Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Realization of Ultralow ON-Resistance LDMOS With Optimized Layout

To achieve lower specific ON-resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{{ \mathrm{\scriptscriptstyle ON},\text {SP}}}$ </tex-math></inline-formula>), a novel accumulation lateral double-diffused MOSFET (ALDMOS) characterized by a convex-shaped field plate (CFP) structure is proposed and investigated experimentally. The CFP structure above the <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula>-drift region consists of two diodes, with its contact regions embedding into the drain and source region. Hence, the proposed device could shrink the drift length and utilize the drift region effectively to sustain the same level of breakdown voltage in the OFF-state. Furthermore, the CFP structure enables to form a continuous electron accumulation layer at the drift surface in the ON-state. Consequently, the proposed CFP ALDMOS could obtain an ultralow <inline-formula> <tex-math notation="LaTeX">${R}_{{ \mathrm{\scriptscriptstyle ON},\text {SP}}}$ </tex-math></inline-formula> and achieve a better tradeoff relationship between BV and <inline-formula> <tex-math notation="LaTeX">${R}_{{ \mathrm{\scriptscriptstyle ON},\text {SP}}}$ </tex-math></inline-formula>. The experimental results show that the CFP ALDMOS realizes a BV of 448 V and <inline-formula> <tex-math notation="LaTeX">${R}_{{ \mathrm{\scriptscriptstyle ON},\text {SP}}}$ </tex-math></inline-formula> of 16.9 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot $ </tex-math></inline-formula>cm<sup>2</sup> with an extremely high figure of merit of 11.87 MW/cm<sup>2</sup>, decreasing <inline-formula> <tex-math notation="LaTeX">${R}_{{ \mathrm{\scriptscriptstyle ON},\text {SP}}}$ </tex-math></inline-formula> by 55.6% compared with theoretical triple-REduced SURface Field (RESURF) LDMOS at the same BV.
Reconfigurable SIW-Based Leaky-Wave Antenna Composed of Longitudinal Cells

A novel fixed frequency beam-steering leaky-wave antenna (LWA) is presented in this manuscript. Radiation occurs through thin offset longitudinal slots etched on a substrate integrated waveguide (SIW). The beam-steering is achieved by implementing GaAs varactor diodes on the backside of the antenna. Sweeping the bias voltage from 1V to 15V causes the phase constant variation, leading to the 25° beam-steering at 28.5 GHz. The key novelties of the proposed reconfigurable antenna are the low-profile and electronic beam-steering capability using a single varactor diode per cell located on the antenna’s backside. Moreover, placing the components on the backside reduces the unwanted blockage effects on the radiation. The antenna’s thickness, length, and width are 0.32 mm, 95 mm, 24 mm, respectively. The measured peak realized gain of the proposed antenna at 28.5 GHz is 9 ± 0.8 dBi. Low-profile, small gain variation, ease of fabrication, and electronic beam-steering capability make the proposed LWA suitable for the 5G beam-steering applications. A detailed sensitivity analysis along with the experimental validations were performed to prove the validity of the proposed novel design.
NFReducer: Redundant Logic Elimination for Network Functions with Runtime Configurations

Network functions (NFs) are critical components in the network data plane. Their efficiency is important to the whole network's end-to-end performance. We identify three types of runtime redundant logic in individual NF and NF chains when they are deployed with concrete configured rules. We use program analysis techniques to optimize away the redundancy where we also overcome the NF specific challenges — we combine symbolic execution and dead code elimination to eliminate unused logic, we customize the common sub-expression elimination to eliminate duplicated logic, and we add network semantics to the dead code elimination to eliminate overwritten logic. We implement a prototype named NFReducer using LLVM. Our evaluation on both legacy and platform NFs shows that after eliminating the redundant logic, the packet processing rate of the NFs can be significantly improved and the operational overhead is small.
ITE: A Structural Entropy Based Approach for Source Detection

This paper studies the problem of source detection, which is to infer the source node out of an aftermath of a cascade, i.e., the observed infected graph GN of the network at some time. Prior arts have adopted various statistical quantities such as degree, distance or infection size to reflect the structural centrality of the source. In this paper, we propose a new metric which we call the infected tree entropy (ITE), to utilize richer underlying structural features for source detection. Our idea of ITE is inspired by the conception of structural entropy [1], which demonstrated that the minimization of average bits to encode the network structures with different partitions is the principle for detecting the natural or true structures in real-world networks. Accordingly, our proposed ITE based estimator for the source tries to minimize the coding of network partitions brought by the infected tree rooted at all the potential sources, thus minimizing the structural deviation between the cascades from the potential sources and the actual infection process included in GN. On polynomially growing geometric trees, with increasing tree heterogeneity, the ITE estimator remarkably yields more reliable detection under only moderate infection sizes. In contrast, for regular expanding trees, we still observe guaranteed detection probability of ITE estimator even with an infinite infection size, thanks to the degree regularity property. We also algorithmically realize the ITE based detection that enjoys linear time complexity via a message-passing scheme, and further extend it to general graphs. Experiments on various network topologies confirm the superiority of ITE to the baselines. For example, ITE returns an accuracy of 75% ranking the source among top 5%, far exceeding 45% of the classic algorithms on scale-free networks.
Joint Dice and Intersection over Union Losses for Deep Optical Disc Segmentation

