Miracle-dz/UniFAR-FTUs huggingface dataset:
Inner Supply Data Transmission in Quasi-Resonant Flyback Converters for Li-Ion Battery Applications Using Multiplexing Mode

This paper proposes data transmission method between primary and secondary of the flyback converter without additional communication circuit while simultaneously transferring power. In some application such as a battery charger, the data exchanges between the primary and secondary sides are necessary. In the conventional system, an additional line or wireless communication modules is used for data exchanges, thereby increasing the system and connector size. The proposed system, in comparison, does not use additional signal transceiver but instead exchanges data by simply alternating operation mode of the flyback converter, thus adding communication function while not increasing the volume of the terminal and overall system. The waveform of transformer voltage is used to count the number of resonant pulses, which is used for decoding and encoding the data packet. Bidirectional communication between primary and secondary sides is possible while power is transferred to the output using an appropriate communication protocol. This paper proposes data transmission method for both single output and the multioutput cases. Also, both half-duplex and full-duplex communication using the proposed method is explained. The experimental results are presented to verify the performance of the proposed communication method.
Investigation of Various Rotor Retaining Sleeve Structures in High-Speed PM Brushless Motors

A rotor retaining sleeve is normally a must in high-speed permanent magnet brushless motors to firmly retain the magnets at their positions and ensure the rotor integrity. The rotor retaining sleeve can be of different materials and shapes as long as it can withstand the high centrifugal forces exerted on the magnets. On the other hand, it is practically very difficult to remove the heat from the rotor in these motors. As the inevitable rotor temperature rise results in severe performance deterioration and even irreversible demagnetization, the overall rotor loss, especially the eddy current loss, is of paramount importance for these high-speed permanent magnet brushless motors. In this paper, the influences of retaining sleeve configurations, including laminated sleeve, circumferentially grooved sleeve, copper shielded sleeve, and irregular-shaped laminated sleeve, on the rotor mechanical stress and eddy current loss in a proposed high-speed permanent magnet brushless motor have been comprehensively investigated. The conductivity of the retaining sleeve and the permanent magnet has a huge impact on the rotor eddy current loss and the loss versus equivalent conductivity curve approximates to a Gaussian distribution. The study reveals the proposed laminated sleeve configuration can not only reduce the rotor eddy current loss effectively but also provide a robust rigid magnet rotor construction.
Hybrid Beamforming for Massive MIMO: A Unified Solution for Both Phase Shifter and Switch Networks

There have been extensive researches on hybrid beamforming for massive MIMO to achieve huge spectral efficiency with reasonable cost and power consumption. In this paper, we propose a unified solution to maximizing the spectral efficiency given the number of radio frequency (RF) chains. Compared with the existing work, our method is novel in that it can deal with different RF networks, including networks of switches, variable phase shifters with different resolutions, and shifterplus-switches. Given the analog beamformer, we then obtain the digital beamformer using the standard water-filling method. The simulation verifies that the hybrid beamforming achieved by our algorithm can find a solution very close to the fully digital beamforming (within a small fraction of dB). It also shows that the switch network-based hybrid beamforming is about 4dB away from the ideal phase shifter-based one.
Text to Image Translation using Generative Adversarial Networks

The learning process becomes easier when one can visualize the things being spoken about or being described. To help a person visualize, the description in the form of text which the person gives can be translated to a set of images, this is achieved by a Generative-Adversarial Model. A novel implementation for translating description to images using Generative Adversarial networks is proposed in this paper. We propose a RNN-CNN text encoding along with the Generator and Discriminator network to take the text description of flowers as the input and the resultant output would be a set of unique images generated which match the description for the same. The dataset primarily used is the Oxford 102flowers dataset along with its captions procured from the Oxford University website. It has 102 categories of flowers with each category consisting of a minimum of 40 images.
Demagnetization Analysis of Interior Permanent Magnet Machines Under Integrated Charging Operation

Integrated charging, multiplexing the motor windings for battery charging in electric vehicles, can improve the onboard fast charging capability without increasing the weight and cost. However, during the charging operation, the magnets in the traction motor have a risk of irreversible demagnetization caused by the charging currents. This paper investigates the demagnetization effects of charging current on magnets in interior permanent magnet machines. By using numerical analysis and circuit-coupled simulation, the demagnetization effects during integrated charging are evaluated.
FPGA Implementation of Energy Efficient Approximate Multiplier with Image Processing Applications

