Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Battery State-of-Charge Estimation Method for Electric Vehicle Battery Management System

In this paper, an adaptive battery state-of-charge (SOC) estimation method for electric vehicle (EV) battery management system (BMS) is presented. In these days, many parts of EV have been developed with electrical systems, and it makes a growth of energy storage system named battery. Therefore, to make many type of batteries safer and more reliable, BMS is employed and implemented together in EV. The BMS monitors many kinds of battery states and is responsible to manage its charging and discharging. SOC is a key parameter in judging by BMS, and therefore it is certainly important to estimate the SOC accurately. Many SOC estimation methods have been studied, and extended Kalman-filter (EKF) based methods show the best performance. However, they have high computation complexity. In this paper, adaptively combination of EKF and conventional Coulomb counting method is proposed. Finally, the proposed adaptive method shows within 2% error with 70% decreased complexity compared to EKF.
A New Technique for Underwater Acoustic Wireless Sensor Network

Medium Access Control (MAC) protocols play an important role in underwater environment communication. There is a strong need to enhance the performance of MAC protocols in Underwater Sensor Networks (UWSNs) especially improving the adequacy of ALOHA Protocols. In this paper, a Slotted-Buffering-ALOHA protocol is proposed. This protocol is based on three components: buffering, slot time, and Carrier Sense (CS). The simulation results demonstrated a superior execution of this protocol in the throughput rate, the energy consumption, and the average delay in comparison with the three protocols: slotted ALOHA, Slotted-CS ALOHA, and ST-Slotted-CS-ALOHA protocols.
Highly Integrated and High Efficiency S-Band Quasi-MMIC Power Amplifier

In this paper, a high integrated, compact and high efficiency S- band GaN quasi-MMIC power amplifier is developed. It uses two-stage amplification to achieve high gain. The driving and intermediate stage matching circuit is designed by GaAs monolithic circuit, and the output matching circuit is designed by ceramic material matching circuit. The operating conditions of the amplifier are -2.0V gate voltage, 48V drain voltage, 100* pulse width and 10% duty cycle. At 2.7-3.7GHz frequency, the saturated output power of the amplifier is more than 60W, the power added efficiency is more than 60%, and the gain is more than 26dB. The perfect combination of broadband high-power and high-performance output and miniaturization integration is realized.
A Compact Beam Steering Dielectric Resonator Antenna for Wireless Power Transfer

Design and simulation of a low profile and compact beam steering dielectric resonator antenna are discussed. A central feed primary monopole antenna is used to excite a DR, and another secondary monopole is used to achieve a unidirectional antenna. In order to have the desired direction, a fixed phase shift is added to all monopole antennas due to the physical distance. By switching between five monopole secondary antennas, a 60-degree 3-dB bandwidth pattern with the coverage of a half space is achieved.
Frequency tunable and high selective frequency selective surfaces for Ku-band

In this paper, a tunable frequency-selective surface based on liquid crystal materials in the Ku band is proposed. The frequency selective surface is based on the aperture coupling structure, and has a narrow bandwidth and high selectivity. There are two transmission zeros on both sides of the transmission passband, and the tuning range reaches 13.4%.
Analysis of decentralized control techniques for multi-generator microgrid islanded operation with traditional controllers

The islanded operating capacity is one of the most important features of a microgrid as it allows for continuous supply even when there are interruptions in the main grid. However, the transition from connected mode to islanded mode is not trivial, especially considering the interaction of controls from different generators. The present work proposes to analyze the controls usually used in distributed generators, verifying their behavior in the face of unintentional islanding events. The study focused on decentralized controls, characterized by no need for communication among generators within the microgrid boundary. Work evaluated the impact of control parameters and switching time delay on the dynamic performance of microgrid. The results showed that the potential for improving microgrid survivability is directly linked to the choice of mode of operation and control parameters of the generators.
Absorbing Structure Design of Planar Spiral Antenna for Improving Multipath Effect

