Miracle-dz/UniFAR-FTUs huggingface dataset:
Encrypted Data Retrieval Scheme Based on Bloom Filter

Attracted by low-cost and high-quality service of cloud computing, more and more organizations and users outsource their data into the cloud server. In order to protect data privacy, the sensitive data has to be encrypted which brings great challenges to effective data utilization. In this paper, we describe our schemes for the problem of searching on encrypted data based on Bloom Filter. The proposed schemes have a number of crucial advantages: The untrusted server cannot learn anything about the plaintext when only given the ciphertext in our solution. The schemes provide query isolation for searches, meaning that the untrusted server cannot learn anything more about the plaintext than the search result. The proposed scheme not only enhances system usability, but also reduces computation in user side.
Convex Optimization Based Relay Node Placement for Wi-Fi Mesh Networks

Wi-Fi Mesh Networks (WMNs) have flexible network structure and support high-volume data traffic. A WMN can be instantly set up to provide networking service for various applications. Relay node placement is a key problem during network design. To design a WMN, it is necessary to consider both network cost and network performance. The Wi-Fi system supports diverse transmission rates in MAC layer, which makes it more challenging to select relay node positions in WMNs while ensuring network performance. In this paper, we propose a Convex Optimization based Relay Node Placement (CO-RNP) method, which formalizes relay node placement as a convex optimization problem. CO-RNP reduces relay node number by minimizing the length of non-routing links and preserves network performance by constraining the length of routing links. We evaluate the performance of networks designed by CO-RNP with network simulations in ns-3. The simulation results suggest that our method achieves effective balance between network cost and network performance.
Adaptive Step-Size Biphase Error Newton Algorithm

This paper first derives Biphase Error Algorithm (BiPhEA) using a “biphase function” and then proposes Adaptive Step-Size Biphase Error Newton Algorithm (ABENA) for adaptive filters in the complex-number domain with a Gaussian regressor. We present a stochastic model called Contaminated Gaussian Noise (CGN) for impulsive observation noise found at the filter output. To improve the filter convergence for a correlated regressor, we combine the BiPhEA with an estimate of the inverse covariance matrix of the regressor calculated using the Newton's method. We further propose a new stable Adaptive Step-Size (ASS) control algorithm. Performance analysis of the ABENA is developed for theoretically calculating transient and steady-state convergence behavior. Through experiments with typical examples, we demonstrate faster convergence and high robustness of the proposed ABENA against the CGN. Good agreement between simulated and theoretical convergence curves validates the analysis.
Continuous Cuffless Blood Pressure Estimation Based on Pulse Transit Time: A New Evaluation with Invasive BP Reference*

One of the indirect approaches to obtain blood pressure (BP) values is through the so-called pulse transit time (PTT) that is the time for the cardiac wave to propagate through a length of the arterial tree or between the heart and a peripheral arterial site. In this work, we investigated the performance of four latest cuffless BP estimation methods including the method proposed in our previous work by using the invasive BP as reference. The invasive BP data was collected from 10 patients during surgery. Experimental results showed that the estimation errors from the compared methods are much larger than the 5 ± 8 mmHg limits that is required for any BP measurement method or instrument to be satisfied the requirements of the AAMI Standard. Therefore, the PTT-based BP estimation methods need to be further improved and enhanced for obtaining acceptable performance.
A Current Optimization Strategy for Torque Ripple Reduction of Brushless DC Motor with Nonideal Back Electromotive Force

This paper presents a study of the torque component under the d-axis and q-axis of brushless DC motor with nonideal back electromotive force. Based on the improved torque harmonic expressions, the main ripple contributors can be found and are also used as the basis of designing the phase current. Then, a current optimization controller based on optimized proportional resonant control, which accounts for the influences of non-ideal back EMF and phase current lagging, is proposed to track the designed reference current in real time. Its effectiveness in minimizing low order torque harmonic component is validated and confirmed experimentally.
The Characteristics of Attention Flow of MOOC Students and Their Prediction of Academic Persistence