Multiplication is the pivotal building squares in image processing, DSP applications. To make high-speed here using an approximate multiplier this is an efficient one. For that the method is to rounding the values of numbers to the nearest power of two. Using this method the complicated part of multiplication can be removed thus can improve the speed of multiplication. This multiplier is applicable for both positive and negative number. The efficiency of this multiplier is analysed by comparing with the accurate multipliers and finally studied its efficiency in image processing applications like image smoothening and sharpening. The multiplier operation is finally implemented in FPGA SPARTAN kit.
Control Strategy of Dual Current Source Inverters for High Power Application Under Unbalanced Grid

A dual current source inverters (CSIs) topology with low switching frequency for high power application is proposed in this paper. On the basis of the instantaneous mathematical model of the grid current and reactive power under unbalanced grid fault, this paper proposes a novel control strategy which adopts the PI controllers in the double-sequence synchronous rotating frame. Compared with the conventional control methods, the proposed control method can eliminate the negative-sequence grid current and realize unity power factor operation without decoupling control. Besides, the proposed control strategy can be performed at a low switching frequency. A grid-tied dual CSIs system is built in PSCAD/EMTDC. The results validate the correctness and effectiveness of the proposed control strategy under unbalanced grid fault.
Deep Reinforcement Learning for Collision Avoidance of Robotic Manipulators

In this paper a real-time collision avoidance approach using machine learning is presented for safe humanrobot coexistence. More specifically, the collision avoidance problem is tackled with Deep Reinforcement Learning (DRL) techniques, applied to robot manipulators with a workspace invaded by unpredictable obstacles. Since the robotic systems are defined in the continuous space, a Normalized Advantage Function (NAF) model-free algorithm has been used. In order to assess the proposal, a robotic system, that is a COMAUSMART3-S2 anthropomorphic robot manipulator, has been considered. The robotic system has been interfaced with external tools for evaluation, control, and automatic training. Simulations carried out on a virtual environment are finally reported to show the effectiveness of the proposed model-free deep reinforcement learning algorithm.
Comparative Evaluation of Isolated Bidirectional DC/DC Converter in High-Power High-Frequency Occasions

The Isolated Bi-directional DC/DC Converter (IBDC) is a key equipment in Solid State Transformer (SST) and Electric Energy Router (EER) for power distribution networks. Topologies namely DAB, LLC and CLLC are chosen as candidates because of their DC voltage regulate capability, bi-directional power transmission capability, and soft switching characteristics to achieve high efficiency. In this paper, these topologies are compared in high-power high-frequency occasions comprehensively. Their topology models and control methods are investigated. Based on a 30kW, 20kHz IBDC module, their design procedures are carried out to achieve best soft switching characteristics. Their main transmission characteristics and loss characteristics are evaluated and compared using design results. Moreover, this paper clarifies advantages and drawbacks of these topologies. Resonant topology holds advantage of soft switching feature and ease of control. Topology selection advice is given at last.
A Cost-Effective Wideband Switched Beam Antenna System for a Small Cell Base Station

A wideband switched beam antenna array system operating from 2 to 5 GHz is presented. It is comprised of a $4\times 1$ Vivaldi antenna elements and a $4\times 4$ Butler matrix beamformer driven by a digitally controlled double-pole four-throw RF switch. The Butler matrix is implemented on a multilayer structure, using 90° hybrid couplers and 45° phase shifters. For the design of the coupler and phase shifter, we propose a unified methodology applied, but not limited, to elliptically shaped geometries. The multilayer realization enables us to avoid microstrip crossing and supports wideband operation of the beamforming network. To realize the Butler matrix, we introduce a step-by-step and stage-by-stage design methodology that enables accurate balance of the output weights at the antenna ports to achieve a stable beamforming performance. In this paper, we use a Vivaldi antenna element in a linear four-element array, since such element supports wideband and wide-scan angle operation. A soft condition in the form of corrugations is implemented around the periphery of the array, in order to reduce the edge effects. This technique improved the gain, the sidelobes, and helped to obtain back radiation suppression. Finally, impedance loading was also utilized in the two edge elements of the array to improve the active impedance. The proposed system of the Butler matrix in conjunction with the constructed array can be utilized as a common RF front end in a wideband air interface for a small cell 5G application and beyond as it is capable to simultaneously cover all the commercial bands from 2 to 5 GHz.
Low Complexity Blind Detection Scheme for Polar Codes: A Segmented CRC Approach

Recently, polar codes are selected by 3GPP as the channel coding method for control information in the 5-th generation mobile communications (5G). In 5G, to reduce the signaling overhead and avoid unnecessary post-processing, each user equipment (UE) is required to use blind detection method to identify which control messages are transmitted to it. Therefore, how to implement blind detection of polar coded frames with limited computational complexity, latency, and power consumption has attracted a lot of research interests. To address this issue, we propose a low-complexity blind detection scheme based on segmented cyclic redundancy check (CRC). Besides using the radio network temporary identifier (RNTI) as the frozen bit pattern, it places several CRC segments at some breaking points of every coding frame. Since now the UE can perform CRC check much earlier, early termination occurs with high probability when other UEs’ control messages are received, thus reducing the decoding complexity and power consumption. Numerical results show that the proposed scheme provides good blind detection performance with much lower decoding complexity.
Hybrid 3D Surface Description with Global Frames and Local Signatures of Histograms