A new absorbing structure based on a planar spiral antenna is designed to deal with multipath propagation in a complex urban environment. Firstly, a multipath propagation model is introduced, and then a specific size metal ring structure is designed for multipath signals, and the final wave-absorbing structure is obtained through improvement and optimization. In this paper, emphasis on the value of the phase and the root mean square error of the antenna received by multipath signals of different angles and frequencies under the absorbing structure. The simulation results verify its effectiveness.
Performance Enhancement of Corrupted Images Using Independent Component Analysis by Kurtosis for Blind Source Separation After Curvelet Denoising

The current paper proposes to remedy the problem of corrupted images using efficient digital signal processing (DSP) scheme. The proposed scheme named Curvelet denoising followed by Blind Source Separation (BSS) by Wavelet Packets Decomposition (WPD) and Kurtosis standard. The proposed scheme aims to enhance the performance of removing the noise from corrupted images. Two source images for Lena and Boat have been chosen for testing the simulations. The source images have been corrupted by sources of noise. A comparison study has been performed between the performance of the last studies and the proposed scheme. The outcomes of the results have confirmed that; of all the addressed techniques, the proposed technique is the most efficient for improving the picture quality.
FMCW-Interference of Frequency Agile OFDM Radars

Flawless operation even with a high density of radar sensors in the vicinity is an essential prerequisite for radar sensors in automotive applications, safety systems, future autonomous driving, and in industry applications. Recent research shows the great potential of digitally modulated radars such as orthogonal frequency-division multiplexing (OFDM). Furthermore, novel frequency agile OFDM approaches such as stepped and sparse OFDM are superior to standard OFDM as they are able to achieve high resolution beyond the hardware-related band limits. In order to prevent possible mutual interference issues of common and novel modulation schemes, the influence of a state-of-the-art FMCW on stepped and sparse OFDM is examined in this paper. The effects of interference are investigated theoretically and through measurements using an experimental OFDM setup at 76 GHz and scenarios with up to two FMCW interferers. To prevent signal deterioration and limited detection performance due to interference, an efficient but low-cost detection and mitigation approach is presented for each of the two approaches.
Recent Trends for (Bio)Chemical Impedance Sensor Electronic Interfaces

A large class of chemical and biological sensors have been based on electrical characterization of interfaces where sensing is often related to the characterization of interfaces between ion-based and electron-based conductive materials by means of typical electric variables such as voltage, current and charge. Integrated electronics has started a revolution in this field allowing very complex electronic systems to be shrunk to millimeter square sizes. This paper will introduce some recent trends in integrated sensor interface design, showing how progress in electronic devices and signal processing would enhance performance and applications of biochemical sensors.
Cross-Modal Hashing by lp-Norm Multiple Subgraph Combination

With the explosion of multi-modal Web data, effective and efficient techniques are in urgent need for cross-modal data retrieval with relevant semantics. Among all the possible solutions, the hashing techniques provide compact and measurable binary representation, thus gain much attention in related research domain. To better deal with diversified real world data, we propose MSC, a novel cross-modal hashing approach based on the generalized $l_{p}$ -norm Multiple Subgraph Combination. Specifically, by jointly considering the content similarity, the correspondence and other weak correlation among cross-modal documents, we build the intra-modal similarity with multiple affinity subgraphs, and encode the inter-modal correlation with a bipartite subgraph. Then these subgraphs are combined into one multi-modal similarity graph for all the data from heterogeneous modalities, where the weights of multiple intra-modal visual similarity subgraphs are regularized by $l_{p}$ -norm penalty. The optimal hash codes and the combination coefficients are learned simultaneously by efficient alternating optimization. The hash functions for different modalities are learned separately by utilizing nonlinear classification models, encoding the complicated semantic relations among cross-modal data. Experiments on challenging real world datasets demonstrate the advantage of our method over existing approaches.
Radar Waveform Coexistence: Interference Comparison on Multiple-Frame Basis

