Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multilingual Decision Support System for early detection of Diabetes using Machine Learning approach: Case study for Rural Indian people

More than 77 million people in India are influenced by diabetes mellitus and a significant number of them are under risk with specific complications, for instance cardiovascular failure, stroke, nerve infection, etc., The prevalence ratio of diabetes is high in urban areas due to the migration of rural people and industrialization. While considering diabetes in prosperous urban, it has become a grave anxiety among rural people also. Early diagnosis and proper therapeutic management may reduce the expenditure and mortality rate. however, the cost of early diagnosis and laboratory testing is very high. To provide a user-friendly and cost-effective system, this paper proposes a multilingual decision support system by integrating the best predictive model (among various machine learning algorithms) and clinical decision support system. The proposed system provides a user interface to assess diabetes by themselves or with a nursing assistant available in primary health centre.
Condition Monitoring of Three-Phase Induction Motor

Analysis of stator current and vibration of induction motor has long been used, in researches as well as industries, for the condition monitoring purpose. This paper presents the hardware results of three phase induction motor condition monitoring by current and vibration analysis, presenting one novel diagnosis method while analyzing the frequency spectra. Here current and vibration data are collected with the help of Digital Storage Oscilloscope and Vibration Analyzer respectively. These collected data are used to obtain frequency components of current and vibration with the help of MATLAB program. Later, the set of the most dominating frequency components of current and vibration are compared with that of healthy motor in order to establish the healthy/faulty condition. A 3 HP, 2.2 KW, three-phase induction motor has been used for analysis purpose. Three types of major faults, namely, stator inter turn short, rotor broken bar and bearing defect are considered. The proposed method proves to be quite successful in predicting rotor and bearing faults, while not being so successful in detecting stator inter-turn faults.
Design and Analysis of High Speed Dynamic Comparator for Area Minimization

Comparators are basic building block for analog to digital converter (A/D converter) as well as many mixed signal processing circuits. Therefore, it is very essential to improve its power dissipation and speed, keeping the chip area as compact as possible due to the increasing demand of portable devices. This paper discusses, a modified latched comparator and its characteristics are compared with the conventional models from different literature. The proposed circuit is simulated using Spectre Circuit Simulator tool-Cadence. The target is to achieve low power consumption with low delay profile in a lowest possible chip area. Feature size is considered to be 90nm. Power and delay are measured and compared with other comparators that were proposed earlier for representing the improvement of proposed model. Its power dissipation is 33.5 μw and delay is 54.32ps. The chip area is found to be (8.83*8.61) μm2. Also, it has low parasitic effect and low noise in comparison with those conventional models.
Controlled freezing and freeze drying: a versatile route for porous and micro‐/nano‐structured materials

Freeze drying is a process whereby solutions are frozen in a cold bath and then the frozen solvents are removed via sublimation under vacuum, leading to formation of porous structures. Pore size, pore volume and pore morphology are dependent on variables such as freeze temperature, solution concentration, nature of solvent and solute, and the control of the freeze direction. Aqueous solutions, organic solutions, colloidal suspensions, and supercritical CO2 solutions have been investigated to produce a wide range of porous and particulate structures. Emulsions have recently been employed in the freeze drying process, which can exert a systematic control on pore morphology and pore volume and can also lead to the preparation of organic micro- and nano-particles. Spray freezing and directional freezing have been developed to form porous particles and aligned porous materials. This review describes the principles, latest progress and applications of materials prepared by controlled freezing and freeze drying. First of all the basics of freeze drying and the theory of freezing are discussed. Then the materials fabricated by controlled freezing and freeze drying are reviewed based on their morphologies: porous structures, microwires and nanowires, and microparticles and nanoparticles. The review concludes with new developments in this area and a brief look into the future. Copyright © 2010 Society of Chemical Industry
Leveraging Social Networks to Enhance Effective Coverage for Mobile Crowdsensing

With the development of the Internet of Things and smart city, the demand for mobile crowdsensing (MCS) is increasing. Most state-of-the-art studies in MCS assume that the participants are those who have registered with the MCS platform. In this paper, we propose to exploit social network for MCS worker recruitment instead of limiting participants to the platform. MCS platform can motivate more users to join in the task by leveraging the social influence of seed workers. Inspired by this, we first propose a social influence propagation model for MCS task. Considering the constraint of budget, our objective is to maximize the effective sensing coverage by selecting a limited number of seed workers, which is formulated as MESC problem. Based on the voting theory, a heuristic algorithm named as KT Voting is proposed to select seed workers. KT Voting algorithm allows users to vote for the most influential user to themselves and add a weight to their vote based on their sensing locations. After that, seed workers are selected based on the votes received. Extensive experiments based on two real-world data sets verify the effectiveness and efficiency of the proposed KT Voting algorithm.
A Piecewise Direct Instantaneous Energy Control Strategy for DC-link Capacitor and Torque Ripple Reduction in Multi-phase Switched Reluctance Motor Drive Systems