This paper presents a novel 3D descriptor named Frame-SHOT to combine global structural frame with local surface information. Global feature descriptors are generally more descriptive for surface representation, while susceptible to occlusion and clutter. In contrast, local feature descriptors are more robust, while less discriminative due to the limit of the support region. We employ the advantages of these two methods by combining both local and global information for surface description. The Signature of Histograms of Orientation (SHOT) descriptor is used to characterize the local surface and structural frame points of the object are used to encode the global surface. We have compared the proposed Frame-SHOT descriptor with the state-of-the-art global and local methods on two public datasets. The results show that our proposed descriptor is more descriptive and robust for surface matching and recognition.
A Study on Harmonic Power Calculation for Nonuniform Period Linear Time Modulated Arrays

In this letter, sideband radiation (SR) power has been investigated for nonuniform time modulated array (NTMA) systems. It is suggested that the SR calculations already studied in the literature need to be reviewed for NTMAs. From this point of view, a so-called prime-distributed time-modulated array that is a special kind of NTMA is introduced and a closed-form harmonic power expression is presented. Finally, it is shown that the former expression needs to be reconsidered for more appropriate results.
Effect of Electric Field on Methanol Generation in Transformer

Chemical indicators were widely used in condition assessment of transformers. Methanol is considered as a better method than furfural to assess the condition of transformer with higher detection rate and thermal stability. However, the effect of electric field on the generation of methanol still needs further investigation. In this report, insulation oil and 5mm-thick cellulose paper were put into aging cell with a weight ratio of 10:1 after degased and dehydration. Aging experiments were carried out at 120°C with and without an electric field of 2kV/mm, respectively. Degree of polymerization, concentration of methanol, furfural and ethanol dissolved in oil were measured after accelerated experiments. The concentration of methanol in oil was much higher than that of furfural at early aging while it increased with aging time. Samples aged under electric field had higher methanol concentration, and the amount of methanol was proportional to the number of scissions of cellulose chain. It was indicated that the electric field accelerated the generation of methanol by aggravating the scission of cellulose chain.
Weak Supervised Learning Based Abnormal Behavior Detection

Artificial features are adopted in most of the existing abnormal behavior detection. However, it is difficult to choose and design an effective behavior feature for the reason of highly computational complexity and complex scenarios. To solve this problem, temporal consistency based weak supervised abnormal behavior detection method is proposed in this paper. First, temporal gram matrices are constructed for a given video sequence. Then, a pair of behavior units (Candidate action fragment) are formed by exploiting the temporal consistency and the smoothness of human behavior, which helps to locate the start frame and end frame of the related class of abnormal behavior in the video sequence to train the corresponding classifier. Finally, sparse reconstruction is here utilized to detect abnormal behavior. Experiments conducted on common database including CAVIAR and Crossing demonstrate the effectiveness of the proposed method.
A Novel Cloud-based Fog Computing Network Architecture for Smart Factory Big data Applications

Research on the cloud system is increasing among the various ways to deal with the data generated by Smart Factory. In this paper, we propose Cloud-Fog computing network architecture that can be efficiently used to store, analyze, and utilize the accumulated data in smart factories. Build and evaluate system modeling and test bed based on the proposed architecture. As a result, the cloud developed in the open stack can be used for smart factory operation, analyzing various types of data, and checking the status of real-time processing through a dashboard, making it the most suitable structure for a smart factory environment requiring real-time performance.
Temporal Pattern Localization using Mixed Integer Linear Programming

In this paper, we consider the problem of localizing the subsequence in time series which contains the dynamic pattern of interest. This is motivated by brain computer interface application where we need to analyze the dynamic pattern of brain signals in response to external stimulus. We treat the localization as a binary label assignment problem and formalize a mixed integer linear programming (MILP) problem. The optimal solution is obtained by minimizing a cost function associated with label assignment subject to empirical constraints induced by data acquisition process. We first experiment with synthetic data to evaluate the effectiveness of the proposed MILP formulation and achieve 10.8% improvement on F1-score. We then experiment with electrocorticographic (ECoG) data for a classification task and achieve 8.8% improvement on accuracy using subsequences localized by our method compared to other methods.
Multiple Mice Tracking: Occlusions Disentanglement using a Gaussian Mixture Model

