Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybridizable Discontinuous Galerkin Method Contrast Source Inversion of 2-D and 3-D Dielectric and Magnetic Targets

We present a microwave imaging algorithm capable of simultaneously reconstructing electric and magnetic targets from both electric and magnetic field measurement data, with support for arbitrary imaging system boundaries and inhomogeneous background media. A high-order time-harmonic discontinuous Galerkin method (DGM) forward solver is adopted within a contrast source inversion (CSI) algorithm formulated for electric and magnetic targets. Due to its high-order capabilities, the DGM forward solver effectively decouples the contrast and field discretizations without introducing a dual mesh. The drawback of standard DGM forward solvers, namely, their high computational cost, is addressed by a hybridizable DGM (HDGM) formulation. Synthetic and experimental results for both DGM-CSI and HDGM-CSI are presented for a variety of 2-D and 3-D measurement configurations and both dielectric and magnetic targets. HDGM-CSI is shown to have imaging performance comparable to FEM-CSI for dielectric targets, and significant additional flexibility that will enable future electromagnetic imaging applications and system design.
A progression strategy of proximal algorithm applied to the digital images inpainting

Digital inpainting uses spatial or frequency information to restore a damaged/deleted portion of photos. Digital Image inpainting is a new and exciting research topic in the field of multimedia computing and image processing since 2000. In order to accurately solve the problem of digital image restoration in which the objective function is nonlinear, a new optimization method different from both linear search and trust region is presented in this paper. This new method is based on the family of proximal algorithms. We customize our algorithm to solve the problem of image restoration from a noisy image with a very high missing pixel.Finally, the results proved the accurate and rapid convergence of the proposed algorithm compared to other algorithms.
Capped $l_1$ -Norm Sparse Representation Method for Graph Clustering

As one of the most popular clustering techniques, graph clustering has attracted many researchers in the field of machine learning and data mining. Generally speaking, graph clustering partitions the data points into different categories according to their pairwise similarities. Therefore, the clustering performance is largely determined by the quality of the similarity graph. The similarity graph is usually constructed based on the data points’ distances. However, the data structure may be corrupted by outliers. To deal with the outliers, we propose a capped <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula>-norm sparse representation method (CSR) in this paper. The main contribution of this paper is threefold: 1) a similarity graph with clear cluster structure is learned by employing sparse representation with proper constraints; (2) the capped <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula>-norm loss is utilized to remove the outliers, which ensures the graph quality; and 3) an iterative algorithm is developed to optimize the proposed non-convex problem. The extensive experiments on real-world datasets show the superiority of the proposed method over the state-of-the-art, and demonstrate its robustness to the outliers.
A Supervised Approach-based Job Scheduling Technique for Distributed Real-Time Systems

Distributed real time systems have end-to-end jobs which are scheduled on multiple processors. These jobs are composed of several sub-jobs which do not have individual end-to-end constraints. To efficiently schedule these sub-jobs, their local deadline requirements are needed to be known. The local deadline assignment problem has been recognized as a crucial problem in distributed real-time system research. In this paper, we present a supervised machine learning based job scheduling technique for a distributed Real-Time System (RTS). We use linear regression, support vector machine, and artificial neural network machine learning techniques for predicting the local deadline of upcoming workload with a given release time and deadline of executed sub-jobs. We also develop a technique for labeled dataset creation in a distributed RTS. We demonstrate that the supervised machine learning based job scheduling technique reduces the job dropping rate and thereby enhances the utility of the distributed RTS.
Analysis of Single Phase Transformer Excited by Superposition of Square and Sine Wave

In this paper the performance of single phase two winding transformers is analyzed when excited by a voltage produced by superposition of square and sine wave and supplying different kind of loads. The transformer is excited in this manner to increase its voltage rating which results in power capacity enhancement. A study is carried out to access the response of two different class of transformers, small transformer (1 KVA) and distribution transformer (25 KVA) with different loads, for above said input. The effect of above said excitation on magnetizing current, load voltage, and load current is analyzed and compared with sine wave excitation. To access the effectiveness of this power capacity enhancement method, numerical simulations are carried out and the empirical results are presented.
Creative strategies in problem solving

Three experiments investigated how individuals solve “shape” problems. These problems are not susceptible to a meansends strategy. They consist of a configuration of squares, whose sides consist of separate pieces; and the task is to remove a given number of pieces to leave behind a given number of squares. The paper presents a theory of how individuals develop strategies for these problems. Experiment 1 explored the constraints of symmetry and visual saliency in shape problems. Experiment 2 corroborated the theory’s prediction of a major shift in which knowledge acquired during the evaluation of tactical steps comes to govern the generation of these steps. Experiment 3 showed that participants could be biased to adopt strategies making use of specific tactical steps.
Ultracapacitor Aided Performance Enhancement of Battery Powered Electric Vehicles

