Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel LUT Model for FET with Three-input Terminals

In this paper, we propose a novel look-up table (LUT) model for the three-input T-channel field effect transistor (Ti-TcFET), which is implemented into a circuit simulator. Silvaco TCAD simulators are used to generate LUTs of the Ti-TcFET. Unlike the LUT model for the device with single-input terminal, the LUT model for the device with three-input terminals requires a large amount of data and a complex modeling process. We simplify LUT model by using the symmetry of the front gate and back gate of the Ti-TcFET to reduce the data size in LUT. The effectiveness of the proposed model is verified by comparing the TCAD and HSPICE simulations of Ti-TcFETs. Simulation results prove the accuracy of the proposed model. The proposed modeling method provides an idea for HSPICE simulation models of the other devices with multi-input terminals.
Tumor-Acidity Responsive Polymeric Nanoparticles for Targeting Delivery of Angiogenesis Inhibitor for Enhanced Antitumor Efficacy With Decreased Toxicity

Various nanocarriers with tumor targeting ability and improved pharmacokinetic property have been extensively utilized to reduce the toxicity of existing clinical chemotherapeutics. Herein, we showed that by encapsulating angiogenesis inhibitor anlotinib into polymeric nanoparticles, we could significantly decrease its in vivo toxicity. The introduction of pH-responsiveness into the nanocarrier further enhanced its anti-tumor activity. Systemic administration of the anlotinib-loaded nanocarrier into mice bearing A549 and 4T1 subcutaneous tumor received a higher tumor growth suppression and metastasis inhibition without detectable side effects. This strategy offers a promising option to improve the patient compliance of anlotinib.
Improved Hierarchical IR Drop Analysis in Homogeneous Circuits

IR drop analysis plays an important role in chip design. However, this analysis usually takes long time. In this work, we propose an improved hierarchical method for IR drop analysis in homogeneous chips. Experimental results show that the more speedup can be achieved with larger number of homogeneous cores. For the chip with 8 homogeneous but not identical cores, our approach can obtain 5.21X speedup in the whole analysis with 99% accuracy.
Re-Thinking Mixed-Criticality Architecture for Automotive Industry

Mixed-Criticality System (MCS) has been considered widely within academic literature, but is proving difficulty to implement in industry as the theoretical models underpinning the research do not always consider industrial safety standards and practice (e.g., DO-178C, ISO26262, and EN50128). This paper analyses and formalises the mismatches between theoretical models and industrial standards, and presents a generic industrial MCS architecture, termed as Z-MCS. Z-MCS is built upon the conventional theoretical MCS model (i.e., Adaptive Mixed-Criticality), but with additional satisfaction on the industrial safety requirements: i). run-time safety analysis, which determines preserved applications in each system mode; ii). correct partitioning and isolation of different critical elements with temporal, spatial and fault isolation. Furthermore, three implementing methods of Z-MCS are proposed, with a generic schedulability analysis for timing guarantee. Finally, we evaluate and demonstrate Z-MCS in terms of system schedulability and overheads, along with a real-world case study. In addition, this paper is the first attempt for connecting the theoretical MCS model with the industrial context.
Study of Deceleration Control using the Power Regenerative Brake to Improve the Ride Comfort in the Electric Vehicle

Ride comfort of regenerative brake in the electric vehicle is studied. Experiment device is consisted of the 3-phase synchronous motor, rotational load and the electrical double layer capacitor (EDLC) as the energy storage. The rotational load simulates the kinetic energy of the EV. The control method before improvement, large deceleration is generated when regenerative braking starts. It is caused by large current flowing into the circuit. As a result, regenerative energy is large. Thus, target value of deceleration is set. And deceleration control is studied by limitation of the current value by duty ratio control of the switching device in the circuit. And then it is a problem to be generated a large jerk when braking starts. To improve this problem, the drive motor is controlled by the speed command value given from the PC. As a result, current is suppressed and it is possible to be deceleration control on the target value.
Effect of High-K Dielectrics in Different Doping Concentrations in a Junctionless GAA Nanowire Transistor Structure