Optic disc segmentation on retinal images is essential for the diagnosis of various eye-related diseases, such as diabetic retinopathy, glaucoma, and macular edema. However, manual segmentation of optic discs from fundus images by ophthalmologists is time-consuming, tedious, and labor-extensive. In the literature, various optic disc segmentation algorithms have been proposed. In general, the existing methods are evaluated in terms of the Dice coefficient and Area of Overlap. These two metrics indicates the capability of the existing methods in preserving the accurate optic disc contour and size. However, most available segmentation methods do not attempt to reduce the two measures directly. In this paper, we present a new deep optical disc framework that can tackle the drawbacks of the methods in the literature. The proposed framework performs segmentation by taking the advantages of the U-shaped convolutional neural network (U-Net); U-Net could be trained using a limited number of data. Unlike most other methods in the literature, we use a joint dice and intersection over union losses for training the deep network. We train and evaluate the proposed framework on retinal images from the DRIVE and DRISHTI-GS datasets. In the experimental parts, the proposed framework is capable of outperforming competing methods in terms of the Dice coefficient and Area of Overlap. Therefore, our framework is suitable for broad applications of automated retinal diseases diagnosis.
3-D AND-Type Flash Memory Architecture With High-κ Gate Dielectric for High-Density Synaptic Devices

Advanced 3-D synaptic devices with a stackable AND-type rounded dual channel (RDC) flash memory structure are proposed for neuromorphic networks. AND synaptic arrays composed of RDC flash devices enable program/erase (PGM/ERS) using Fowler–Nordheim (FN) tunneling, high-speed operation because of parallel read operations, and high density with multilayer stacking. Key fabrication steps are explained and the successful operation of the device in 3-D stacked structure is verified by measurement results. In addition, current summation and selective PGM/ERS behavior in synaptic arrays, which are essential in neuromorphic networks, are demonstrated. A hardware-based convolutional neural network (CNN) is designed considering the operating characteristics of the RDC flash memory. The accuracy evaluation and analysis for the CIFAR-10 image classification are performed. In addition, we propose a method of constructing a hardware-based CNN with the high-density synaptic array by stacking layers.
Principal Component Analysis-Based Low-Light Image Enhancement Using Reflection Model

In this article, a novel low-light image enhancement (LIME) using reflection model and principal component analysis (PCA) has been proposed. The proposed algorithm works adaptively for dark images based on reflection model and multiscale principle. An input RGB color image is first stretched to correct any type of color distortion and then converted to HSV color space. By using the concept of multiscale theory, the illumination coefficient of the $V$ component is calculated. Then, an image brightness enhancement scheme is employed based on the Fechner principle, which adaptively regulates the parameters of the enhancement function. Further to this, PCA based on image fusion approach is framed to pull out the relevant features from these two images. Finally, the contrast-limited adaptive histogram equalization (CLAHE) model is applied to improve the global contrast. In comparison with other methods, the proposed method gives better outcomes in context of subjective and objective assessments.
Prison Break of Android Reflection Restriction and Defense

Java reflection technique is pervasively used in the Android system. To reduce the risk of reflection abuse, Android restricts the use of reflection at the Android Runtime (ART) to hide potentially dangerous methods/fields. We perform the first comprehensive study of the reflection restrictions and have discovered three novel approaches to bypass the reflection restrictions. Novel reflection-based attacks are also presented, including the password stealing attack. To mitigate the threats, we analyze these restriction bypassing approaches and find three techniques crucial to these approaches, i.e., double reflection, memory manipulation, and inline hook. We propose a defense mechanism that consists of classloader double checker, ART variable protector, and ART method protector, to prohibit the reflection restriction bypassing. Finally, we design and implement an automatic reflection detection framework and have discovered 5,531 reflection powered apps out of 100,000 downloaded apps, which are installed on our defense enabled Android system of a Google Pixel 2 to evaluate the effectiveness and efficiency of our defense mechanism. Extensive empirical experiment results demonstrate that our defense enabled system can accurately obstruct the malicious reflection attempts.
Theory and Experiments on Enclosing Control of Multi-Agent Systems

