Miracle-dz/UniFAR-FTUs huggingface dataset:
EERA-KWS: A 163 TOPS/W Always-on Keyword Spotting Accelerator in 28nm CMOS Using Binary Weight Network and Precision Self-Adaptive Approximate Computing

This paper proposed an energy-efficient reconfigurable accelerator for keyword spotting (EERA-KWS) based on binary weight network (BWN) and fabricated in 28-nm CMOS technology. This keyword spotting system consists of two parts: the feature extraction based on melscale frequency cepstral coefficients (MFCC) and the keywords classification based on a BWN model, which is trained through the Google’s Speech Commands database and deployed on our custom. To reduce the power consumption while maintaining the system recognition accuracy, we first optimize the MFCC implementation with approximate computing techniques, including Pre-emphasis coefficient transformation, rectangular Mel filtering, Framing and FFT optimization. Then, we propose a precision self-adaptive reconfigurable accelerator with digital-analog mixed approximate computing units to process the BWN efficiently. Based on the SNR prediction of background noise and post-detection of network output confidence, the BWN accelerator data path can be dynamically and adaptively reconfigured as 4, 8, or 16 bits. For the BWN accelerator, we proposed a time-delay based addition unit to process bit-wise approximate computing for the convolution layers and fully connected layers, and a LUT based unit for the activation layers. Implemented under TSMC 28 nm HPC+ process technology, the estimated power is $77.8~\mu \text{W}~\sim ~115.9\mu \text{W}$ , the energy efficiency can achieve 163 TOPS/W, which is over $1.8\times $ better than the state-of-the-art architecture.
An Efficient Hand Gesture Recognition System Based on Deep CNN

The goal of this paper is to use a webcam to instantly track the region of interest (ROI), namely, the hand region, in the image range and identify hand gestures for home appliance control (in order to create smart homes) or human-computer interaction fields. Firstly, we use skin color detection and morphology to remove unnecessary background information from the image, and then use background subtraction to detect the ROI. Next, to avoid background influences on objects or noise affecting the ROI, we use the kernelized correlation filters (KCF) algorithm to track the detected ROI. The image size of the ROI is then resized to 100x120 and then entered into the deep convolutional neural network (CNN), in order to identify multiple hand gestures. Two deep CNN architectures are developed in this study that are modified from AlexNet and VGGNet, respectively. Then, the above process of tracking and recognition is repeated to achieve an instant effect, and the system’s execution continues until the hand leaves the camera range. Finally, the training data set can reach a recognition rate of 99.90%, and the test data set has a recognition rate of 95.61%, which represents the feasibility of the practical application.
A Novel Resistive Field Plate Superjunction like Structure Simulation Study

A novel superjunction-like ultralow Specific On-Resistance (R$_{on,sp}$) structure is proposed based on Resistive Field Plate and RESURF principle. The numerical simulation results show that the structure features R $_{on,sp}$ of $12\mathrm{m} \Omega \cdot$ cm$^{2}$ at 628V blocking voltage, even down to $3.4\mathrm{m} \Omega \cdot$ cm $^{2}$ at 5V forward bias with 603V blocking voltage. In terms of charge balance behavior, the structure features peak breakdown voltage (BV) point ranging from 611V to 642V with charge imbalance from -75% to +100%, which is more larger than popular superjunction structure.
Wideband Tunable Differential Phase Shifter With Minimized In-Band Phase Deviation Error

This letter presents a tunable differential phase shifter with wideband flat phase characteristics. The proposed structure consists of a power dividing circuit and two 3-dB hybrids. To achieve the tunable flat phase characteristics over a wide bandwidth (BW), the coupled and through ports of one hybrid is terminated with short-circuited transmission lines (TLs), whereas those of another hybrid are terminated with a TL and varactor diode. For experimental validation, the proposed structure was designed and fabricated at a center frequency of 2.5 GHz. From the measurement results, the fabricated circuit provided a tunable differential phase shift range of 89° to 193° with a maximum in-band phase error of ±8.6° over a BW of 1 GHz.
Testing Method for Composite Insulators Interface Based on Nonlinear Ultrasonic

