Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of periodically changing load on Li-ion battery cells

The aim of our research is to identify the effect of periodically changing load on the available capacity and state-of- health of a selected battery cell. In this paper the discharge current changed between 1C and 2C, so the profile was created by the switch of the load. Two different measurements were performed, with both periodically changing resistance and current. For comparison a reference cell was also measured with constant resistance and discharging current. In case of the constant current it was 1C while the constant resistance was calculated so that the current of the cell was around 1C. The effects of the ambient temperature were the same to both cells, and to obtain more information on the behavior of measured cells, it was recorded as well. During the measurement, effects of four different switching frequencies were examined on the available capacity and state-of-health. Altogether ten measurements were performed in case of each switching frequency, each of them preceded by a reference measurement with 1C discharge to compare the capacity change of the reference and measured cell. As the discharge capacity is affected by the charging current both cells were charged in the same way with 1C constant current-constant voltage method.
Probabilistic sensor design for healthcare technology

In healthcare technology, failure to accurately detect a medical event, such as electromyographic (EMG) activity or the onset of a heart attack, could have serious consequences for the user. The application of sensor networks to optimize event detection is therefore a key area of biomedical engineering research. The optimum number of sensors for event detection can be difficult and time-consuming to determine experimentally. A novel probabilistic model could be used to estimate the appropriate number of sensors required and is described in this paper. A simple statistically independent model (naïve approach) is introduced, which may be useful but relies on a set of assumptions that do not reflect most real-world applications. From there, a more practical, revised model that can be more easily applied to real-life systems was developed. Both models were subsequently tested against experimental data from a real EMG system. While the naïve approach was practically unachievable, the model was found to be mathematically sound. Predictions from the models were compared and the revised model was found to be more accurate than the naïve model. The revised model was verified against a majority voting system and it successfully predicted sensor network performance with only a small margin of error. This work presents a probabilistic sensor network approach to virtually exploring possible networks and informing optimum design of sensor networks used in healthcare and industry.
From Monolithic Architecture to Microservices Architecture

The purpose of this work is the definition of a strategy, still in early stage, that will be able to support the migration from a Monolithic Architecture to a Microservices Architecture. This strategy aims to be applied to monolith systems, encouraging their evolution into microservices-based systems. Using this migration strategy, the newborn system will take advantages of a number of benefits offered by microservices architecture, such as scalability and maintainability. Companies will be able to migrate their old monolith systems into more flexible microservices-based systems, evolving their software in a more powerful one.
Multilayer Slot-Coupler Based On Integrated Substrate Gap Waveguide

A multilayer slot-coupler based on integrated substrate gap waveguide (ISGW) is proposed. The proposed coupler is consisted of two ISGWs, which are broadside coupled through fish bone shape slot in the common ground. Simulation shows that the proposed coupler can have either $-3\pm 1\ \text{dB}$ tight coupling from 23.07 to 28.55 GHz or $-6\pm 1\ \text{dB}$ tight coupling from 24.48 to 30.73 GHz. Moreover, the phases between through and coupled port differ by 90°. The return loss and isolation are less than −20 dB over the operating frequency band.
Unsupervised Change Detection in Remote Sensing Images using Multi-View Learning

The geospatial objects laying on the earth surfaces are changing rapidly due to various reasons. The multi-temporal remote sensing images are widely used data sources to capture changed occurred at a particular location. Change detection is the process to determine disagreement in the nature of geospatial objects of the same location from different time's instances. Unsupervised change detection is an approach to determine the changed and unchanged state of a location without using any label information. The remote sensing images are captured in multiple spectral bands and the extraction of spatial, texture and shape features in each band increases the dimensionality of the data sets. The conventional change detection techniques may perform poor for the high dimensional remote sensing data sets. In this work, a multi-view learning based unsupervised technique has been developed for the unsupervised change detection in temporal remote sensing images. The experimental results on the two benchmark data sets show that the proposed unsupervised multi-view approach has performed better than single view unsupervised change detection techniques.
Li-Ion Battery Parameter Identification Using Pseudo Random Noise

This paper identifies the battery parameters using direct assessment approach in which impedance spectroscopy is considered to find parameters. This technique uses Randles' equivalent battery model in Simscape MATLAB, Battery parameters including series resistance, charge transfer resistance and double layer capacitance are found through Electrochemical Impedance spectroscopy. Input current is fed as square charging and discharging pulses. Battery has dynamic behavior in which all parameters in a battery tends to change with temperature and its operating conditions, that change in parameters predict a battery's life and other characteristics.
UGC Online Learning Proactive Caching Strategy for Multi-levels Wireless Networks