Mouse models play an important role in preclinical research and drug discovery for human diseases. The fact that mice are a social species partaking in social interactions of high degree facilitates the study of diseases characterized by social alterations. Hence, robust animal tracking is of great importance in order to build tools capable of automatically analyzing social behavioral interactions of multiple mice. However, the presence of occlusions is a major problem in multiple mice tracking. To deal with this problem, we present here a tracking algorithm based on Kalman filter and Gaussian Mixture Modeling. Specifically, Kalman tracking is used to track the mice and when occlusions happen, we fit 2D Gaussian distributions to separate mouse blobs. This helps us to prevent mice identity swaps as it is an important feature for accurate behavior analysis. As the results of our experiments show, the proposed algorithm results in much fewer identity swaps than other state of the art algorithms.
Robust Projective Low-Rank and Sparse Representation by Robust Dictionary Learning

In this paper, we discuss the robust factorization based robust dictionary learning problem for data representation. A Robust Projective Low-Rank and Sparse Representation model (R-PLSR) is technically proposed. Our R-PLSR model integrates the L1-norm based robust factorization and robust low-rank & sparse representation by robust dictionary learning into a unified framework. Specifically, R-PLSR performs the joint low-rank and sparse representation over the informative low-dimensional representations by robust sparse factorization so that the results are more accurate. To make the factorization and representation procedures robust to noise and outliers, R-PLSR imposes the sparse L2, 1-norm jointly on the reconstruction errors based on the factorization and dictionary learning. Note that L2, 1-norm can also minimize the reconstruction error as much as possible, since the L2, 1-norm theoretically tends to force many rows of the reconstruction error matrix to be zeros. The Nuclear-norm and L1-norm are jointly used on the representation coefficients so that salient representations can be obtained. Extensive results on several image datasets show that our R-PLSR formulation can deliver superior performance over other state-of-the-arts.
Image Reconstruction Performance of a 12-Electrode CCERT Sensor Under Five Different Excitation Patterns

To obtain useful knowledge and experience of CCERT under different excitation patterns, the image reconstruction performance of a 12-electrode CCERT sensor under five different excitation patterns (1-electrode excitation pattern, 2-electrode excitation pattern, 3-electrode excitation pattern, 4-electrode excitation pattern, and 5-electrode excitation pattern) are investigated and compared. A 12-electrode CCERT prototype, which can implement different excitation patterns, is developed to obtain the practical experiment data. Four typical image reconstruction algorithms (Linear Back Projection (LBP) algorithm, Landweber’s iteration algorithm, Algebraic Reconstruction Technique (ART) algorithm, and Simultaneous Iterative Reconstruction Technique (SIRT) algorithm) are introduced to investigate and compare the image reconstruction performance with the experimental data obtained from the CCERT prototype. Research results show that the 1-electrode excitation pattern and the 2-electrode excitation pattern have better global image reconstruction performance. These two excitation patterns are better choices, while the 3-electrode excitation pattern, the 4-electrode excitation pattern, and the 5-electrode excitation pattern are not recommended.
Performance Evaluation of N O- Reference Image Quality Metrics for Visible Wavelength Iris Biometric Images

Image quality assessment plays an important role in iris recognition systems because the system performance is affected by low quality iris images. With the development of electronic color imaging, there are more and more researches about visible wavelength (VW) iris recognition. Compared to the near infrared iris images, using VW iris images acquired under unconstrained imaging conditions is a more challenging task for the iris recognition system. However, the number of quality assessment methods for VW iris images is limited. Therefore, it is interested to investigate whether existing no-reference image quality metrics (IQMs) which are designed for natural images can assess the quality of VW iris images. In this paper, we evaluate the performance of 15 selected no-reference IQMs on VW iris biometrics. The experimental results show that several IQMs can assess iris sample quality according to the system performance.
Persona Traits Identification based on Myers-Briggs Type Indicator(MBTI) - A Text Classification Approach

Personality is a very important part of our lifestyle. It determines how we live, speak, react, and indicates our preferences, and even affects our mental health. Personality analysis is an intuitive ability of humans, carried out every day with multiple people, and for innumerable reasons. Personality profiling, specifically, has several real-life use cases, such as mental health screening tests, screening during human resource interviews, recommendations to writers about the interplay between personalities that people enjoy reading about and friend suggestions. This work presents the analysis of text written by a person such as an essay, tweet, or blog post and creates a personality profile of the person. The main considerations of the work are the type of data gathered, text preprocessing methods, and the machine learning techniques used to estimate personality scores. Various machine learning models and feature vector combinations have been compared, and deployment of solutions have been described. Accuracies up to 88% were achieved using the methods detailed in this work.