Through analysis of a typical MOOC click-stream data, this paper explores the attention flow characteristics of MOOC students, compares and analyzes the attention flow characteristics of students with different performances, and proposes a real-time prediction model of academic persistence based on marked attention flow temporal data by using Recursive Neural Network combined with LSMT algorithm. After testing, the predictive model achieved the accuracy of 0.84. This paper provides an insight into MOOC's curriculum design and real-time learning monitoring and early warning based on click-stream data.
Design of a Smart Socket Functioned with Electrical Appliance Identification

In order to solve a series of problems of the existing smart socket using intrusive load monitoring method, such as high cost and complicated manufacturing process, this paper applies non-intrusive load monitoring method to the smart socket and proposes a design of a smart socket functioned with electrical appliance identification. Based on two load identification algorithms, namely, relative Euclidean distance algorithm and vector distance algorithm, the smart socket has the ability to learn the electricity parameters of various electrical appliances in the electrical characteristics sample database. Then it can automatically identify the types of the electrical appliances plugged into the smart socket and provide users with electricity information of the electrical appliances, such as voltage, current, active power, power factor, frequency and cumulative power consumption.
Modified HTRCI with Variable Pulse Generation for Pilot De-contamination

Massive Multiple-Input-Multiple-Output (MIMO) systems, where the base station (BS) is equipped with hundred antennas, is expected to be one of the key technologies for the next generation wireless systems. Pilot contamination is one of performance degradation factors for massive MIMO. This paper aims to eliminate this pilot contamination impact. High time resolution carrier interferometry (HTRCI) was previously proposed which can perform highly accurate channel estimation with reducing the number of pilot signals. The original HTRCI generated the impulses in 1/4 equal interval. With regarding the current OFDM system design, more fine and variable impulse allocation is available, e.g. the guard interval (GI) length of Long-Term-Evolution-Advanced (LTE-A) is 1/8 or 1/16 of the OFDM symbol duration. This paper proposes the modified HTRCI with variable pulse generation at 1/8 interval which can estimate channel state information (CSI) of the 4 access per received antenna. This paper expands the concept of HTRCI to estimate more channels and these CSIs can be separated by time-domain signal processing in the same manner. It is applied to the pilot de-contamination on downlink multi-cell MIMO transmission. Computer simulation results confirm that the BER performance can be improved as compared with the conventional method.
Reflections on the Cultivation System of Innovative Talents in Universities in the era of Artificial Intelligence

Aiming at the problems of overcoming the deep dilemma and changing trend brought by the coming of intelligent age to the cultivation of innovative talents, this paper proposes an innovative education framework system under the artificial intelligence environment and an innovative teaching system of human-computer cooperation in colleges. These systems take the cultivation of innovative talents in the modernization of education as an important goal, analyze the cultivation of innovative personality, the generation of creative thinking, the improvement of innovative ability and the construction of innovative knowledge of college students. These factors are based on the idea that the cultivation of innovative talents in colleges and universities should turn to the construction of cultivating students' innovative subject knowledge with the help of the ability of intelligent equipment. Finally, the function and role of teachers in the stage of wisdom education are proposed. The results show that the teaching systems improve the teaching effect in college training practice.
Finite Frequency Fault Diagnosis for Heterogeneous Multi-agent LPV Systems

This paper addresses the problem of distributed fault detection and isolation (FDI) observer design in finite frequency domain for a class of heterogeneous multi-agent linear parameter-varying (LPV) systems. For this purpose, each agent uses an unknown input observer (UIO) as the FDI module which generates the residual signal. The proposed distributed FDI problem is formulated as a multi-objective optimization problem based on low frequency $\mathcal{H}_{\infty}$ and $\mathcal{H}_{-}$ performance indices to respectively measure disturbance robustness and fault sensitivity. Sufficient conditions for designing the FDI observers are obtained based on linear matrix inequality (LMI) conditions. It is shown that based on the constructed residual signals each agent is capable of detecting and isolating its own faults and also its neighbors’ faults. Finally, a numerical example is provided to demonstrate the applicability of the proposed design framework and its advantages over other approaches.
Design of a Wireless Power Transfer System Based on Dual-Class E Self-Resonant Synchronous Rectifier

