemic preconditioning (IP) as oxygen radical. METHODS Seventy-two rats were divided into six groups and all rats were treated with 30-min left anterior descending coronary artery occlusion and 3-h reperfusion as ischemic reperfusion(IR). In IR group, IR+PAF group and IR+ginkgolide B (GB) group, normal saline(NS), PAF (3 μg·kg -1 ) and PAF antagonist GB(5 mg·kg -1 ) were given twice at 25 and 10 min before prolonged ischemia, respectively; IP+IR group,IP+IR+PAF group and IP+IR+GB group received two cycles of 5-min coronary occlusion and 10-min reperfusion as IP, and NS, PAF and GB were given twice at each beginning of the brief reperfusion,respectively. The following variables were measured: cardiac function; infarct size; polymorphonuclear neutrophils (PMNs) counting; myocardial myeloperoxidase activity;terminal deoxyuridine triphosphate deoxynucleotidyltransferase nick end labeling. RESULTS ①PAF significantly aggravated heart injury, IR induced PMNs infiltration and myocyte apoptosis. GB significantly attenuated the injuries of IR except cardiac function; ②PAF significantly attenuated the protective effects of IP against heart injury, PMNs infiltration and apoptosis; ③GB had no effect on the protection of IP. CONCLUSION PMNs accumulation after IR plays an important role in the reperfusion-induced myocyte apoptosis. PAF was failed to mimic the protective effects of IP. On the contrary, it abolished the protective effects of IP.
11-Level Operation With Voltage-Balance Control of WE-Type Inverter Using Conventional and DE-SHE Techniques

This article presents an 11-level operation of the WE (Wee)-Type inverter. The topology employs a single DC-source, has a reduced number of components, and exhibits a boosting capability. A voltage balancing algorithm is proposed where the inverter’s redundant states are employed to maintain the auxiliary DC-link voltage. It is shown that with the control algorithm, the link voltage is preserved at half the primary DC-link voltage, and the 11-level operation is possible for any load. The presented 11-level WE-type structure is also compared with recent 11-level structures and has a lowest cost factor. Two modulation strategies verify the 11-level operation of the inverter: first, a modified nearest level control (MNLC) with the usage of both zero states is developed; and, second, selective harmonic elimination (SHE) is employed, where the angles are generated using a differential evolution (DE) technique. The maximum efficiency of the inverter is 97.55 %. The performance of the inverter is validated in MATLAB/Simulink and on the hardware-in-the-loop platform.
A Versatile 200-V Capacitor-Coupled Level Shifter for Fully Floating Multi-MHz Gate Drivers

This brief presents a novel level-shifter circuit for high-frequency high-voltage (HV) gate-drives. The proposed level shifter (LS) is designed based on a capacitive-coupler/current mirror/ latch structure which helps to extend operation voltage of a floating supply into the negative range, achieves sub-ns and constant delay, and consumes very low power from the floating supply. Additionally, common-mode noise cancellers based on a cross-current mirror and transmission gates are also presented to enhance the dV/dt immunity of the LS against slewing of the floating ground. Implemented in 0.18 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> HV BCD-on-SOI (bipolar-CMOS-DMOS on silicon-on-isolator) process, the post-layout simulation of the proposed design shows a delay of 680 ps, 200 V/ns of <inline-formula> <tex-math notation="LaTeX">$\text{d}{V} _{\mathrm {SSF}}$ </tex-math></inline-formula>/dt slew rate immunity, It dissipates no static power and only 8.1 pJ/transition from the floating supply, improving FoM<sup>1</sup> and FoM<sup>2</sup> of the proposed LS by 3 times and 11.7 times compared to respective state-of-the-art works.
Ego Network-based Virtual Network Embedding Scheme for Revenue Maximization