The interface is the weakness of composite insulators. As the interface bonding fails, the uniform distribution of electric field will be affected, which accelerates the aging of composite insulators and affects the safe operation of transmission lines. Because of the stress between the sheath and the rod, the air gap at the interface is generally less than 10 $\mu \text{m}$ and there is extrusion contact in some areas. Therefore, conventional nondestructive testing technology based on refraction and reflection principle cannot effectively identify the faulty insulators in the initial stage of bonding failure. Based on the constitutive model of the interface of composite insulators, the friction force of the interface after bonding failure introduces more nonlinearity in mechanical properties than hydrogen bond under well bonding condition. The numerical results show that under the influence of the nonlinear constitutive relation, the higher harmonic components in the spectrum will increase significantly. The experimental results of plate and insulator samples confirm the validity of numerical calculation. Based on the normalized nonlinearity coefficient, the interface state of composite insulators at the initial stage of bonding failure can be effectively identified.
Sliding Mode Fault Tolerant Tracking Control for a Single-Link Flexible Joint Manipulator System

In this paper, the fault-tolerant tracking control problem for a class of single-link flexible joint manipulator (SFJM) system with uncertainty, fault, nonlinear function, and unmatched disturbance is investigated. An observer-based sliding mode control approach is designed. Concretely, first of all, the SFJM dynamic system with uncertainty, fault, and unmatched disturbance is established. Then, by transforming the system into two subsystems, a novel composite observer is proposed to estimate the fault and disturbance, respectively. Furthermore, a robust sliding mode controller, which contains a third-order sliding mode surface, a continuous control strategy, and a visual estimated fault signal, is constructed. In the control scheme, an adaptive law is also concluded to compensate for the estimation error. Finally, the proposed method is applied to the SFJM system and the simulation results illustrate the effectiveness of the proposed method.
Mobile Malware Detection: An Analysis of Deep Learning Model

Due to its widespread use, with numerous applications deployed everyday, smartphones have become an inevitable target of the malware developers. This huge number of applications renders manual inspection of codes infeasible; as such, researchers have proposed several malware detection techniques based on automatic machine learning tools. Deep learning has gained a lot of attention from the malware researchers due to its ability of capture complex relationships among inputs and outputs. However, deep learning models depend largely on several hyper-parameters (i.e., learning rate, batch size, dropout rate). Hence, it is of utmost importance to analyze the effect of these parameters on classifier performance. In this paper, we systematically studied the effect of these parameters along with the effect of network architecture. We showed that building arbitrary deep networks does not always improve classifier performance. We also determined the combination of hyperparameters that yields best result. This study will be useful in building better deep neural network based model for malware classification.
A Novel BPNN-Based Method to Overcome the GPS Outages for INS/GPS System

In order to improve the performance of the global positioning system (GPS) and inertial navigation system (INS) integrated system during GPS outages, a novel fusion algorithm based on back propagation neural network (BPNN) is proposed. A new model is built which relates the INS velocity, inertial measurement unit (IMU) outputs and the duration of GPS outages to the GPS position increment. Performance of the proposed method has been experimentally evaluated in a land vehicle navigation test. The test results show: (1) the proposed model can efficiently predict the increment of position and compensate the INS errors accumulation during GPS outage; (2) the advantage of new model on positioning accuracy becomes more obvious when the GPS observations are unavailable for a long time; (3) utilizing the current and past 2-step information as the input of BPNN model can effectively balance the computation burden and accuracy.
A Measurement Framework for Analyze The Influence of Service Quality and Website Quality on User Satisfaction (Case Study: An IT Service in Jember University)