Wireless Power Transfer technology operating at megahertz is widely considered to be promising, which is used for the medium distances at a medium power. It is known that the Class E inventor and rectifier are suitable for high frequency Power converter. This paper derives and analyzes a Class E rectifier, then reports a Dual-Class E self-resonant synchronous rectifier used for parameter design of a MHz WPT system. The input impedance is derived from the analysis of the class E rectifier. The parameters of the coupled coil and BPF filter are designed and optimized in an example 2.5-MHz WPT system based on the input impedance. In a validation experiment, the experimental results are in good agreement with the analytical results and simulation results. In the experiment, the 2.5-MHz WPT system where mutual inductance coefficient (k=0.144) can achieve up to 80% efficiency at 20 W.
An Eigen-based Matrix Inverse Approximation Scheme with Stair Matrix Splitting for Massive MIMO Systems

Approximative matrix inversion (MIA) has been extensively researched with the aim of addressing the complexity issues encountered by Zero-forcing (ZF) precoder design in massive multiple-input multiple-output (MIMO) systems. The eigen-based MIA scheme with diagonal matrix splitting is presented recently where additional coefficients can be designed and optimized to improve MIA accuracy and bit-error-rate (BER) compared to the well-known Neumann series expansion (NS) or Newton iteration (NI) framework for Rayleigh flat fading channel, regardless the presence of transmit antenna correlation or not. In this paper, stair matrix splitting is proposed for the eigen-based MIA method for uncorrelated Rayleigh flat fading channel. Simulation result shows that, with additional information in the inverse of stair matrix, both the ZF precoder approximation precision and BER can be enhanced compared to the diagonal matrix splitting scenarios without sacrificing complexity.
Accelerating Stochastic Gradient Descent using Adaptive Mini-Batch Size

Training Artificial Neural Networks takes a long time to converge and achieve acceptable accuracy. The proposed method alternates between two modes: fast-forward mode and normal mode. The fast-forward mode iterates faster than normal mode by using a smaller number of samples in each mini-batch. Cycling between those two modes in an adaptive way is driven by accuracy change, by selectively using faster mode as long as it gives good results. Otherwise, it falls back to normal mode. This way training becomes feasible even on commodity CPUs. Our approach was tested on commodity CPU on Pets-37 dataset obtaining an accuracy of 91% in less than an hour and on the Birds-200 dataset obtaining an accuracy of 72% in less two and a half hours.
Impedance Modeling of Virtual Synchronous Control of Doubly-Fed Wind Power System Under Weak Grid Condition

Stability problem on doubly fed induction generator (DFIG) based wind generation systems could be obvious under the connected weak grid, though the virtual synchronous control (VSC) based DFIG could provide frequency support for the weak grid and improve the stability of the grid connection, the stability analysis is insufficient. Aiming at the grid-connected stability analysis of the VSC based DFIG, this paper proposed the output impedance modeling in dq rotating coordinate system and analyzed the stability effects. Firstly, the impedance model of DFIG with the VSC is derived by using the small disturbance analysis method. Secondly, the output impedance characteristics are obtained and validated by model-based frequency scanning method. Finally, the influence of different grid strengths and the VSC parameters on the stability is analyzed by using the generalized Nyquist criterion (GNC). The results show the derived output impedance models are valid, and the stability of DFIG using the VSC could be effected by the virtual inertia time constant and virtual damping coefficient.
Immunity Evaluation of SRAM Chips for SOI and SI Technology

