Miracle-dz/UniFAR-FTUs huggingface dataset:
Current Sensor with low inductance

This paper deals with design of current sensor introducing low inductance. The sensor is designed for a current up to 500 A and 120 kHz. Since the market offers several types of sensors suitable for the targeted application, they usually have problems with measured signal postprocessing. This paper describes the principle of operation and includes simulation results and measurement made on the laboratory prototype of the proposed sensor.
Hardware Modelling of a PLC Multipath Channel Transfer Function

PLC channel transfer functions are critical in developing channel emulators. Emulators are used to test PLC transceivers that would have been otherwise tested on a live network which is not only hazardous but also is not repetitive since the PLC network's characteristics vary with time and frequency. The PLC channel transfer function that is considered here is the multipath channel model of Zimmermann and Dostert. Zimmermann's multipath channel transfer function includes not only the attenuation experienced by the signal, but also the phase change due to the multipath. The inclusion of the phase in the transfer function requires the processing of imaginary numbers in the FPGA. Euler's theorem is used to convert from the polar form to the rectangular form of the transfer function. This paper focuses on the hardware modelling of the rectangular form using Hardware Description Language. The real and imaginary components of the rectangular form are modelled as two circuit models. The designed channel transfer function module uses fixed point (FP) format. The HDL model was successfully synthesized to a hardware equivalence using the XILINX ISE tool. XILINX core generator was used for the CORDIC module that computes the sine, cosine, hyperbolic sine and hyperbolic cosine functions. Simulation result of the hardware model revealed an accuracy of up to at least 3 decimal places when compared to the theoretical results using Excel or Matlab.
A Risk Prediction Model for Type 2 Diabetes Based on Weighted Feature Selection of Random Forest and XGBoost Ensemble Classifier

Type 2 diabetes mellitus is a severe chronic disease threatening human health and has a high incidence worldwide. People need to use effective prediction model to diagnose and prevent diabetes in time. At present, data mining technology has become an increasingly important technology with classification capability in the field of medical diagnosis. This paper proposes a risk prediction model for type 2 diabetes based on ensemble learning method. In the proposed model, the weighted feature selection algorithm based on random forest (RF-WFS) is used for optimal feature selection, and extreme gradient boosting (XGBoost) classifier. The effectiveness of the method was validated by comparing the various performance metrics and the results of different contrast experiments. Additionally, we get a better prediction accuracy using the method than using the other classification algorithms (C4.5, Naive Bayes, AdaBoost, Random Forest). The validation results at UCI Pima Indian diabetes dataset shows that the model has better accuracy and classification performance than other research results mentioned in the literature. As a result, it has been proven that the model would be effective for the diagnosis of diabetes at the initial stage.
Implementation of IoT Architecture for Intruder Alert System using MQTT Protocol and MEAN Stack

Internet of Things (IOT) facilitates the interdependence between intelligent machines, people and the data. IoT has already been leveraged by many industries and it will be extensively used very soon. Automated emergency alert system is one such application which can commercially scale and can achieve high operating efficiency when integrated with IoT. In this paper, an end-to-end architecture of an intruder alert system has been proposed. The proposed work uses MQTT protocol and web development tools like MEAN. The merits of using these protocols over other protocols and tools in resource constrained IoT applications were discussed. Finally, the real-time implementation of the proposed architecture has been demonstrated.
Methods for Describing and Characterizing the Mechanical Behavior of Running-Specific Prosthetic Feet

Current methods for describing and characterizing the mechanical behavior of running-specific prosthetic feet are incomplete, and this has limited our understanding of how design parameters impact athlete performance. Deflections induced by most ground reaction forces consist of vertical, horizontal, and angular components, but previous work has focused only on the vertical component. Furthermore, the deflection depends heavily on the direction of the force, which changes throughout stance phase of running. In this paper, we introduce several methods that can be used to more precisely describe and characterize the mechanics of running-specific prosthetic feet. We use a custom finite element model to simulate these methods, and validate them with a series of tests using a prototype foot in a universal testing machine.
Fast ISAR Imaging based on Physical Optics method and Reconstruction via Compressed Sensing

This paper implements the compressed sensing theory into the inverse synthetic aperture radar imaging and the reconstruction under sparse sampling. The physical optics method based on quadratic discretization is employed to obtain backscattered fields with respected to multi-frequency and multi-azimuth angle scan. Due to the sparse characteristics in image domain, compressed sensing theory is utilized for reconstructing the 2D ISAR image under sparse sampling.
Using Data Mining Tools for Breast Cancer Prediction and Analysis