Network Virtualization (NV) technology allows multiple virtual network requests to share resources on the same subtract network. In network virtualization, Virtual Network Embedding (VNE) is one of the main techniques used to map a virtual network to the substrate network. The effectiveness and efficiency of the virtual network are determined by the performance of the embedding algorithm. Hence, an efficient embedding algorithm is required to reduce the rejection rate and embed the maximum number of virtual networks which best fit the subtract network. In this article, we propose Ego Network-based Virtual Network Embedding (EN-ViNE) algorithm which aims to improve the performance of the embedding to accept more VNRs and increase the long-term revenue. We utilize the ego-network technique to search the nearest subtract nodes for embedding virtual nodes and found the shortest path between them for link embedding. The proposed scheme attempts to minimize the rejection of virtual network requests (VNRs) that are intended to maximize the long-term revenue for the substrate network provider. Extensive computer simulation reveals that the proposed scheme considerably outperforms the existing algorithms, topology-aware, and baseline for the long-term average revenue, acceptance ratio, and revenue/cost ratio.
Adversarial Hardware With Functional and Topological Camouflage

The lately attack on the Qualcomm’s Snapdragon chip reminds us security risks related to digital arithmetic circuits. Most system designers neglect the employed computer arithmetic algorithms or their implementation details. Arithmetic circuits are therefore usually used as “black box” units that are instantiated by third-party intellectual-property core or electronic design-automation tool vendors. In this brief, we propose the first most-significant digit-first arithmetic-based hardware Trojan attack by addressing the questions of where to insert and with what. First, we demonstrate how functional camouflage is achieved. Certain arithmetic modules can be quietly replaced with functionally equivalent ones, which we term functionally camouflaged Trojans. Next, we introduce a topologically camouflaged Trojan by employing graph-centrality analysis on rare behaviours in the circuit. The proposed approach is applicable to any digital computing scenarios. Experimental results on a financial computing system demonstrate that completely inaccurate numeric results are yielded, along with an up-to 91.6% numerical error tested.
On the Design of a Fault-Tolerant Scalable Three Dimensional NoC-Based Digital Neuromorphic System With On-Chip Learning

Neuromorphic systems have shown improvements over the years, leveraging Spiking neural networks (SNN) event-driven nature to demonstrate low power consumption. As neuromorphic systems require high integration to form a functional silicon brain-like, moving to 3D integrated circuits (3D-ICs) with three-dimensional network on chip (3D-NoC) interconnect is a suitable approach that allows scalable design, shorter connections, and lower power consumption. However, highly dense neuromorphic systems also encounter the reliability issue where a single point of failure can affect the systems’operation. Because neuromorphic systems rely heavily on spike communication, an interruption or violation in the timing of spike communication can adversely affect the performance and accuracy of a neuromorphic system. This paper presents NASH,a a fault-tolerant 3D-NoC based neuromorphic system that incorporates as processing elements, lightweight spiking neuron processing cores (SNPCs) with spike-timing-dependent-plasticity (STDP) on-chip learning. Each SNPC houses 256 leaky integrate-and-fire (LIF) neurons and 65k synapses. Evaluation results on MNIST classification, using the fault-tolerant shortest-path K-means-based multicast routing algorithm (FTSP-KMCR), show that the NASH system can maintain high accuracy for up to 30% permanent fault in the interconnect with an acceptable area and power overheads when compared to other existing systems.aThis project is supported by the University of Aizu, Competitive Research Funding (CRF), Ref. UoA-P6-2020
SPB-YOLO: An Efficient Real-Time Detector For Unmanned Aerial Vehicle Images

Recently, using unmanned Aerial Vehicle(UAV) to capture images has become a popular application. However, the large scale variation and dense object distribution characteristic of UAV images brings challenges to object detection. Hence, we propose an efficient end-to-end detector named SPB-YOLO for UAV images. In this paper, firstly we design a Strip Bottleneck (SPB) module to better understand the width-height dependency by using an attention mechanism for improving the detection sensitivity of different scales’ objects in the UAV image. Secondly, we propose an upsample strategy based on Path Aggregation Network(PANet) for the feature map and add another one detection head compared to YOLOv5, which specially deal with the detection task of dense objects distribution. Finally, we execute some experiments on two public datasets, and the results show that the proposed SPBYOLO outperforms other latest UAV image detectors and makes a good trade-off between detection accuracy and speed.
A Synchronized Current Difference Updating Technique for Model-Free Predictive Current Control of PMSM Drives