User Generated Content (UGC) increases rapidly owing to the development of mobile Internet. Proactive caching can unload popular content flow and reduce content request delay to improve QoS. The effectiveness of proactive caching depends on the imformation about content popularity. In this paper, we suggest a UGC online learning proactive caching strategy. In this strategy, with the utilize of multi-level online learning and user clustering, the preference similarity among cache group members is improved, thus improving online learning efficiency and cache hit ratio. Simulation results demonstrate the effectiveness of the proposed method.
Fault Diagnosis of Nuclear Power Plant Based on Simplified Signed Directed Graph with Principal Component Analysis and Support Vector Machine

To solve the problem of low efficiency of signed directed graph (SDG) model in fault diagnosis, the model is divided into layers and blocks, and a simplified signed directed graph (SSDG) model for fault diagnosis is obtained to improve the diagnosis speed. Then a hybrid method is proposed, which uses principal component analysis (PCA) for fault detection, SSDG model for path reasoning to determine the cause of fault and support vector machine (SVM) for judging the severity of the fault. The method is validated by the data of Personal Computer Transient Analyzer (PCTRAN) simulation platform. Experimental results show that the proposed method can not only diagnose the fault accurately, but also improve the efficiency of diagnosis.
Test Case Prioritization Based on Path Complexity

Software undergoes many modifications after its release. Regression testing is performed to ensure that the modification has not introduced any errors in the software and the software continues to work correctly. Regression testing is an expensive process. Three types of cost reduction techniques are used in regression testing. These techniques i.e., test case selection, test suite minimization and test case prioritization are used to reduce the cost of regression testing and improve the rate of fault detection. The focus of our research is on test case prioritization. Instead of minimizing test suite or selecting fewer test cases, test case prioritization orders test cases in such a way that the test cases detecting more faults are executed earlier. In case of limited resources, only top priority test cases are executed to ensure the reliability of the software. In this research, we have proposed an approach which uses path complexity and branch coverage to prioritize test cases based on assumption that the complex code is more likely to contain faults. Halstead's metric has been used to calculate the path complexity of the test cases. Proposed approach is compared with branch coverage based prioritization technique using some example programs. The results show that proposed approach outperforms existing branch coverage based approach in terms of APFD (Average Percentage of Faults Detected) up to 18% on average.
Discriminative Sparse Generalized Canonical Correlation Analysis (DSGCCA)

Multi-view learning (MVL) is a strategy for fusing multi-view data, which has better generalization performance than single-view learning algorithms. Canonical correlation analysis (CCA) is a representative multi-view subspace learning approach, which plays an important role in MVL classification and information retrieval. Traditional CCA can only be used to calculate the correlation of two views, and the learned features are usually dense. Moreover, it is unsupervised, and hence wastes label information in supervised learning. To overcome these limitations, this paper proposes discriminative sparse generalized CCA (DSGCCA), which integrates generalized CCA to handle more than two views, and supervised discriminative sparse principal component analysis to make use of the label information. DSGCCA can handle small multi-view datasets with high feature dimensionality and any number of views. Experiments on four classification datasets demonstrated that DSGCCA outperformed several other representative CCA-based MVL approaches.
The Classification Detection Technology of Car Quarter Glass Based on the Feature Parameter

Background: At present, the classification detection of car quarter glass is mainly depends on human’s experiences for some factories. However, some kinds of car quarter glass are not only very similar in black-band contour but also very similar in terms of material (color). This manual method for identification is prone to make mistakes. Although people have done large amounts of research on the defect detection of car glass, there are few studies on the classification detection of the car quarter glass with black band. In this paper, we innovatively propose the use of machine vision technology and feature parameter (e.g., perimeter, area and HU moment) comparison to realize the contour detection of the quarter glass. Then, by using the light transmittance as characteristic parameter, the material detection of the quarter glass can be realized by combining this characteristic parameter with the using of rejecting abnormal values. A combined utilization of contour detection and material detection can effectively guarantee the reliability of quarter glass classification detection. By using different materials and specifications quarter glass to test in the experiments, the experimental results show that almost all of the correct detection can be obtained no matter what position and angle of the glass be placed. Even if an incorrect glass which is much similar to the standard glass can still detected to show as error. The quarter glass classification detection above prove that this method has a nearly 100% accuracy and the robustness of algorithm are significant.
Path Tracking Control of Wheeled Mobile Robot Based on Improved Pure Pursuit Algorithm*