This paper mainly studies the electromagnetic immunity of two kinds of static random access memory (SRAM) chips using bulk silicon (SI) and insulator silicon (SOI) technologies respectively, and the effect of temperature on chip immunity. Direct power injection (DPI) method was used to test the immunity of each functional pin of the two chips, and the ground pin was selected to study the immunity under heat stress. The test results show that the immunity of each functional pin is different, the immunity of the same pin is also different under different working states, and the SOI technologies has better immunity than the SI technologies. At the same time, it was found that different temperatures would cause the chip’s immunity threshold to drift. This provides an important reference for the design of the chip immunity.
Optimal Efficiency Current Trajectory Control of Permanent Magnet Synchronous Motor Considering Cross Coupling and Magnetic Saturation

This paper proposes a current trajectory control strategy to achieve maximum efficiency control while considering cross coupling and magnetic saturation effect. The controllable electrical loss can be minimized by the optimal control of the armature current vector which take cross coupling and magnetic saturation effect into account. The proposed method obtains the stator current amplitude which leads to optimal efficiency base on the loss model which considering cross coupling and magnetic saturation. Then the optimal efficiency control of interior permanent magnet synchronous machines (IPMSM) is realized by using the optimal stator current and current incremental vector adjustment module which calculate the d-q axis current reference by calculating the current increment. Two control strategies based on optimal efficiency current trajectory control (OECTC) and maximum torque per ampere (MTPA) are analyzed. The results of the simulation show that the proposed control strategy has well static and dynamic performance, and the efficiency is improved in the entire operating region of the machine.
Kinetic Energy based Rotor Speed Control for the PMSG-WECS Considering the losses

Based on the system kinetic energy, a rotor speed control considering system losses is proposed for the permanent magnet synchronous generator wind energy conversion system (PMSG-WECS). The sub-optimal power point of the PMSG is tracked to output the relative smoother power by comparing with the maximum power point tracking (MPPT) control. Meanwhile, the output power efficiency is guaranteed. The model of the WECS including the wind power generator and back-to-back converter considering system losses is built based on the Matlab/Simulink. The proposed kinetic based rotor speed control strategy is validated by the simulation results.
Review of DC Circuit Breaker Technology for HVDC Application

With the development of HVDC transmission system, the research of direct current circuit breaker (DCCB) has drawn the attention of many scholars. This paper summarizes the new development of DC circuit breakers in recent years, including the auxiliary circuit and control method in DCCB. In addition, this paper introduces the DCCB with cascade module, device combination and test circuit for DC circuit breaker. The topology principle, advantages and main problems in high voltage direct current transmission (HVDC) application are discussed. The possible future development direction and related problems of DC circuit breaker are put forward.
Prevalence Of Hepatitis Delta Virus Superinfection In chr onic HBV infection

Summary Background: Viral hepatitis is a common cause of morbidity and mortality all over the word. HDV is another cause of severe fulminate hepatitis and chronic liver disease. Objective: This is a prospective study aimed at finding the prevalence of HDV superinfection based on positive anti-HDV IgM type in patients with chronic liver disease and carriers of HBV infection. Patients and Methods: 72 patients with C.L. D and 36 patients who were asymptomatic HBV infection were screened for HDV superinfection by ELIZA IgM Ami- HDV Antibody. Results: In this study we found that the prevalence of HDV superinfection is 8.3% for carriers which is comparable to that of some mediterranean countries and for chronic liver disease 20.83%. We also found that there is a significant relationship of HDV prevalence with age, history of blood transfusion and severity of chronic liver disease. Conclusion: HDV infection is not uncommon in Iraq. For better evaluation, asymptomatic HBV carriers and patients with HBV C.L. D should be screened for HDV infection.
A Position Sensorless Strategy for Surface-mounted Permanent-magnet Synchronous Motors Based on Asymmetric Inductance

A novel surface-mounted permanent-magnet synchronous motors (SPMSMS) for the sensorless control strategy is proposed in this paper. The proposed topology is detecting asymmetric inductance of motor to identify the rotor position and can be derived in the range of high speed and low speed with the high and reliable commutation accuracy. Another attractive feature is that different from the traditional sensorless control strategy, this strategy can detect the information of the rotor position in the whole mechanical cycle. In the end, the performance has been verified via simulation.
Design of Vivaldi Antenna Array with a Back Reflector for Low Side Lobe Level and High Gain