Breast Cancer is one of the most common disease that is responsible for high number of women’s deaths every year. Despite the fact that cancer is treatable and healable in earliest stages, the huge number of patients are examined with cancer very late. Data mining process and classification are an efficient way to categorise the data particularly in medical fields, where those approaches are broadly used in diagnosis to make decision. This paper presents a performance comparison among the classifiers: Decision tree classifier (J4.8, Simple CART), Bayes classifier (NaiveBayes, Bayesian LogisticRegression). The Wisconsin Breast Cancer(original) dataset is used here and is taken from UCI Machine learning Repository. The main goal is to classify data of both the algorithms in terms of accuracy. Our experimental result shows that among all the classifiers, decision tree classifier i.e. Simple CART (98.13%) gives higher accuracy.
A 0.2 - 8 MS/s 10b flexible SAR ADC achieving 0.35 - 2.5 fJ/conv-step and using self-quenched dynamic bias comparator

A 10b flexible SAR ADC is presented incorporating a self-quenched dynamic bias comparator and a self-triggered asynchronous delay line. The ADC is fabricated in 65nm CMOS, occupies 0.04mm2 and has an ENOB > 9bit and SFDR >66dB for sampling rates from 0.2 to 8MS/s at supply voltages respectively from 0.7V to 1.3V with a Walden FoM from 0.35 to 2.5fJ/conv-step. Keywords: SAR ADC, flexible, Walden Figure-of-Merit, SNDR, dynamic bias.
Novel Magnetic Cell-Scaffold Construct with and without Magnetic Field Enhanced Osteogenesis of Stem Cells and Formation of new bone

Superparamagnetic iron oxide nanoparticles (IONPs) are promising bioactive additives to fabricate magnetic scaffolds for bone tissue engineering. Their similarity to macromolecules and many unique properties indicate that functional nanoparticles have promising potential for the modification and improvement of traditional scaffolds to obtain excellent biocompatibility, tunable stiffness, physical sensing, and stimulus-response capabilities. Furthermore, magnetic stimulations from static magnetic fields (SMFs) and electromagnetic fields (EMFs) can also substantially improve bone repair and regeneration. Therefore, the objectives of this study were to: (1) develop a novel magnetic IONP incorporated scaffold for bone tissue engineering, and (2) investigate the effects of IONP-incorporation and SMF application on the proliferation, osteogenic differentiation and bone mineral synthesis of stem cells like human dental pulp stem cells (hDPSCs) seeded on IONP-incorporated scaffold for the first time. Our results demonstrate that the novel CPC functionalized with IONPs is promising to promote osteoinduction and bone regeneration.
A Wideband Substrate Integrated Waveguide Slot Antenna with Quad Modes

This paper presents a quad-mode slot antenna based on the substrate integrated waveguide (SIW) structure. A SIW antenna is designed at first, and two slots are etched on the patch, to radiate energy in the same phase. By analyzing the electric field distributions of the selected mode, several pairs of shorting vias are arranged inside the SIW cavity. Consequently, the lower modes (TE310 mode and TE120 mode) are moved upward and merged with the TE320 mode and a high-order mode. Hence, a wideband SIW slot antenna with quad modes is achieved. The proposed antenna shows an impedance bandwidth of 16.4% and a peak gain of 9 dBi from the simulated results.
Automatic Pedestrians Segmentation Based on Machine Learning in Surveillance Video*

Pedestrian detection and segmentation play an important role in video surveillances. This paper presents a novel pipeline framework for automatic pedestrian detection and segmentation by combining machine learning with traditional computer visual methods. In particular, the Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) are employed for pedestrian detection, and then the frame difference method is adopted for the tracking of the pedestrian. GrabCut and Mask R-CNN methods are used in the segmentation of pedestrians. The experiments are conducted on common benchmarks. The experimental results show that our method has made significant progress in automatic pedestrian detection and segmentation compared to the traditional Grabcut method.
A 7nm 4GHz Arm®-core-based CoWoS® Chiplet Design for High Performance Computing

A dual-chiplet Chip-on-Wafer-on-Substrate (CoWoS®) was implemented in 7nm 15M process. Each SoC chiplet has four Arm® Cortex®-A72 processors operating at 4GHz. The on-die interconnect mesh bus operates above 4GHz at 2mm distance. The inter-chiplet connection features a scalable, 0.56pJ/bit power efficiency, 1.6Tb/s/mm2 bandwidth density, and 0.3V Lowvoltage- In-Package-INterCONnect (LIPINCONTM) interface achieving 8Gb/s/pin and 320GB/s bandwidth. Silicon test-chip measurements validate the processor, on-die interconnects and inter-chiplet interface performance. The built-in eye-scan feature shows the inter-chiplet connection achieves 244mV eye-height and 69% UI eye-width.
Study of Low-Voltage Ride Through of Wind Permanent Magnet Synchronous Generator by means of STATCOM