In this research, the impact of gate dielectric, doping concentration and channel length on the transfer characteristics of cylindrical gate-all-around junctionless transistor is resolved by using the CVT model approach by using TCAD Silvaco Atlas. Short channel effect parameters like Drain induced barrier lowering (DIBL), Threshold Voltage Roll-off (TVRO), Subthreshold Swing (SS), on current and off current ratio (Ion/Ioff) for the CG structure of n-channel gate-all-around JLNW transistor are analyzed. Depletion of carriers in the device layer of cylindrical gate-all-around JLNWT are more effective in comparison with tri-gate, double gate, rectangular gate-all-around JLNWT. In quintessence, the Cylindrical gate-all-around JLNW transistor exhibits better transfer characteristics with a high Ion/Ioff ratio, SS is near about to the 60mV/decade which is ideal and highly reduced DIBL. Moreover, the Cylindrical gate-all-around JLNW transistor has better control over the carriers which flow through the device layer. In short, a Cylindrical gate-all-around JLNW transistor established as a promising candidate having benefits of reduced short channel effects and low power operation at nanoscale technology.
Research on the Structure and Principle of Axial-radial Flux Switched Reluctance Rim Driven Motor

The permanent magnet rim driven propulsor (RDP) has attracted much attention in the field of ship propulsion in recent years. However, when rim driven motor (RDM) is immersed in turbid water; the surface of the PM rotor may adsorb fine ferromagnetic particles in the water, which affects the performance of motor because it is unsealed structure between its stator and rotor. In response to this problem, an axial-radial flux switched reluctance rim driven motor (AFSR-RDM) is proposed in this paper. Without permanent magnet in structure, AFSR-RDM can avoid the problem of adsorbing, and it also has the characteristics of small volume and large power density. The structure of this motor is introduced, the characteristics of the structure and magnetic circuit are analyzed, and the inductance and torque characteristics are studied in this paper.
Mining Author Identifiers for PubMed by Linking to Open Bibliographic Databases

Author identifier (ID) is essential for many downstream tasks, such as co-author network and scientist mobility analysis. As a widely used database, author ID of PubMed is not officially provided by National Institutes of Health (NIH), that restrict some identifier-based researches or systems. This study exploited three open bibliographic databases Aminer, Microsoft Academic Graph (MAG) and Semantic Scholar (S2) to associate author ID for PubMed. For this purpose, paper linking and author linking was performed in order to mine paper and author links between PubMed and these databases. Performance of author name disambiguation (AND) was evaluated on two datasets. Our findings suggested that, S2 contains full volume of PubMed regarding link completeness. With respect to correctness of author ID, S2 and MAG achieved better performance than Aminer. The best F1 score of there available identifiers is below 90%, indicate AND for large scale database remain as a difficult task and efforts are being need for further improvement. We made the final dataset publicly available for facilitating future research.
Bit-Selection Control for Energy-Efficient Handwritten Digits Recognition Hyperdimensional Computing Architecture

Hyperdimensional computing (HDC) is a braininspired computing framework that provides simple and convenient methods to perform cognitive tasks like classification. Its foundation lies in the properties of very high dimensional vectors called hypervectors (HV). HDC is a promising alternative to the conventional von-Neumann architectures, but its high-dimensional processes still contain massive bit-wise operations. Current optimizations often sacrifice accuracy for better energy-efficiency. This work finds redundant bits in the associative memory that do not contribute any information during classification. A proposed bit-selection control trims these redundant bits leading to improved throughput and energy-savings without sacrificing accuracy. For the handwritten digits recognition problem, this simple control results in a 44.62% energy reduction at the cost of 8.34% increase in area.
A study of security awareness in Dhaka city using a portable WiFi pentesting device

Wireless networks have become one of the most popular ways to accessing the Internet in Bangladesh. However, there is a limited amount of data about the vulnerability and threat they pose for its users in Bangladesh. In this paper we present a pilot study in a campus environment where we analyzed the currently deployed scenario in terms of their exposure to attacks for malicious purposes. In particular, we demonstrate that a significant number of wireless access points are exploitable. Using a custom built portable wireless penetration test device, we demonstrate how easily a wireless users can become victim of evil twin attacks. The paper contains a number of recommendations to mitigate this issues.
Resiliency Analysis of ONOS and Opendaylight SDN Controllers Against Switch and Link Failures

Video conference applications require a high quality of service (QoS) from the point of latency, jitter, and bandwidth. As the QoS parameters seriously suffer from instant congestions and network failures, resiliency mechanisms utilized at network level have a direct impact on the experienced video quality. In addition to the solutions offered under traditional networking, software defined networking (SDN) paradigm also offers various solutions to keep quality of experience (QoE) at certain levels even in instant network failure situations. Different types of SDN controllers provide different resiliency mechanisms to recover from failures. Recovery time is an important parameter to keep video conference experience at satisfactory levels as longer recovery time causes frozen streams, streaming delays and even drops in video conferencing. This study gives a comparative analysis of the recovery time on ONOS and Opendaylight SDN controllers in case of the link and switch failures.
Shape-Variation Crawler Mechanism with Four Foldable Legs for Constant Platform Posture