This paper presents a proposal of new 10×1 microstrip Vivaldi antenna array with a low side lobe level and high gain. To obtain low side lobe level, tapering amplitude technique has been applied. The amplitude distribution has been obtained by using the Bat algorithm and has been implemented through a series feeding network. 10 Vivaldi antenna elements and the feeding network have been constructed co-planar on Roger RO4003C with the size of 140 × 450 × 1.524 mm3. To improve the performance of the array, a back reflector has been proposed and investigated. The simulation result shows that the side lobe level has been suppressed up to 28 dB from the main lobe at the frequency of 3.5 GHz while the maximum gain is around 16.5 dBi; Both simulated and measured reflection coefficients indicate that the array has an operating frequency range of 3.44 – 3.58 GHz (bandwidth 140 MHz) with S1,1 =-10 dB. The proposed array can be a good candidate for fixed wireless access systems, 5G wireless systems in the C band.
Cascaded Layered Recurrent Neural Network for Indoor Localization in Wireless Sensor Networks

The growth in using various smart wireless devices in the last few decades has given rise to indoor localization service (ILS). Indoor localization is defined as the process of locating a user location in an indoor environment. Indoor device localization has been widely studied due to its popular applications in public settlement planning, health care zones, disaster management, the implementation of location-based services (LBS) and the Internet of Things (IoT). The ILS problem can be formulated as a learning problem utilizing Wi-Fi technology. The measured Wi-Fi signal strength can be used as an indication of the distribution of users in a various indoor location. Developing a classification model with high accuracy can be achieved using a machine learning approach. Artificial Neural Network is one of the most successful trends in machine learning. In this article, we provide our initial idea of using Cascaded Layered Recurrent Neural Network (L-RNN) for the classification of user localization in an indoor environment. Several neural network models were trained, with the best performance attainment is reported. The experimental results marked that the presented L-RNN model is highly accurate for indoor localization and can be utilized for many applications.
Continuous wave laser excitation based portable optoacoustic imaging system for melanoma detection

Optoacoustic imaging has been demonstrated successfully in detecting cutaneous melanoma in its early stage. In this work, we develop a handheld optoacoustic based portable medical imaging system for early melanoma diagnosis. In the portable optoacoustic system, laser excitation is provided by a compact continuous wave laser diode, which is driven by an FPGA controlled Howland circuit driver board. Optoacoustic detection is performed using a 2D square ultrasound transducer array and a portable multichannel data acquisition system. To pursue the optimized imaging SNR and resolution, chirp modulated laser excitation and matched filtering based acoustic pulse compression are further incorporated. To evaluate the feasibility of imaging melanoma, multisite 3D imaging on a human breast phantom is handheld performed. The prospect of using low-cost continuous wave laser diode and matrix array ultrasound transducers for volumetric optoacoustic imaging would be a real breakthrough towards commercialization of the optoacoustic technique.
Combining Deep Neural Networks and Beamforming for Real-Time Multi-Channel Speech Enhancement using a Wireless Acoustic Sensor Network

This work presents a multi-channel speech enhancement algorithm using a neural network combined with beamforming deployed realtime on a wireless acoustic sensor network (WASN) of distributed microphones. We combine spectral mask estimation via a deep neural network together with spatial filtering to obtain a robust speech enhancement system even in difficult real-world scenarios (e.g. speech in noise, reverberant environments). Although the model is trained on simulated data, it performs comparably well on real-world tasks relative to an ideal oracle beamformer. We show that the model can be deployed on a WASN platform that allows for remote placement of microphones and on-board computing. We consider models with a small parameter count and low computational complexity. It achieves signal-to-distortion ratio (SDR) improvements of up to 10dB in a real-world scenario and runs real-time on-board the WASN, with a latency in the order of hundreds of milliseconds.