This paper presents energy control of a battery powered Electric Vehicle (EV) with the aid of Ultra Capacitor (UC). Induction motor powered EV is fed by the battery through the buck boost converter and inverter with New European standard Driving Cycle (NEDC). A 48V battery and 300F UC is connected to the motor load of excitation of 200 V through a 5kW bi-directional buck boost converter. The voltage profile of the load is controlled by Reset-Set-Track (RST) controller thereby controlling the load torque profile. Energy flow to load is from both battery and UC with power management, which limits the battery current requirement and hence improves the battery performance. Results show that the voltage regulation of battery is improved from 4.8% to 1.8 % by the compensation of voltage sag of load with the aid of UC and the State Of Charge (SOC) is increased by4.2% over a period of2 hours. Peak power management is also improved by monitoring the load profile. Simulation has been done using MATLAB/SIMULINK.
Aspectos históricos e metodológicos da medição e estimativa da taxa metabólica basal : uma revisão da literatura Historical and methodological aspects of the measurement and prediction of basal metabolic rate : a re v i

This paper re v i ews the most re l e vant aspects of measuring basal metabolic ra t e ( B M R ) . The authors discuss individual and lifestyle factors (age, a n t h ro p o m e t ry, s m o k i n g , p h y s ical activity, d i e t , m e n s e s , and ethnicity) as well as possible environmental factors at time of meas u rement which may alter BMR va l u e s . Various available pre d i c t i ve equations, especially those recommended by the Wo rld Health Organization in 1985, and subsequent validation studies, p a rt i c u l a rly in populations living in the tro p i c s , a re pre s e n t e d . The re v i ew indicates the need for m o re information on BMR from populations living in different parts of the world so that better estimates can be provided for clinicians and epidemiologists.
The Link between Knowledge, Attitudes and Practices in Relation to Atmospheric Haze Pollution in Peninsular Malaysia

Transboundary haze episodes caused by seasonal forest fires have become a recurrent phenomenon in Southeast Asia, with serious environmental, economic, and public health implications. Here we present a cross-sectional survey conducted among people in Kuala Lumpur and surrounds to assess the links between knowledge, attitudes, and practices in relation to the transboundary haze episodes. Of 305 respondents, 125 were amateur athletes participating in a duathlon event and the remainder were surveyed in an inner-city shopping mall. Across the whole sample, people who possessed more factual information about the haze phenomenon showed significantly higher levels of concern. Duathletes were more knowledgeable than non-duathletes and also more concerned about the negative effects of haze, especially on health. For all people who regularly practice outdoor sports (including people interviewed at the shopping mall), higher levels of knowledge and concerned attitudes translated into a greater likelihood of engaging in protective practices, such as cancelling their outdoor training sessions, while those with greater knowledge were more likely to check the relevant air pollution index on a daily basis. Our results indicate that the provision of accurate and timely information about air quality to residents will translate into beneficial practices, at least among particularly exposed individuals, such as amateur athletes who regularly practice outdoor sports.
Clinical and serological manifestations associated with interferon-α levels in childhood-onset systemic lupus erythematosus

OBJECTIVE: To determine the serum levels of interferon alpha in childhood-onset systemic lupus erythematosus patients, their first-degree relatives and healthy controls and to evaluate the associations between serum interferon alpha and disease activity, laboratory findings and treatment features. METHODS: We screened consecutive childhood-onset systemic lupus erythematosus patients in a longitudinal cohort at the pediatric rheumatology unit of the State University of Campinas between 2009 and 2010. All patients demonstrated disease onset before the age of 16. Disease status was assessed according to the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) and Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI). Interferon alpha levels were measured using an enzyme-linked immunoabsorbent assay. RESULTS: We included 57 childhood-onset systemic lupus erythematosus patients (mean age 17.33±4.50), 64 first-degree relatives (mean age 39.95±5.66), and 57 healthy (mean age 19.30±4.97) controls. Serum interferon alpha levels were significantly increased in childhood-onset systemic lupus erythematosus patients compared to their first-degree relatives and healthy controls. Interferon alpha levels were significantly increased in patients with positive dsDNA antibodies, patients with cutaneous vasculitis, patients with new malar rash and patients who were not receiving medication. Interferon alpha levels correlated with C3 levels and systemic lupus erythematosus Disease Activity Index scores. In addition, we observed an inverse correlation between patient age and interferon alpha levels. CONCLUSION: Interferon alpha may play a role in the pathogenesis of childhood-onset systemic lupus erythematosus, especially in cutaneous manifestations and dsDNA antibody formation. The observation that interferon alpha levels are increased in patients who are not taking medication should be investigated in longitudinal studies to determine whether elevated interferon alpha levels may predict systemic lupus erythematosus flares.
Persymmetric Subspace Detection in Structured Interference and Non-Homogeneous Disturbance