The information system is a set tools to present information that has been managed well in order to make it easy and useful for its users. One indicator of the successful implementation of information system is how the end-user satisfaction. User satisfaction can be measured using user satisfaction methods. This study aims to develop a measurement framework to measure the user satisfaction of IT services. The measurement framework will be developed using 3 (three) basic theories such as Servqual by Parasuraman, Webqual by Barnes and Vidgen, and Information System Succes Model (ISSM) by DeLone and McLean. The model will be applied to a case study that an IT Service called Sistem Informasi Terintegrasi (SISTER) of the Jember University. This measurements using 100 respondents are students as SISTER’s users and will be tested using t testing and GAP analysis. Based on the measurement results, the variable service quality has no significant influence on user satisfaction, and another variable is website quality has a significant influence on user satisfaction. Based on GAP analysis, it’s found that the average gap value for service quality variable is -1.12, website quality is -1.00, and user satisfaction is -1.00. It means, the service quality that represents the tangible components, reliability, responsiveness, assurance, and emphaty of SISTER’s provider according the students perceptions are still not good. In the future, SISTER’s provider need to improve the quality of measurement items of indicators of reliability, responsiveness, assurance, and emphaty if they want to increase the user satisfaction of SISTER.
Analysis on Customer Satisfaction Dimensions in Peer-to-Peer Accommodation using Latent Dirichlet Allocation: A Case Study of Airbnb

Customer satisfaction becomes a key influencer for people’s habits or daily activities. One of the examples is in the decision-making process about whether they will use specific products or services. People often need other’s review or rating about what they are going to use or consume. In this research, by using customer’s online review that available from Airbnb website, we try to extract what are the most talked factors about peer-to-peer accommodation, and how customer sentiment about them. We use Latent Dirichlet Allocation (LDA) to extract that factors and conduct sentiment analysis by utilizing semantic analyzer from Google Cloud NLP. We analyze which factors that has more effect on customer satisfaction, not only in general but more specific based on customer gender and tourism destination object. The result shows that factors related to social benefit and service quality have impact on customer satisfaction, moreover different customer gender and different tourism object destination bring different sentiment among customer. We also find several factors that can be improved by the owner of the accommodation to improve customer satisfaction toward their services.
Group-Wise Itinerary Planning in Temporary Mobile Social Network

Temporary mobile social networks has been used at hotels, concerts, theme parks, and sports arenas, where people form a mobile social group for a short time with a common interest or activity. People confined to such specific places or activities are allowed to join the temporary mobile social networks using their main social network accounts (e.g., Foursquare, Facebook). Users registered for the same business/research conference may have common connections and thus may be willing to travel together in the conference city. Traveling with temporal friends can improve the mobile users’ experiences as well as help them save money. Currently, renting cars to travel around becomes very general, and one car usually can contain at least four guests. Therefore, traveling with temporal friends can help those guests save their travel cost, such as renting cost and oil cost. To this end, in this paper, we propose a group-wise itinerary planning framework to improve the mobile users’ experiences. The experiment results over real data sets illustrate the effectiveness of our proposed framework.
An adaptive protection framework for MPEG base layer video streaming over Internet

Video streaming over the Internet or wireless environment is an imperative application. However, packetized video frames suffer from random losses when they are streamed over the Internet. Packet losses degrade video quality, even if applications adopt error resilience techniques. To provide more guaranteed video quality, we re-investigate the PET system (Albanese et al., November 1996) and propose an adaptive optimization-based protection assignment framework based on PET for streaming MPEG base layer video under a given bandwidth constraint Our framework incorporates considerations of both the variant bandwidth issue and the packet loss issue on the Internet. The results of our experiments demonstrate our proposed framework achieves the goal of adaptive optimization assignment for MPEG video streaming.
Basic Research on Wire-Wound On-Line Monitoring of Vehicle Pressure Vessel