Model-free predictive current control (MFPCC) is a promising substitute for model predictive current control (MPCC). However, the performance of the MFPCC, to a large extent, hinges on the update frequency of its lookup table. Conventionally, the update is only performed when two successive switching states applied by the controller are identical, causing a stagnation problem to the current difference of those switching states that are not applied. To address the stagnation problem, this paper proposes a novel mechanism called synchronized current difference update for model-free predictive current control (SCDU-MFPCC). The presented scheme uses the model of permanent-magnet synchronous motor (PMSM) to construct equivalent differential stator currents corresponding to seven basic voltage vectors. To that end, current slope will be defined from the current difference of two successively applied voltage vectors. An updating factor associated with the current slope is then introduced into the prediction scheme to correct the enforced response of all switching states. This scheme is applied on every current measurement to update the stored information regardless of the successive switching states applied are distinct or not. Finally, experiments are conducted to assess the performance of the new approach using a TMS320F28379D microcontroller. Experimental results demonstrate that the proposed method substantially reduces the stagnation effect under steady-state and dynamic operations.
Through-the-Wall Radar Imaging Based on Bayesian Compressive Sensing Exploiting Multipath and Target Structure

Compressive sensing (CS) applied to through-the-wall radar imaging (TWRI) exploits the group sparsity of a target scene in the presence of wall clutter and multipath from enclosed structures towards achieving high-resolution imaging with limited measurements. In this paper, we extend the CS-based TWRI to include the clustering structure property of the target within a hierarchical Bayesian framework. An extended structured spike-and-slab prior is imposed to statistically encourage spatially extended cluster structures of a target scene and model the signal group sparsity due to multipath propagation. The expectation propagation scheme is used for the approximate posterior inference. The proposed nonparametric Bayesian algorithm can achieve substantial improvements in terms of preserving a target cluster structure and suppressing isolated spurious false alarms compared to other state-of-the-art algorithms. Furthermore, it does not require prior information about the targets themselves, such as size, shape or number.
A 43.1TOPS/W Energy-Efficient Absolute-Difference-Accumulation Operation Computing-In-Memory With Computation Reuse

Recently, Computing-In-Memory (CIM) processors have been proposed to achieve high energy-efficiency by reducing data movement and solving memory bottlenecks. Furthermore, a network with high accurate image classification has been introduced through the Absolute-Difference-Accumulation (ADA) operation instead of the multiplication-and-accumulation operation, which is widely used in DNN. ADA operation provides not only opportunities for high energy-efficient DNN accelerating by reducing multiplication but also a chance to reuse computation results. However, the previous CIM processor cannot reuse previous computation results for other computations. In this brief, we propose a high accurate and high energy-efficient ADA-CIM processor that with two key features: 1) computation reuse for low-power, resulting in a 49.5% CIM operation power reduction, and 2) low-cost sign prediction core with 3-bit activation and weight quantization for high utilization. From the two key features, the proposed ADA-CIM processor is simulated in 28 nm CMOS technology and occupies 3.78 mm2. It consumes 2.77mW and achieves 43.1 TOPS/W energy-efficiency with a high-accuracy of 91.62% at CIFAR-10 (ResNet-20).
Interaction-Aware Probabilistic Trajectory Prediction of Cut-In Vehicles Using Gaussian Process for Proactive Control of Autonomous Vehicles