This letter addresses the problem of distributed target detection in structured interference and non-homogeneous disturbance. The target signal and interference locate in two linearly independent subspaces with unknown coordinates, while the disturbance is partially homogeneous with an unknown covariance matrix. By incorporating the persymmetric structure of received data, we propose a persymmetric subspace detector for a distributed target, which includes the detector for a point-like target as a special case. Remarkably, the proposed detector is shown to ensure a constant false alarm rate property with respect to both the covariance matrix structure and the power scaling factor. In addition, analytical expressions for the probabilities of false alarm and detection of the special case of the proposed detector for point-like targets are derived, which are verified by Monte Carlo trials. Numerical examples illustrate that the proposed detector can achieve better detection performance, as well as anti-interference ability in training-limited situations.
1.618 – The golden number for generating five phase space vector PWM

The paper brings out the observation that all the space vectors of a five phase inverter both in the $(\alpha_{1}, \beta_{1})$ plane and $(\alpha_{3}, \beta_{3})$ plane can be automatically generated using the golden number 1.618. The repeated multiplication of the number generates all the five phase space vectors. This observation has been used to generate SVPWM for a five phase inverter and results are also presented. The logic for mapping the coordinates between the $(\alpha_{1}, \beta_{1})$ and $(\alpha_{3}, \beta_{3})$ coordinates is also presented and utilized for automated generation of five phase SVPWM avoiding complex computations. The generation of SVPWM using the proposed method does not require look up tables and complex trigonometric computations. The proposed method is explained and validated for a two level inverter which can also be extended to any n-level inverter without increased complexity. The simulation of the work has been carried out in MATLAB/SIMULINK for which results are presented
A Novel Approach for Anomaly Event Detection in Videos Based on Autoencoders and SE Networks

In the paper, we develop an unsupervised learning approach for anomaly event detection in videos based on a 3D ConvNet encoder-decoder for extracting spatial features and a ConvLSTM encoder-decoder for learning the temporal evolution of the spatial features. Moreover, squeeze-and-excitation networks (SENet) is incorporated into our model to take global information of each frame into account. In training, our model only includes normal events of video, whereas in testing, the videos have both normal events and abnormal events. The effectiveness of the proposed approach for anomaly event detection is validated through experiments on the UCSD datasets.
An Improved Bidirectional dc/dc Converter with Split Battery Configuration for Electric Vehicle Battery Charging/Discharging

A bidirectional dc/dc on-board chargers for EV battery charging/discharging application provides a better solution of V2G and G2V compatibility. Isolated converter prefer to deal with high power density at wide load range which is useful in EV application. The bidirectional converter performs the step-up and step-down operation at zero voltage switching for all power switches in both directions. Low voltage or battery side converter is chosen as current source converter to limit short circuit current and effective current rating of the converter. The gain of the converter is offered by input boost converter along with transformer. Additional gain is provided by end side converter with voltage double to synchronize voltage with dc grid or high voltage dc During step down mode, The converter allows a low charging current to charge the batteries. To increase charging current of the battery, the voltage has to reduced with two battery system. During charging of batteries, the split of is done to achieve high charging current. The commutation of switches is achieved at ZVS as soft-switching without snubber circuitry and the voltage across switched clamped naturally. The proposed topology is simulated using MATLAB/Simulink for a 350W battery system.
An Algorithm for Self-Motivated Hierarchical Sequence Learning

This work demonstrates a mechanism that autonomously organizes an agent’s sequential behavior. The behavior organization is driven by pre-defined values associated with primitive behavioral patterns. The agent learns increasingly elaborated behaviors through its interactions with its environment. These learned behaviors are gradually organized in a hierarchy that reflects how the agent exploits the hierarchical regularities afforded by the environment. To an observer, the agent thus appears to exhibit basic selfmotivated, sensible, and learning behavior to fulfill its inborn predilections. As such, this work illustrates Piaget’s theories of early-stage developmental learning.
Development of a 10-DOF robotic system for upper-limb power assistance