Within the conception of green energy spreading more and more the wind generated energy became a promising source of electricity. The connection of wind parks to the net however causes problems concerning the stability and the quality of electro energy. The work of power system is sensible to the disturbances in the net and it is necessary to provide the low voltage ride through of wind synchronous generator consists from permanent magnets by the help of system devices like STATCOM. Using a mathematical model it is investigated the behavior of the generator and compensator by different disturbances in the net. Using different approaches and algorithms their control is investigated.
An Opinion Mining for Indian Premier League Using Machine Learning Techniques

Social media has dramatically changed the way people express their opinion, appraisals or feelings towards entities or brand. Among many social media sites one of the free social service websites i.e. twitter, that permits users to publish their everyday life related events. As we know that twitter blog posts are being originated continually and Twitter having character short or limit to the Twitter posts (tweets) and also extremely compatible origins of continuous flow data for finding or detect opinion mining. Blog posts will reveal general or people emotions once taken in collection as an example throughout events like IPL 2016. Here our work presents, and provide the effectiveness of a machine learning model as positive or negative sentiment on tweets. The data collection of tweets and processing them by filtration best of the authorized IPL hastags (#IPL 2016 and #IPL 9)which can be done through using of Twitter's API(Application Programming Interface) service. We analyze the performance of the ‘Random Forest’ against existing supervised machine learning algorithms with respect to its accuracy, specificity, sensitivity etc. here explanation of our paper is to performed opinion mining for the event like Indian Premier League 2016 effectively.
Advanced Power System Stabilizer for a Single Machine - Infinite Bus Model: Transient Analysis

This paper considers tuning of a two-stage lead-lag power system stabilizer (PSS) for Al Sarir west power plant (SWPP) which is operated by General Electricity Company in Libya. The considered power plant has been modeled as a single machine infinite bus configuration. The model has been developed using MATLAB/Simulink and validated through NEPLAN package. The proposed PSSs are fundamentally designed to damp out the low frequency oscillations, consequently this research considers several techniques to find out the most appropriate tuning algorithm in the sense of an accepted overshoot and small settling time. The article exploits Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to find the required tuning parameters. The optimization of the PSS parameters are calculated in order to enable the PSS to provide a significant damp action to the local area mode of oscillations that occur in power systems as consequences of disturbances including a three phase short circuit at the infinite bus. Fuzzy Logic Control (FLC) is also considered in this work, where two-stage of optimization is examined in this article. The superiority of the proposed techniques are judged through an integral square error (ISE) and Integral Absolute Error (IAE) performance indices.
Effects of rTMS and intensive rehabilitation in Parkinson’s Disease on learning and retention

Movement is accompanied by modulation of oscillatory activity in different ranges over the sensorimotor areas. This increase is more evident in normal subjects and less in patients with Parkinson’s Disease (PD), a disorder associated with deficits in the formation of new motor skills. Here, we investigated whether such EEG changes improved in a group of PD patients, after two different treatments and whether this relates to performance. Subjects underwent either a session of 5 Hz repetitive Transcranial Magnetic Stimulation (rTMS) over the right posterior parietal cortex or a 4-week Multidisciplinary Intensive Rehabilitation Treatment (MIRT). We used a reaching task with visuo-motor adaptation to a rotated display in incremental 10° steps up to 60°. Retention of the learned rotation was tested before and after either intervention over two consecutive days. High-density EEG was recorded throughout the testing. We found that patients adapted their movements to the rotated display similarly to controls, although retention was poorer. Both rTMS and MIRT lead to improvement in retention of the learned rotation. Mean beta modulation levels changed significantly after MIRT and not after rTMS. These results suggest that rTMS produced local improvement reflected in enhanced short-term skill retention; on the other hand, MIRT determined changes across the contralateral sensorimotor area, reflected in beta EEG changes.
Providing an IEC 61850 Counterpart of the Trusty Multimeter - Approaching the Maintenance Procedures for IEC 61850 Substations

Part of the pre-commissioning test in a conventional substation is wires checking. This is usually done by verifying connectivity and measuring voltage using the trusty multimeter. In a substation where fiber optic and data cables are used instead of copper wires, new tools have to be utilized. This paper introduces a fundamental method on 1) SCD file validation during Factory or Site Acceptance Tests, and 2) interoperability issues troubleshooting. The IEC 61850 network traffic is compared to the design description on the SCD file and differences between the two are found. The paper gives a practical example for the concept applied to horizontal communication (GOOSE).
Classification of EEG Multiple Imagination Tasks Based on Independent Component Analysis and Relevant Vector Machines

To solve the problem of feature extraction in braincomputer interface (BCI), the position, size and direction of dipole are located by using dipole localization method, so as to locate the active part of advanced nerve activity and remove a series of physiological and electrical artifacts such as electro-ophthalmogram. The common space pattern and correlation vector machine are used to extract the effective components of EEG signals and classify multiple motor imagery tasks. The results show that the combination of EEG dipole localization and common spatial pattern can effectively improve the signal-to-noise ratio of EEG signals and extract more obvious features. The correlation vector machine provides better classification results and is an effective method to complete the classification and recognition of motor imagery signals.
An Online Spectral Information-Enhanced Approach for Artifact Detection and Fault Attentuation in Myoelectric Control*