This paper practically tackles the design and implementation issue of maintaining a desired platform posture. As a solution approach, a novel shape-variation crawler mechanism is proposed, enabling a crawler robot to keep its desired posture while moving. More practically, the shape-variation crawler mechanism is implemented by employing individual foldable legs controlled by a toothed rack and pinion gear. The proposed crawler mechanism enables the robot to change its crawler shape according to a local surface topology. This paper details on the realization of the proposed mechanism. The effectiveness for the mechanism is evaluated through experiments after its realization.
Impact of Pre-processing on the Performance of Text-Independent Speaker Recognition System

In this paper, we explore the effects various pre-processing techniques have on the performance of a text-independent speaker recognition system. The methods used in these analysis are Spectral Subtraction Method (SSM), Spectral Subtraction Method using Oversubtraction (SSMO), Wiener Filtering and Minimum Mean-Square-Error Short-Time Spectral Amplitude Estimator (MMSE-STSA), followed by a recently proposed voice-activity-detection (VAD) algorithm. For this analysis, we extract the Mel-frequency cepstral coefficients (MFCCs) features to distinguish and verify the identity of various individuals using two different modelling methods, namely, (i) Gaussian Mixture Model (GMM) [using Expectation-Maximization (EM) algorithm] and (ii) Vector Quantization (VQ) [using Linde-Buzo-Gray algorithm], by mapping and classifying the MFCC features. The focus of this work is not to explore the latest or best performance algorithm for a speaker recognition system, but rather a robust and stable, hardware friendly, real-time system, specifically for internet-of-things (IoT) applications. For this purpose, we have manually collected data recorded by different individuals, using different devices, in different environments, from various locations in India. The results indicate that among the eight models trained using pre-processing techniques with 47 users enrolled, six of them gave a recognition accuracy of more than 95%, with the best accuracy of 97.8%.
Machine Learning Based Real-Time Monitoring of Long-Term Voltage Stability Using Voltage Stability Indices

This article presents a machine learning approach to predict the long-term voltage stability margin as represented by the Loadability Margin (LM). LM is an intuitive and easily understandable indicator of voltage stability. The unique feature of the proposed technique is the use of different Voltage Stability Indices (VSI) proposed in the literature as inputs to an ensemble of machine learning models which predict the LM. The VSIs used are carefully selected to include those based on different principles and computable using real time synchrophasor measurements. In addition, the paper presents a methodology to generate training data under different operational conditions and N-1 contingencies to train the machine learning models. The best machine learning algorithm and the categories of input VSIs are selected through a comparative study. These studies were conducted on the IEEE 14 bus system and IEEE 118 bus system and led to the selection of Random Forest Regression machine learning algorithm, and confirmed the accuracy and robustness of the proposed method. The system was implemented on real time PhasorSmart® synchrophasor application platform and validated using RTDS® real-time simulator. The impact of synchrophasor measurement errors on the proposed technique were also analyzed.
Suppression of High-Frequency DC- and AC-Side Common-Mode Currents in DC-Fed Motor Drive Systems

To reduce radiated electromagnetic interference (EMI) in a motor drive system with long power cables, it is necessary to suppress both high-frequency (HF) input- and output-side common-mode (CM) current. In a DC-fed motor drive system, due to stray capacitance between power devices and heat sink, it is difficult to suppress both HF DC- and AC-side CM current by using only the conventional AC-side passive EMI filter. In this paper, the authors investigate how to suppress both HF DC- and AC-side CM currents on a DC-fed motor drive system. To achieve the above purpose, the DC-fed motor drive system based on SiC-MOSFET is constructed and DC- and AC-side CM currents are measured and evaluated for each filter condition.
Implementing Sparse Linear Algebra Kernels on the Lucata Pathfinder-A Computer

We present the implementation of two sparse linear algebra kernels on a migratory memory-side processing architecture. The first is the Sparse Matrix-Vector (SpMV) multiplication, and the second is the Symmetric Gauss-Seidel (SymGS) method. Both were chosen as they account for the largest run time of the HPCG benchmark. We introduce the system used for the experiments, as well as its programming model and key aspects to get the most performance from it. We describe the data distribution used to allow an efficient parallelization of the algorithms, and their actual implementations. We then present hardware results and simulator traces to explain their behavior. We show an almost linear strong scaling with the code, and discuss future work and improvements.
A High Throughput Parallel Hash Table on FPGA using XOR-based Memory