In the research on the path tracking control of wheeled mobile robot, path tracking accuracy of Pure Pursuit algorithm is poor. The tracking effect of Pure Pursuit algorithm is affected by the preview distance Ld. To solve this problem, an improved Pure Pursuit algorithm is proposed, and a rule of adaptive adjustment of preview distance is designed. Select a temporary preview point $P_{g}'$;. The preview distance Ld can be adjusted adaptively according to steering angle $\alpha$ and deflection angle $\theta$. The tracking path is adjusted by constructing two tangential circles Simulation results show that when tracking the desired path of a straight line, the average displacement error and average course angle error of the improved Pure Pursuit algorithm are 0. 1342m and 0. 0559rad respectively. When tracking the expected path of the curve, the average displacement error and average course angle error of the improved Pure Pursuit algorithm are 0. 1732m and 0. 0576rad respectively. Compared with similar algorithms, the improved Pure Pursuit algorithm has smaller tracking error and no obvious oscillation phenomenon.
A Non-Restricted Measuring System for Human Step Length Estimation

In this paper, a non-restricted human step length measuring system has been derived by deploying the wearable inertial measurement sensor on subjects’ foot. The measuring errors caused by the unwanted data noise, sensor drift as well as uncompensated gravity are discussed in detail. Based on the error model analysis, an efficient MARG orientation estimation method and the LRT zero-velocity update strategy have been utilized to mitigate this error induced effect. Subsequently, the procedure of estimating the step length of human ambulation is described, and the experimental assessment with subjects has also been conducted for verifying the robustness and the practicability of the proposed method. The experiment result has been statistically analyzed and shows the promising future to patients’ gait clinical evaluation, human-oriented robot control as well as self-contained human movement tracking.
Enhancing Environmental Sensing Capability of AFM-based Nanorobot Via Spiral Local Scan Strategy

Atomic force microscopy (AFM) based nanorobotic technology has been widely implemented in light of the overwhelming advantages, such as nanometer spatial resolution, adaptability to various ambient, and numerous advanced measurement approaches. It is noted that even though the AFM possesses nanometer imaging resolution, it is hard to achieve nanometer tip locating precision due to complicated uncertainties, especially the nonlinearity of tip-environment interaction and the random drift influence. Since an AFM image is typically utilized as a map for nanomanipulation, the uncertainty distorted image will definitely introduce location deviation between the real and the captured nano-world, which typically leads to low efficiency or even failure of tasks. Besides, complicated tip-environment interaction is generally hard to model and to make accurate prediction, which will also lead to task failure. Therefore, to achieve highly accurate operation at the nanoscale, environmental sensing capability of AFM-based nanorobot should be promoted. In this paper, we propose a local environment sensing approach to detect positioning uncertainty between nanorobot tip and its surroundings by developing a multi-functional spiral local scan (MSLS) strategy comprised of structured objects location detection function and local surroundings imaging function. Briefly, sphere/cylinder-like object location detection strategies were proposed; a tip motion predictor was developed to tackle the heavy noise issue of detection tasks at dozens of nanometers scale, based on which a local area imaging approach was established. Efficiency of the MSLS method was verified through experimental study.
Development of an Answer Verification System for Helping Visually Impaired People to Learn a Spreadsheet Software

We have developed an answer verification system for helping visually impaired people who are learning how to use a spreadsheet software by answering problem-solving exercises. Visually impaired learners have difficulty in verifying their answers on their own with conventional software tools. Hence, the answer verification system is designed to provide feedback to the learners with respect to the numbers of errors in their answers, the error locations, and the reasons behind the errors. The messages output from our system can be read out by a screen reader, and our system can treat various kinds of problem-solving exercises of a spreadsheet software. We conducted an evaluation study of our system both with the teachers and the students to assess its usability and functionality. Based on the results of this study, we generated an improved version of our system, which is also described here.
Implementation of Motion Cueing and Motor Position Control for Vehicle Simulator with 4-DOF-Platform