In this paper, radar-to-radar interference across different waveforms (FMCW, PMCW and OFDM) is studied. Practical implementations having similar radar parameters and application objectives are simulated to provide a fair and realistic comparison. The interference effects are presented on multiple consecutive radar frames for all possible combinations of waveforms, where the interference-induced noise floor after Range-Doppler processing are computed and compared. This paper shows that there is no perfect waveform that is robust to any interfering waveform, although OFDM radar appears to have a less fluctuating noise floor and appears to be more robust to PMCW and OFDM radar for the selected radar configurations.
A model predictive control method based on space vector modulation for MMC

In this paper, a new control method for modular multilevel converters(MMCs) is proposed. The inner-loop circulating current control is using a model predictive controller instead of a traditional PI controller. In this controlling method, the reference voltage vector is obtained firstly according to the load current, which is the primary controlling objective. Then, the equivalent number of submodules(SMs) that should be inserted in the next sampling period is determined based on the principle of minimum voltage deviation of submodules. Finally, the equivalent number is adjusted to suppress the circulating current. Switching states obtained by comparing the adjusted equivalent number with a carrier wave will be applied to the switching devices. The simulation results performed in MATLAB/Simulink software verify the effectiveness of the proposed method.
Adaptive Filter Design for Simultaneous In-band Full-duplex Communication and Radar

The in-band full-duplex (IBFD) wireless communication can achieve not only appreciable throughput gain but also is a key technology to enable concurrent radar and communication by one hardware. In such a system, an analog self-interference (SI) canceller is essential so that the received signal can be processed digitally to achieve sufficient SNR for IBFD communication. Unlike the previous similar works which rely on a classical matched filter, this paper derives the range- Doppler profile from the state of an adaptive filter, which primarily is applied to cancel the residual SI. Using standard IEEE 802.11ac signal, the proposed waveform-independent technique in this paper is simulated in a multi-target IBFD scenario. Furthermore, we employed a proof of concept setup to validate the proposed approach. The simulation and experimental results confirm that our method can render accurate range and velocity detection, and potentially satisfies a wide spectrum of opportunistic wireless sensing applications, ranging from hand/body gesture detection to tracking and localization.
Convolutional Neural Network Based Optimization Approach for Wireless Resource Management

In this paper, the feasibility of evolving advanced deep learning technology is demonstrated to solve the NP-hard transmit power control problem for future wireless networks. In the existing deep learning approach, the larger network size adversely affects the performance of the considered fully connected multi-layer perceptron (MLP) network. A Convolutional Neural Network (CNN) based strategy is proposed to develop a scalable method for solving high computation time of conventional power control algorithms. The findings reveal that the sum-rate performance of the proposed CNN outperforms existing neural network based strategy and speeds up the operation to a significant rate over the typical power control algorithm, namely Stochastic Weighted Minimum Mean Square Error (SWMMSE). The proposed scheme offers an improved solution to the optimization algorithm’s high computation time problem of radio resource allocation in real-time applications.
Large-scale farm sensing system using UAV and long-distance wireless communication

In recent years, countries where agriculture is a major industry own vast tracts of farmland, and IoT system to improve the efficiency of farming operations on the vast farmland are attracting attention. In order to collect environmental information from anyway of the vast farmland, the construction of sensor networks using LPWA that enables long-distance communication has been attracting attention. However, many obstacles such as crops and agricultural machinery on the farm makes long-distance wireless communication difficult even when using the LPWA. In addition, even when the clear line of sight from the sensor nodes is ensured, the radio waves may interfere with each other, which makes the gateway impossible to receive the sensor data correctly.Therefore, in this research, we develop a sensor network system using an Unmanned Aerial Vehicle (UAV) that can move freely in the sky. The UAV works as a gateway to collect the sensor data from the sensor nodes and move autonomously for searching the locations where they can maintain good communication property from sensor nodes dispersed over a wide area of a large farm. Furthermore, in order to improve the efficiency of the sensor data retrieval, the sensor nodes within the same area of the farm construct clusters and select one representative node in each cluster. In addition, in order to prevent radio interference caused by simultaneous data transmission, the sensor nodes are implements media access control mechanism for LPWA.
A Novel Ultra-Wideband RF Connector-Microstrip Line Transition