In order to ensure the safety of the vehicle pressure vessel, a safety monitoring method based on detecting the deformation of the vehicle pressure vessel has been developed, which uses the wire to wind the outer wall of the vehicle pressure vessel to detect the deformation. The preliminary law of the deformation amount is obtained by XFEM analysis, and the corresponding measurement scheme is formulated. Experimentally we studied the cylinder with axial defects inside. We Explored the influence of internal pressure on the external geometry and shape of the cylinder, and found the influence of the defect size on the alarm pressure and safety margin. The experimental results show that the safety of the vehicle pressure vessel can be effectively monitored by deformation, and the danger caused by the defect of the vehicle pressure vessel can be effectively monitored.
A survey of multi-robot regular and adversarial patrolling

Multi-robot systems can be applied to patrol a concerned environment for security purposes. According to different goals, this work reviews the existing researches in a multi-robot patrolling field from the perspectives of regular and adversarial patrolling. Regular patrolling requires robots to visit important locations as frequently as possible and a series of deterministic strategies are proposed, while adversarial one focuses on unpredictable robotsʼ moving patterns to maximize adversary detection probability. Under each category, a systematic survey is done including problem statements and modeling, patrolling objectives and evaluation criteria, and representative patrolling strategies and approaches. Existing problems and open questions are presented accordingly.
Advanced 22nm FD-SOI Technolgy With Metal Gate Last Process

In this work, we demonstrate an advanced 22 nm fully depleted silicon-on-insulator substrate (FD-SOI) process for very large scale integrated circuit (VLSI). The fabrication process features hybrid substrate, epitaxy for raised source/drain, high-k/metal gate and trench silicidation. For the first time, high-k first/metal-gate last process is applied. The metal-gate last process allows wide choice of metal materials to tune the threshold voltage of the transistor. The fabricated n-type and p-type MOSFETs show very low subthreshold swing and limited drain induced barrier lowering (DIBL) effect.
Image analysis by two types of Franklin-Fourier moments

In this paper, we first derive two types of transformed Franklin polynomial: substituted and weighted radial Franklin polynomials. Two radial orthogonal moments are proposed based on these two types of polynomials, namely substituted Franklin-Fourier moments and weighted Franklin-Fourier moments ( SFFMs and WFFMs ), which are orthogonal in polar coordinates. The radial kernel functions of SFFMs and WFFMs are transformed Franklin functions and Franklin functions are composed of a class of complete orthogonal splines function system of degree one. Therefore, it provides the possibility of avoiding calculating high order polynomials, and thus the accurate values of SFFMs and WFFMs can be obtained directly with little computational cost. Theoretical and experimental results show that Franklin functions are not well suited for constructing higher-order moments of SFFMs and WFFMs, but compared with traditional orthogonal moments ( e.g., BFMs, OFMs and ZMs ) in polar coordinates, the proposed two types of Franklin-Fourier Moments have better performance respectively in lower-order moments.
Impact of transmission topology for protective operations in multi-terminal HVDC networks

This paper presents an outcome of a comprehensive study which evaluates the transient behaviour of point-to-point and multi-terminal high voltage direct current (MT-HVDC) networks. The behaviour of the HVDC system during a permanent pole-to-pole and pole-to-ground fault is assessed considering a range of fault resistances, fault positions along the line, and operational conditions. The emphasis of this investigation is on DC fault characteristics which would facilitate a reliable method of faulty line discrimination in a multi-terminal direct current (MTDC) system using local measurements only (i.e. assuming that no communication media is used). All the simulated waveforms (and subsequent analysis) utilise the sampling frequency of 96 kHz in compliance with IEC-61869 and IEC-61850:9-2 for DC-side voltages and currents.
Application of the 2D-ERT to evaluate phantom circumscribed regions in various sucrose solution concentrations

Synthesis of the crystals is a very important step in manufacturing of many solid food products and pharmaceutical products. Electrical Resistance Tomography (ERT) provides an in-situ modality in observation of the progress in the process of batch reactor crystallization. Lowering of the conductivity in the non-ionic solutions such as sucrose solution poses challenges in the quantitative evaluation. In this work, the 2D electrical resistance tomography (2D-ERT) was implemented in laboratory-based batch reactor to observe circular, hollow and static sugar filled phantoms at various sucrose solution concentrations. The evaluations were done using a single plane of 16 surface electrode setup with voltage controlled current injection method. Modified Bayesian and iterative Gauss-Newton reconstruction methods were used to compare the phantom reconstructions qualitatively. It was observed that the 10 mm and 15 mm phantoms were discernible using both the reconstruction methods at the central location in the reactor vessel.
Area-Power-Delay Trade-Off in Ternary FPRM Circuits Based on MOTLBO Algorithm