Vehicle simulator is used for various purposes, mainly driver training and vehicle model test. One of the most important part of vehicle simulator is motion simulator which simulates the vehicle motion. This part makes the user feel the motion sensation given by the real vehicle even though the user is in the simulator platform. The motion simulator itself consists of several subsystems : user interface, dynamic model calculation, motion cueing, and platform control system. This paper explains the implementation and its result of designed motion cueing and the motor position control which is a part of platform control system. The design is implemented on vehicle simulator in Institut Teknologi Bandung which has 4 degrees of freedom for its motion (pitch, roll, sway, and surge). The implemented motion cueing algorithm (MCA) is model predictive control (MPC), an optimization-based motion cueing algorithm. Sliding mode control (SMC) with saturation function is implemented for position control of the motor to solve nonlinear load torque disturbance which appear from a static behaviour when the platform rotates on pitch motion. From the motion cueing result, it can be inferred that MPC-based MCA can track the motion sensation of the real vehicle, especially for the surge and sway motion. For pitch and roll sensation, reference signals with lower frequency yield worse results compared to the signals with higher frequency ones. Meanwhile, from the motor position control result, it can be concluded that SMC with saturation function can track the position reference according to the calculation of motion cueing.
Remote Sensing Image Registration Based on Spatial Transform Network and Phase Correlation Method

Remote sensing image registration technology can weaken or even eliminate image position, size difference and deformation caused by imaging equipment, viewing angle and other influencing factors. It has been widely used in urban planning, ecological monitoring and land management. In this paper, the spatial transformation network(STN) model is used to extract the image features and train to obtain the affine transformation coefficients, so that the image to be registered can be adaptively transformed with reference to the coefficients to achieve the initial registration purpose. In order to obtain a more accurate registration effect, we use the phase correlation algorithm to calculate the phase shift of the frequency domain in the frequency domain caused by the translation of the two image spaces in the initial correction result. Then the relative motion vectors of the two images are obtained, and the entire registration phase is finally completed.
An Efficient Flash Memory Devices

Now a day's, all the electronic gadgets Primary concern on memory requirement. It's a great challenge to fulfil the requirements of the consumers based on memory design on non-volatile devices. But Power Consumption is the main constraint to design flash memories. So different authors proposed different designing strategies on NAND and NOR array based flash memories because of we can write data many time, when there is no supply. In this paper we verified MOS transistor based NAND and NOR array operation and we also verified by using FinFET based NAND and NOR array. By comparing the parameters of Power Consumption, Propagation Delay and Leakage Power we concluded that FinFET based array systems have efficient performance compared CMOS based system.
A Novel Switched Coupled Inductor DC-AC inverter

This paper presents, the simulation of a single phase single stage switched coupled inductor DC-AC inverter. The proposed inverter has common ground between DC input and output AC, which is very useful for photovoltaic inverter system. The Z source and q-Z source inverter have high voltage gain compared with conventional inverters and the proposed inverter have higher voltage gain than the Z source and q-Z source inverter The proposed inverter also has very high efficiency as compare to q-Z source inverter. The proposed inverter uses coupled inductor, due to coupling of inductor the maximum current flowing and volume of inverter can be reduced effectively. The MATLAB simulation, operating principle is discussed in this paper to verify performance of the inverter.
Consensus design with guaranteed cost for generic linear multi-agent systems subject to control input constraints

This paper investigates the guaranteed cost consensus design problem for generic high-order multi-agent systems subject to control input constraints. Firstly, an intermittent consensus control protocol is constructed with intermittent local information, where the related weighted quadratic cost index is considered to trade off the consensus performance against the energy expenditure while achieving consensus. The guaranteed cost consensus analysis criterion is given by using linear matrix inequality methods. Meanwhile, a guaranteed cost is derived to show the upper bound of the cost index. Then, by the changing variable approach, the guaranteed-cost design criterion is presented. It is proved that multi-agent systems with control input constraints realize guaranteed cost consensus via designing the control gain of the consensus control protocol with the related cost index. Finally, a numerical simulation is provided to verified the effectiveness of the derived theoretical results.
An Improved Feature Selection and Classification of Gene Expression Profile using SVM

Support Vector Machine (SVM) is the most widely used classifier for performing the classification of a massive dataset. This research paper aims to improve the feature selection and classify the gene expression data by using the SVM classifier. And also aim to decrease the computational time of the SVM-RFE (support vector machine recursive feature elimination) algorithm by identifying more than one redundant genes and removing them in every iteration. Most of the gene expression profile contains an enormous number of features with few numbers of samples, to reduce the number of features before applying to the classifier for performing the classification; a feature selection algorithm is needed. The most effective algorithm used to perform the feature selection of microarray is, SVM-RFE. On every iteration, it generates the rank of the features and removes the very least ranked feature, which is the most irrelevant. Since the modified algorithm is used to remove more than one redundant features in every iteration. It will help to reduce the computational time and increase the accuracy prediction.