This paper addresses a new robotic system to assist the upper limb movements for the elderly and the disabled in activities of daily living (ADL). The proposed robotic system consists of 1-degree-of-freedom (DOF) under-actuated exoskeleton mechanism for hand grasping, 3-DOF exoskeleton parallel mechanism for wrist motion, and 6-DOF serial mechanism that is attached to the wrist mechanism. Thus, the serial mechanism covers shoulder and elbow workspace and supports arbitrary movement of the forearm. The proposed mechanical structure and upper limb mounting manner on the power assistant system minimizes mechanical constraints imposed on the body, thus, it allows to free user from a very slow and cumbersome process for wearing and taking off of the systems.
An Efficient Linear Power Amplifier with 2nd Harmonic Injection

A 2.3GHz hybrid GaN power amplifier (PA) with $2 ^{nd}$ harmonic injection (HI) in the output is studied in terms of efficiency and linearity enhancement under a two-tone signal input. The $2 ^{nd}$ harmonic signal is obtained by frequency doubling of the main RF signal but with opposite phase for ${IMD}_{3}$ cancellation. The HI-PA demonstrates a peak drain efficiency of 80% (assuming 100% efficient $2 f_{0\, }$ injection) at 14.1W output power and more than 10dB reduction in ${IMD}_{3}$ when the injected harmonic phase and amplitude are adjusted for a compromise between efficiency and linearity.
Improved Dual Battery Charging System for Grid Connected Bi-directional EV Charger

This paper proposes an isolated low power bidirectional on board electric vehicle (EV) dual battery charging topology with grid connectivity and unity power factor (UPF) operation. The proposed EV charging system is able to exchange active and reactive power demand by either side of the bidirectional charger. The primary EV battery operates in a bidirectional charging mode through a dual active bridge converter with a phase shifting operation whereas the auxiliary battery operates in a unidirectional mode to provide power to the EV auxiliary units. This improved topology of dual battery charging system operates independently to maintain necessary amount of battery voltage without affecting the transformer efficiency. The active and reactive power demand is met by the high capacity EV primary battery, whereas the auxiliary battery is intended only for unidirectional controlled power flow. In this paper the EV charging topology is investigated with a front end decoupled power control and a secondary battery sliding mode control (SMC) for bi-directional power flow operation. The mathematical model of the proposed converter is discussed through state space averaging technique to analyse the converter dynamics with variable demand during bi-directional power flow operation. A MATLAB and Simulink is used to develop the proposed circuit topology of dual battery EV charging system for a 4kW single phase grid connected system. The prototype is designed to validate the DC-DC power flow with a high frequency isolation.
Increase Capacity of QR Code Using Compression Technique

The main objective of this research work is to enhance the data storage capacity of the QR codes. By achieving the research aim, we can visualize rapid increase in application domains of QR Codes, mostly for smart cities where one needs to store bulk amount of data. Nowadays India is experiencing demonetization step taken by Prime Minister of the country and QR codes can play major role for this step. They are also helpful for cashless society as many vendors have registered themselves with different e-wallet companies like paytm, freecharge etc. These e-wallet companies have installed QR codes at cash counter of such vendors. Any time when a customer wants to pay his bills, he only needs to scan that particular QR code. Afterwards the QR code decoder application start working by taking necessary action like opening payment gateway etc. So, objective of this research study focuses on solving this issue by applying proposed methodology.
Maximum Power Point Tracking Using a Novel Current Control Strategy in an SRG Based Variable Speed Wind Energy Conversion System

This paper presents the operation of a switched reluctance generator-based wind energy conversion system (WECS) that is capable of harvesting maximum power under variable wind speed conditions using a novel current control strategy for controlling the generator phase currents. The generator is coupled to a variable speed wind turbine. Its output is harvested using an asymmetrical bridge converter by firing the devices of the converter during the falling inductance region of each of the phases to enable the machine to operate in generating mode. The current passing through each of the phase windings of the machine is continuously monitored and controlled using a hysteresis current controller to make sure that the devices in the converter are fired suitably to harvest maximum power from the WECS at any given wind speed. The entire system has been modeled and simulated in Simulink/MATLAB platform and the results presented show the effectiveness of the control strategy adopted.
A Distributed Control for Adequate Power Sharing Based on Available Power in Sources

This paper proposes an adaptive distributed control algorithm for adequate power sharing between the sources based on available power in individual source. The proposed control calculates the droop constant for each source according to available power in source and bus voltage deviation. It restores the bus voltage deviation to its nominal value and it is based on incremental rate of change of power. It worked with minimum sensors and the overall system becomes easy to control. The designed supervisory control will manage the power balance in the microgrid by incorporating energy storage system (ESS) into the service. The mode of operation of ESS is decided by the bus voltage deviation. The proposed control algorithm is tested in real time simulator.