This paper proposes a control strategy called enclosing control. This strategy can be described as follows: the followers design their control inputs based on the state information of neighbor agents and move to specified positions. The convex hull formed by these followers contains the leaders. We use the single-integrator model to describe the dynamics of the agents and proposes a continuous-time control protocol and a sampled-data based protocol for multi-agent systems with stationary leaders with fixed network topology. Then the state differential equations are analyzed to obtain the parameter requirements for the system to achieve convergence. Moreover, the conditions achieving enclosing control are established for both protocols. A special enclosing control with no leader located on the convex hull boundary under the protocols is studied, which can effectively prevent enclosing control failures caused by errors in the system. Moreover, several simulations are proposed to validate theoretical results and compare the differences between the three control protocols. Finally, experimental results on the multi-robot platform are provided to verify the feasibility of the protocol in the physical system.
Design of a New Tukey MA-DEWMA Control Chart to Monitor Process and its Applications

Control chart is a useful statistical tool for the production process control to maintain the product value at the standard. The objectives of this research were to propose the Tukey Moving Average - Double Exponentially Weighted Moving Average control chart (MMD-TCC chart) to detect the change of mean in the symmetric and non-symmetric distribution, and to compare the effectiveness of the change detection of MMD-TCC with that of MA, DEWMA, MMD, MDM, TCC, and MDM-TCC at the different parameter levels. <inline-formula> <tex-math notation="LaTeX">$ARL_{1}$ </tex-math></inline-formula> and <italic>MRL</italic> criteria were used to measure the efficiency by applying Monte Carlo (MC) simulation. Research results indicated that the change of mean in process under the control where <inline-formula> <tex-math notation="LaTeX">$ARL_{0} =370$ </tex-math></inline-formula> with MMD-TCC was more efficient to detect the change than other control charts unless it was the symmetric distribution with the change of parameter at ±1.50, ±2.00, ±3.00, ±4.00, and the non-symmetric distributions with the change of parameter at 3.00, 4.00 with MA control chart. Furthermore, by applying the proposed control chart to real data, it was found to be in accordance with the research results from simulation technique.
An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification

An automatic system for heart arrhythmia classification can perform a substantial role in managing and treating cardiovascular diseases. In this paper, a deep learning-based multi-model system is proposed for the classification of electrocardiogram (ECG) signals. Two different deep learning bagging models are introduced to classify heartbeats into different arrhythmias types. The first model (CNN-LSTM) is based on a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) network to capture local features and temporal dynamics in the ECG data. The second model (RRHOS-LSTM) integrates some classical features, i.e. RR intervals and higher-order statistics (HOS), with LSTM model to effectively highlight abnormality heartbeats classes. We create a bagging model from the CNN-LSTM and RRHOS-LSTM networks by training each model on a different sub-sampling dataset to handle the high imbalance distribution of arrhythmias classes in the ECG data. Each model is also trained using a weighted loss function to provide high weight for not sufficiently represented classes. These models are then combined using a meta-classifier to form a strong coherent model. The meta-classifier is a feedforward fully connected neural network that takes the different predictions of bagging models as an input and combines them into a final prediction. The result of the meta-classifier is then verified by another CNN-LSTM model to decrease the false positive of the overall system. The experimental results are acquired by evaluating the proposed method on ECG data from the MIT-BIH arrhythmia database. The proposed method achieves an overall accuracy of 95.81% in the “subject-oriented” patient independent evaluation scheme. The averages of F1 score and positive predictive value are higher than all other methods by more than 3% and 8% respectively. The experimental results show the superiority of the proposed method for ECG heartbeats classification compared to many state-of-the-art methods.
A Distributed Dependency-Aware Offloading Scheme for Vehicular Edge Computing Based on Policy Gradient

The proliferation of smart transportation has significantly promoted explosive growth of the Internet of Vehicles (IoV). Especially, with the rapid development of the 5-th generation mobile networks (5G), amounts of computation-intensive and latency-sensitive vehicle applications impose heavy pressures to such limited vehicles. To address this issue, Multi-access Edge Computing (MEC) has served as a promising paradigm to facilitate those devices at the edge of wireless networks. Computation offloading is a critical technology that decides which tasks should be offloaded for minimizing the energy and time cost. However, conventional methods are inefficient to deal with dependency-aware subtask topologies from vehicles, which leads to low efficiency and waste of edge resources, especially for multi-vehicles scenarios. In this paper, we investigate the subtask topologies of IoV applications. Directed acyclic graphs (DAGs) are utilized to obtain the priority of task scheduling. Further, take privacy protection and optimal resource allocation into consideration, we put forward a distributed deep reinforcement learning (DRL) strategy based on policy gradient without information sharing in edge computing. Especially, both actor and critic network employ convolutional layer and transformer to learn the optimal mapping from the input states to the offloading decision for each subtasks. Numerical results show that, for various experimental settings, compared with the existing offloading methods, the proposed scheme achieves the superior performance.
Toward Long-Term FMG Model-Based Estimation of Applied Hand Force in Dynamic Motion During Human–Robot Interactions