With the development of multi-valued logic circuits, requirements of their EDA design are growing, which requires the support of high-performance algorithms. A scheme based on multi-objective teaching and learning optimization algorithm (MOTLBO) is proposed to find the optimal polarity of area, power and delay for ternary fixed polarity Reed-Muller (FPRM) logistic circuits. Firstly, the estimation model of area, power and delay for ternary FPRM circuit is established through analyzing the logic expression of ternary FPRM. Then, the optimal polarity of area, power and delay is searched by combining MOTLBO algorithm with ternary FPRM circuit polarity conversion technology. Finally, experiments on PLA format MCNC Benchmarks show that the presented method achieves competitive or better performance than existing method.
Copula & Marginal Flows: Disentangling the Marginal from its Joint

Deep generative networks such as GANs and normalizing flows flourish in the context of high-dimensional tasks such as image generation. However, so far exact modeling or extrapolation of distributional properties such as the tail asymptotics generated by a generative network is not available. In this paper, we address this issue for the first time in the deep learning literature by making two novel contributions. First, we derive upper bounds for the tails that can be expressed by a generative network and demonstrate Lp-space related properties. There we show specifically that in various situations an optimal generative network does not exist. Second, we introduce and propose copula and marginal generative flows (CM flows) which allow for an exact modeling of the tail and any prior assumption on the CDF up to an approximation of the uniform distribution. Our numerical results support the use of CM flows.
Learning a Behavioral Repertoire from Demonstrations

Imitation Learning (IL) is a machine learning approach to learn a policy from a set of demonstrations. IL can be useful to kick-start learning before applying reinforcement learning (RL) but it can also be useful on its own, e.g. to learn to imitate human players in video games. Despite the success of systems that use IL and RL, how such systems can adapt in-between game rounds is a neglected area of study but an important aspect of many strategy games. In this paper, we present a new approach called Behavioral Repertoire Imitation Learning (BRIL) that learns a repertoire of behaviors from a set of demonstrations by augmenting the state-action pairs with behavioral descriptions. The outcome of this approach is a single neural network policy conditioned on a behavior description that can be precisely modulated. We apply this approach to train a policy on 7,777 human demonstrations for the build-order planning task in StarCraft II. Dimensionality reduction is applied to construct a low-dimensional behavioral space from a high-dimensional description of the army unit composition of each human replay. The results demonstrate that the learned policy can be effectively manipulated to express distinct behaviors. Additionally, by applying the UCB1 algorithm, the policy can adapt its behavior – in-between games – to reach a performance beyond that of the traditional IL baseline approach.
Provably Robust Blackbox Optimization for Reinforcement Learning

Interest in derivative-free optimization (DFO) and "evolutionary strategies" (ES) has recently surged in the Reinforcement Learning (RL) community, with growing evidence that they can match state of the art methods for policy optimization problems in Robotics. However, it is well known that DFO methods suffer from prohibitively high sampling complexity. They can also be very sensitive to noisy rewards and stochastic dynamics. In this paper, we propose a new class of algorithms, called Robust Blackbox Optimization (RBO). Remarkably, even if up to $23\%$ of all the measurements are arbitrarily corrupted, RBO can provably recover gradients to high accuracy. RBO relies on learning gradient flows using robust regression methods to enable off-policy updates. On several MuJoCo robot control tasks, when all other RL approaches collapse in the presence of adversarial noise, RBO is able to train policies effectively. We also show that RBO can be applied to legged locomotion tasks including path tracking for quadruped robots.