RF connector-microstrip line transition is frequently used in various RF circuit and system, and its character is a significant factor of RF performance. The paper proposed a novel ultra-wideband RF connector-microstrip line transition based on microwave multilayer technique, while embedded DGS is introduced to reduce the parasitic capacitor. Benefits from the compensate structure, the proposed transition working through 1 GHz-18 GHz and exhibits good RF performance. 3D EM simulations are employed on design procedure and a prototype is fabricated on 10 mil Rogers 4350 microwave laminate and 4 mil Rogers 4450F prepreg. Among the whole working band, return loss of the back to back model with 50 mm transmission line is better than 13.5 dB, and the insertion loss is better than 2.2 dB.
A Server Migration Method Using Q-Learning with Dimension Reduction in Edge Computing

Edge computing is a promising computing paradigm that satisfies QoS requirements of delay-sensitive applications. In edge computing, server migration control is indispensable for managing client mobility. As a server migration method for edge computing, the method based on Q-learning has been proposed. However, the method assumes that there is only one application client and the number of destination edge servers is limited to one. In this paper, we propose a server migration method using Q-learning that copes with realistic situations where there are multiple application clients and destination edge servers. The contributions of this paper are as follows: 1) we clarify that, under the situation with multiple application clients and multiple destination edge servers, a straightforward server migration method using Q-learning (RL method) does not scale due to state space explosion, and 2) we propose a server migration method using Q-learning (RL-DR method) that reduces the dimensionality of state space by abstracting the numbers of application clients at all locations into a center of the gravity (COG) of application clients. The simulation results show that 1) RL method shows up to 248% worse performance than conventional server migration methods because of state space explosion and 2) RL-DR method achieves up to 38.3% better performance than the conventional methods.
A Binary Protocol Fuzzing Method Based on SeqGAN

Fuzzing test is an important method to mine the security vulnerabilities of network protocol programs. Most of the existing fuzzing methods are based on protocol specifications. However, these methods will fail if the protocol specifications are unavailable. To solve the problem, some researchers proposed fuzzing methods based on deep learning, which try to use neural networks to learn protocol specifications from network data and generate test cases. However, the methods suffer from problems such as gradient disappearance during model learning. In order to solve the problems of fuzzing methods’ protocol specification dependency and gradient disappearance of fuzzing test method based on deep learning, this paper proposed a binary protocol fuzzing method based on SeqGAN, and the method consists of four stages: data preprocessing, SeqGAN training, test cases generation and fuzzing test. Firstly, the application layer messages are extracted from network datagrams. Secondly, the application layer messages are input into SeqGAN and used to train the SeqGAN model. Thirdly, the generator model in SeqGAN construct test cases. Finally, test cases are fed into the target system, and the exceptions of target system are captured. Experimental results show that the method can effectively discover the vulnerabilities of binary network protocol programs, and it has higher acceptance rate of test input and vulnerability detection ability.
A Comparative Analysis of Ensemble Based Machine Learning Techniques for Diabetes Identification

This manuscript develops a framework to deliver prediction of diabetes using the Ensemble-based Machine Learning techniques on Pima Indian Diabetes Dataset (PIDD). We investigate the performance measures of four distinct Machine Learning classifiers separately following by a soft voting classifier in order to enhance the performance metrics of Diabetes Identification. The classifications are estimated according to average classification accuracy, precision, recall, f1 score and ROC AUC of individual classifier models. LightGBM, XGBoost, AdaBoost and Random Forest classifiers are employed as distinct classifiers, and a Soft Voting classifier is further introduced to improve the robustness of performance metrics. Analyzed performance metrics results confirm that LightGBM has produced the optimum result as an individual classifier with accuracy score 94% and ROC AUC 95%, and Soft Voting classifier has additionally improved the complete structure to accuracy score 95% and ROC AUC 96%.
A Framework for Detecting the Presence of an Unattended Child in a Vehicle