In myocontrol of neuroprosthetic devices, multichannel electromyography (EMG) can be used to decode the intended motor command, based on distributed activation patterns of stump muscles. In this regard, the high density EMG (HD-EMG) approach allows for enhancement of the spatiotemporal resolution for motor intention detection. Despite the advantages of relying on several EMG channels, the challenge of high-density electrode systems is the dynamically changing electrode-skin contact impedance, which can affect a considerable number of electrodes over the time of data acquisition. This can result in obtaining unreliable, low-quality EMG recording with a distributed artifact pattern over the grid of EMG sensors. To address this issue, we propose a novel online approach for adaptive information extraction and enhancement for automatic artifact detection and attenuation in HD-EMG-based myocontrol of prosthetic devices. The method is based on an adaptive weighting scheme that modifies the contribution of each HD-EMG channel considering the spectral information content relative to artifacts. The technique (named IE-HD-EMG) was tested as an online pre-conditioning step for a challenging multiclass classification problem of 4-finger activation, using linear discriminant analysis. It is shown that for this application, the proposed IE-HD-EMG technique led to a superior performance in finger activation recognition (79.25% accuracy, 89% sensitivity, 89.15% specificity) in comparison to the conventional HD-EMG recording under the same condition without the proposed approach (56.25% accuracy, 61.3% sensitivity, 67% specificity). Therefore, the proposed technique can have a significant potential to expand the clinical viability of HD-EMG systems.
A framework for closing the loop between human experts and computational algorithms for the assessment of movement disorders

Clinical assessment of abnormal neuromechanics is typically performed by manipulation of the affected limbs; a process with low inter- and intra-rater reliability. This paper aims at formalizing a framework that closes the loop between a clinician’s expertise and computational algorithms, to enhance the clinician’s diagnostic capabilities during physical manipulation. The framework’s premise is that the dynamics that can be measured by manipulation of a limb are distinct between movement disorders. An a priori database contains measurements encoded in a space called the information map. Based on this map, a computational algorithm identifies which probing motions are more likely to yield distinguishing information about a patient’s movement disorder. The clinician executes this movement and the resulting dynamics, combined with clinician input, is used by the algorithm to estimate which of the movement disorders in the database are most probable. This is recursively repeated until a diagnosis can be confidently made. The main contributions of this paper are the formalization of the framework and the addition of the information map to select informative movements. The establishment of the framework provides a foundation for a standardized assessment of movement disorders and future work will aim at testing the framework’s efficacy.
Acoustic Signal Positioning and Calibration with IMU in NLOS Environment

Acoustic signal has become a research hot pursuit of Indoor Location Based Service (ILBS) ascribed to its low synchronization rate, excellent performance and low cost, especially in large-scaled complicated indoor environments. Nevertheless, sound can easily be blocked and absorbed, producing non line-of-sight (NLOS) phenomenon. It brings great challenges to acoustic indoor positioning technology. In order to deal with NLOS, we introduce an Inertial Measurement Unit (TMU) to calibrate the huge error caused by shelters. Estimating the approximate result under NLOS based on the pedestrian posture calculated by adopting pedestrian dead reckoning (PDR) algorithm is a good choice. The coordinate is fed back to acoustic system to obtain the accurate position information. According to numerous of experiments, we achieve an accuracy of approximately 30 centimeters with 95% probability in NLOS positioning error within 50 meters from the anchor point (AP).
A Hybrid Machine Learning Approach for Prediction of Heart Diseases

Heart diseases are the chief cause of death all over the world over the last few decades. To avoid heart disease or coronary illness and discover indications early, individuals over 55 years must have a total cardiovascular checkup. Researchers and specialists developed various intelligent techniques to improve capacity of the health care professionals in recognition of cardiovascular disease. In cardiovascular disease finding and treatment, single data mining strategies are giving the reasonable precision and accuracy. Nevertheless the usage data mining procedure be capable of reducing the number of test that is required to be carried out. In order to decrease the Figure of deaths from heart diseases there has to be a quick and efficient detection technique providing better accuracy and precision. The aim of this paper is to present an efficient technique of predicting heart diseases using machine learning approaches. Hence we proposed a hybrid approach for heart prediction using Random forest classifier and simple k-means algorithm machine learning techniques. The dataset is also evaluated using two other different machine learning algorithms, namely, J48 tree classifier and Naive Bayes classifier and results are compared. Results attained through Random forest classifier and the corresponding confusion matrix shows robustness of the methodology.