This paper presents a probabilistic trajectory prediction of cut-in vehicles exploiting the information of interacting vehicles. First, a probability distribution of behavioral parameters, which represents the characteristics of lane-change motion, is obtained via Gaussian Process Regression (GPR). For this purpose, Gaussian Process (GP) models are trained using real-world trajectories of lane-changing vehicles and adjacent vehicles. Subsequently, the future states of the lane-change vehicle are probabilistically estimated using a path-following model, which introduces virtual measurements based on the information of behavioral parameters. The proposed predictor is applied to the motion planning and control of autonomous vehicles. A Model Predictive Control (MPC) is designed to achieve predictive maneuvering of autonomous vehicles against cut-in preceding vehicles. The proposed predictor has been evaluated in terms of its prediction accuracy. Also, the performance of the proposed predictor-based control has been validated via computer simulations and autonomous driving vehicle tests. Compared to conventional prediction methods, it is shown that the interaction-aware proposed predictor provides improved prediction of cut-in vehicles’ motion in multi-vehicle scenarios. Furthermore, the control results indicate that the proposed predictor helps the autonomous vehicle to reduce the control effort and improve ride quality for passengers in cut-in scenarios, while guaranteeing safety.
Influence du conditionnement et de la déshydratation mécanique sur le séchage des boues d'épuration

Due to increasing amounts of wastewater bio-sludges produced worldwide, a lot of research has been carried out to optimize their treatment in view of reducing their high water content. Large efforts have been made on bio-sludge conditioning prior to mechanical dewatering. Research about drying is more recent but becomes more and more important in order to produce pellets that can safely be used in agriculture or that can be incinerated to recover energy. The consequences of the dewatering step especially, the nature and dosage of the polymer used for conditioning, on further drying still need to be investigated. However, such studies require making a large number of experiments at the laboratory scale, which needs to use a liquid bio-sludge with constant properties in order to get relevant results. Within this context, this article emphasizes the difficulty of making experiments with an organic material, by showing dewatering and drying results obtained during two series of tests. The experiments were conducted with bio-sludge collected after thickening in a local wastewater treatment plant. During the first test series “W,” experiments, i.e. conditioning, dewatering and drying, were performed on three samples with one week apart, using the same initial bio-sludge. For the second series “D,” trials were done each day during a working week. The Zetag 7587 conditioner was used to evaluate bio-sludge flocculation behaviors. The dewatering was made using a normalized filtration-expression cell. Drying experiments were carried out in a convective micro-dryer. The results indicate that an increase in storage duration leads to changes in bio-sludge dewatering and drying behaviors, leading to poor repeatability of experiments. Consequently, any experimental design must be completed in a limited time span to obtain relevant results. About shrinkage response which develops during drying, a two-zone linear model was developed. It was observed that the first part of the curve was almost similar for all experiments, whereas the second part reached progressively to lower volumes with increasing storage duration. In future works, the impact of other polyelectrolytes’ type and dosage at the laboratory scale on the dewatering and drying processes will be investigated more deeply.
Design and Test of Contactless Overvoltage Sensor With Spherical Six-Electrode

Most of the available overvoltage sensors are too sensitive to the installation angle and inducing direction. Even a slight angular deflection will cause a nonnegligible error to the measurement. To reduce the impact caused by angular deflection, in this article, we propose a new spherical six-electrode overvoltage sensor to improve the measuring accuracy and stability. First, we presented the 3-D electric field ( $E$ -field) coupling mechanism, vector synthesis method, design, and output properties of the proposed sensor. Second, we obtained the relationship between the structural parameters and the equivalent capacitance by the finite-element method (FEM). Finally, we completed the steady-state test, transient test, and 3-D angular deflection test. From the results, the sensor has a broad bandwidth of 1.2 MHz, a phase error of less than 1.8°, and good linearity of the output signals. In the case of 3-D angular deviations, the mean relative error (MRE) of the sensor is lower than 2.55%, with a maximum standard deviation (SD) of 0.088%, which proves that the output of the proposed sensor is well consistent with the overvoltage signal, and its accuracy is almost immune to the angular deflection.