Physical human-robot interaction (pHRI) is reliant on human actions and can be addressed by studying human upper-limb motions during interactions. Use of force myography (FMG) signals, which detect muscle contractions, can be useful in developing machine learning algorithms as controls. In this paper, a novel long-term calibrated FMG-based trained model is presented to estimate applied force in dynamic motion during real-time interactions between a human and a linear robot. The proposed FMG-based pHRI framework was investigated in new, unseen, real-time scenarios for the first time. Initially, a long-term reference dataset (multiple source distributions) of upper-limb FMG data was generated as five participants interacted with the robot applying force in five different dynamic motions. Ten other participants interacted with the robot in two intended motions to evaluate the out-of-distribution (OOD) target data (new, unlearned), which was different than the population data. Two practical scenarios were considered for assessment: i) a participant applied force in a new, unlearned motion (scenario 1), and ii) a new, unlearned participant applied force in an intended motion (scenario 2). In each scenario, few long-term FMG-based models were trained using a baseline dataset [reference dataset (scenario 1, 2) and/or a learnt participant dataset (scenario 1)] and a calibration dataset (collected during evaluation). Real-time evaluation showed that the proposed long-term calibrated FMG-based models (LCFMG) could achieve estimation accuracies of 80%-94% in all scenarios. These results are useful towards integrating and generalizing human activity data in a robot control scheme by avoiding extensive HRI training phase in regular applications.
Analog In-memory Computing in FeFET-based 1T1R Array for Edge AI Applications

Deep neural network (DNN) inference for edge AI requires low-power operation, which can be achieved by implementing massively parallel matrix-vector multiplications (MVM) in the analog domain on a highly resistive memory array. We propose a 1T1R compute cell (1T1R-cell) using a ferroelectric hafnium oxide-based FET (FeFET) and TiN/SiO2 tunneling junction of MΩ resistor (MOR) for analog in-memory computing (AiMC). The MOR exhibited a tunneling current behavior and MΩ resistance. A 1T1R-cell array-level evaluation was also performed. A random access for writing with low write disturbance scheme was confirmed from the summation-DC-current output, and binaries were successfully classified into “T” and “L. ” Based on the experimental results of our proposed 1T1R-cell, we obtained a state-of-the-art energy efficiency of 13700 TOPS/W including the periphery. Furthermore, we confirmed that a high inference accuracy can be obtained with our low-resistance-variability 1T1R-cell with a properly trained model.
Analysis of Downlink Connectivity in NB-IoT Networks Employing NOMA with Imperfect SIC

We study the problem of maximizing the number of served devices in a non-orthogonal multiple access (NOMA) based Narrowband Internet of Things (NB-IoT) network for supporting massive connectivity in the downlink. We analyze this problem under practical system limitations of imperfect successive interference cancellation (SIC) at the receiver along with data rate, power and bandwidth constraints. We propose a strategy for joint device and power allocation through an iterative solution for a system of linear equations on each sub-carrier that maximizes the number of connected devices. We evaluate the performance of the proposed solution over a wide range of service scenarios through extensive computer simulations and demonstrate the sensitivity of connectivity in power domain NOMA based NB-IoT systems to the residual interference resulting from imperfect SIC.
Identification of Composite Insulator Criticality Based on a New Leakage Current Diagnostic Index

In this article, a new method is proposed to identify the health condition of composite insulators through investigating the leakage current (LC) along with the electric potential (EP) and electric field stress (EFS) profiles. In this regard, variations of the LC harmonics and the distribution profiles of the EP and EFS are investigated through a series of experimental and simulation analyses conducted on composite insulators under various environmental and pollution conditions. Fast Fourier transform (FFT) and a COMSOL-multiphysics environment are utilized to analyze this variation. The results reveal a strong correlation between the LC harmonics and the insulator health condition. A new diagnostic criticality index based on the third to the fifth harmonic ratios of the insulator LC is proposed to quantify the composite insulator criticality and predict the likelihood of flashover occurrence. The EP and EFS analyses can be used to provide a complementary diagnosis of the insulator’s health state under severe pollution and humidity conditions. The proposed analysis enables the transmission line network operator to get an insight into the insulators’ functional status, thus improving the network reliability through avoiding insulator failure and adopting a proper condition-based maintenance scheme.