Hash table is a fundamental data structure for quick search and retrieval of data. It is a key component in complex graph analytics and AI/ML applications. State-of-the-art parallel hash table implementations either make some simplifying assumptions such as supporting only a subset of hash table operations or employ optimizations that lead to performance that is highly data dependent and in the worst case can be similar to a sequential implementation. In contrast, in this work we develop a dynamic hash table that supports all the hash table queries - search, insert, delete, update, while allowing us to support $p$ parallel queries (p > 1) per clock cycle via $p$ processing engines (PEs) in the worst case i.e. the performance is data agnostic. We achieve this by implementing novel XOR based multi-ported block memories on FPGAs. Additionally, we develop a technique to optimize the memory requirement of the hash table if the ratio of search to insert/update/delete queries is known beforehand. We implement our design on state-of-the-art FPGA devices. Our design is scalable to 16 PEs and supports throughput up to 5926 MOPS. It matches the throughput of the state-of-the-art hash table design - FASTHash, which only supports search and insert operations. Comparing with the best FPGA design that supports the same set of operations, our hash table achieves up to 12.3 x speedup.
An Autonomous Eye-in-Hand Robotic System for Elevator Button Operation Based on Deep Recognition Network

Autonomous elevator button operation is an indispensable function for robot–elevator interaction, which has long been considered an intelligent solution for multifloor navigation of mobile robots. In this article, we present an autonomous robotic system with an eye-in-hand configuration to address the button operation problem. First, we develop a deep neural network for simultaneous button detection and character recognition, which provides accurate and robust perception inputs for the button operation system. Second, we present a button pose estimation algorithm with consideration of perception uncertainties, and the button pose is accurately estimated by fitting a least-uncertainty model. Based on the perception and pose estimation algorithms, a coarse-to-fine control schema is then proposed for driving the manipulator to accomplish the button operation task. Experimental results show that the proposed perception algorithm can outperform state-of-the-art methods in both recognition accuracy and running efficiency. The pose estimation and control schema also demonstrate their effectiveness in real-world button operation tasks. The data and code are available at our project webpage.
KTRussExPLORER: Exploring the Design Space of K-truss Decomposition Optimizations on GPUs

K-truss decomposition is an important method in graph analytics for finding cohesive subgraphs in a graph. Various works have accelerated k-truss decomposition on GPUs and have proposed different optimizations while doing so. The combinations of these optimizations form a large design space. However, most GPU implementations focus on a specific combination or set of combinations in this space. This paper surveys the optimizations applied to k-truss decomposition on GPUs, and presents KTRussExPLORER, a framework for exploring the design space formed by the combinations of these optimizations. Our evaluation shows that the best combination highly depends on the graph of choice, and analyses the conditions that make each optimization attractive. Some of the best combinations we find outperform previous Graph Challenge champions on many large graphs.
Proposed and Verification of Torque Equation of IPM-type Magnetic-Geared

A magnetic-geared motor (MGM) consists of a magnetic gear and a permanent magnet (PM) motor, which are shared one inner rotor. Hence, it has high torque density in comparison with conventional PM motors. A structure of interior permanent magnet (IPM) is superior as an inner rotor in terms of efficiency. However, in the IPM-type MGM, magnetic interaction occurs and the torque equation considering the interaction has not been studied. This paper deals with the torque equation of the IPM-type MGM. First, the torque equation considering the magnetic interaction is derived. Next, the parameters of the torque equation are determined by using a two-dimensional finite element method (2D-FEM). Finally, the validity of the torque equation is proved by comparing with 2D-FEM.
Stereovision Tracking System for Monitoring Loader Crane Tip Position

This paper presents the idea and experimental tests of a non-stationary stereovision system used for supervision of a loading crane. The system based on two independent gimbals, equipped with cameras, allows simultaneously to measure and track a group of individual spatial points. The four steps calibration procedure is required for each gimbal axis, camera optics, gimbals positions, and loading crane position. Modern camera systems have been widely applied in several fields of the industry allowing for accurate measurements and real-time quality control. The systems based on stereo camera models and appropriate image analysis algorithms can measure a group of spatial points, complex surfaces, small displacements, or vibrations. There are also several vision systems such as laser tracker, camera mounted on fully controlled rotating head (called gimbal), or TOF camera, which can be used to follow and measure real-time object displacements. Unfortunately, they all have significant individual limitations explained in this paper. Accuracy tests of 3D measurements including active tracking of length pattern and crane tip have been performed within a selected working area. Based on reconstructed 3D data points, different types of geometric errors have been presented. The results revealed effects of accuracy degradation on the workspace boundaries and for higher observation angles, as well as 3D data bias suggesting mechanical issues. Several improvements in calibration and gimbal construction might result in far enough accuracy for normal operations of the crane.