Sadly, tragic accidents caused by leaving a child unattended in a vehicle continue to be reported. The need for systems to prevent such accidents has been raised, but nothing really exists. In this paper, a framework of development of detect the presence of an unattended child in a vehicle is proposed. This framework includes developments of system-on-chip (SoC) platforms, sensor networks, and smartphone applications. As a result of the proposed framework, a full system with a RISC-V core based SoC platform, a sensor network with a precise and energy efficient detection algorithm, and smartphone alarm application is presented. The functional correctness of the detection system is demonstrated with the FPGA prototyping.
Synthesis of CdSXSe1−X Nanoribbons with Uniform and Controllable Compositions via Sulfurization: Optical and Electronic Properties Studies

Ternary CdSXSe1−X single-crystal nanoribbons (NRs) with uniform and controllable compositions (0 < X < 1) were synthesized for the first time via sulfurizing the CdSe nanoribbons. The product was characterized by means of X-ray diffraction, scanning electron microscopy, high-resolution transmission electron microscopy, and energy-dispersive X-ray spectroscopy. Analysis results revealed that the CdSXSe1−X nanoribbons had a wurtzite structure and grew along the [0001] direction. Photoluminescence measurements showed the CdSXSe1−X NRs had tunable and sharp near-band gap emissions and lasing action from 542 to 668 nm. Metal oxide semiconductor field-effect transistor devices based on single CdS0.25Se0. 75 nanoribbons showed a pronounced gating effect, a threshold voltage of 4.9 V, a transconductance of 95 nS, and an on−off ratio of 105. Electron mobility and carrier concentration of the CdS0.25Se0. 75 NR were estimated to be 14.8 cm2/(V s) and 3.9 × 1016 cm−3, respectively.
Design of a fast capacitance sensing circuit for 16× 16 sized sensor array

This paper presents a CMOS circuit design of capacitance array sensing, including a capacitor to voltage conversion (CVC) circuit, 12-bit S-channel column-level singleslope ADCs, and a scanning circuit. The integrated circuit is implemented based on 0.35 $\mu$ m process. A high-speed scanning circuit is designed. ADC conversion speed is also improved so the conversion cycle is decreased. CVC and ADC adopt pipeline working mode to improve the speed of the proposed sensing circuit, which consumes 330 $\mu$ s for the whole frame (all 256 pixels). Simulation results show the capacitance detection range is 1pF- 5pF, and the accuracy can reach 1fF. ADC conversion time is 9.1 $\mu$ s at 450MHz clock frequency. The total power consumption of the 8-channel column-level single-slope ADCs is 32.81mW.
An Actuation Conflicts Management Flow for Smart IoT-based Systems

IoT-based applications have long been limited to collecting field information; at the edge of their underlying infrastructure, IoT devices utilization is mainly motivated by their capacity at gathering environmental information from sensors as means to support users in decision making. However, in numerous domains like home automation, smart factory, intelligent transportation systems, etc., so-called ‘smart’ IoT-based applications also involve devices interacting with the physical environment through actuators. Throughout their life cycle, from the design, the deployment to operation, the ability to prevent conflicting actuation commands and antagonistic effects (possibly harmful), represents a new challenge in the realm of trustworthy smart IoT-based applications. In this paper, we introduce a complete flow for identifying and resolving actuation conflicts at design time. The proposed approach is part of the DevOps software development life cycle. It advocates the reuse of conflict management solutions through local resolution strategies, considering asynchronous timings of targeted hardware platforms they are deployed on. An illustration of the flow is provided on a use-case.