In this paper a Piecewise Direct Instantaneous Energy Control (PDIEC) strategy is proposed for torque ripple and DC-link capacitor reduction in Switched Reluctance Motor (SRM) drive systems. DC-link energy flow in a conventional SRM drive system contains high frequency switching harmonics, as well as commutation induced low frequency harmonics, which are caused by magnetization of the incoming phase and demagnetization of the outgoing phase during a commutation period. If the SRM drive is controlled to produce constant output torque, the question of how to reduce the low frequency ripple of the input energy is equivalent to that of how to eliminate the low frequency harmonics in the stored field energy of the SRM. Therefore, based on the Direct Instantaneous Torque Control (DITC) technique, a new control technique is proposed to control the stored field energy and the instantaneous torque at the same time.
Correlating Psychophysiological Responses of Exergaming Boxing for Predictive Heart Rate Regression Models in Young Adults

The purpose of this article is to validate the correlations between the heart rate (HR) and rating of perceived exertion (RPE) during exergaming boxing in sitting and standing positions. Thirty healthy adults, whose mean age was 25.10 and standard deviation (SD) 2.95 years, were recruited to play 10 minutes of exergaming boxing in sitting and standing positions. HR measurements were obtained at rest and during gameplay. RPE was assessed using Borg's modified (1–10) and original (6–20) scales. A paired-sample t-test, bivariate Pearson's, and Spearman's rho correlations were used to analyze the results. Mean HR was significantly higher while exergaming in standing position (130.79 SD23.18 bpm) as compared to sitting position (116.46 SD19.08 bpm) (p $\leq 0.05$). There was a significant correlation between HR and RPE values while playing boxing exergaming in the standing position. A regression model that can be fitted into an equation to predict HR from reported RPE was derived from the significant values of Pearson's correlations. However, HR and RPE values did not significantly correlate with each other during exergaming boxing in the sitting position. The formula extracted from the linear regression models provides reliable predictions in estimating HR from the reported RPE while exergaming boxing in the standing position.
A New Multilevel Inverter for Grid Integration of Renewable Energy Sources

As power generation from renewable energy sources (RES) are increasing gradually, the importance of multilevel inverter is also rising day by day for the grid integration of power generated from RES. In this research, a new multilevel inverter (MLI) based on series-connected switched-sources is presented. The proposed MLI requires less number of sources and switches to produce multilevel output. The cost, size, and maintenance are reduced as well as the performance is improved due to the less number of circuit elements. In addition, the proposed MLI is capable of being connected to the grid according to IEEE standard 519. Renewable energy sources can be connected as the source of this MLI. The operation of this proposed MLI is explained by using a fifteen level inverter and the MLI model was verified by simulating in MATLAB Simulink.
Secure Information- and Energy-efficient Wireless Networks With Packet Transmission Creation

The proposed improvement of information and energy efficiency of wireless networks with packet transmission of monitoring signals and images based on the implementation by the subscriber systems is discussed. There is a complex of complementary algorithms for operative processing, encoding of signals samples and frames of video data, crypto protection of data arrays and the formation of information secure packets with increased information capacity. For accurate and compact encoding of signals samplings and video data frame without involving power-consuming codecs and video codecs, it is proposed to determine the most informative essential samples of signals or the video data frames pixels. They are including extremes and inflection points of signals and video images, accurate encoding and recovery of amplitude timing parameters of essential samplings and pixels. The received data streams are subject to compact encoding, lossless compression and one-time encryption protection. The basis of the packets is formed by code-signal sequences that transmit multi-bit sequences and the structure. The base of the sequences is selected depending on the operatively determined desired signal-to-noise ratio. The proposed information technology is focused on the effective sensor, local-regional, microsatellite and Internet of Things networks creation.
Report Search Function Using TF-IDF for PBL Education

The 21st century works need both ‘hard’ skills (traditional domains, such as, history, mathematics, science) as well as ‘soft’ skills (teamwork, disciplined thinking, creativity, social skills, computer literacy and analyze information). KIT and ICT have committed to on-line PBL education to educate students on the skills they need. Accurately grasping the learning situation of each student and promoting his growth by giving appropriate advice is effective for student’s growth, and AI can be utilized to achieve this. We propose the on-line PBL education system using AI. Especially, a past activity case presentation system using AI is explained in detail.
Photosensitivity of the Er/Yb-Codoped Schott IOG1 Phosphate Glass Using 248 nm, Femtosecond, and Picosecond Laser Radiation

The effect of 248 nm laser radiation, with pulse duration of 5 picoseconds, 500 femtoseconds, and 120 femtoseconds, on the optical properties and the Knoop hardness of a commercial Er/Yb-codoped phosphate glass is presented here. Refractive index changes of the order of few parts of 10-4 are correlated with optical absorption centers induced in the glass volume, using Kramers-Kroning relationship. Accordingly, substantially lower refractive index changes are measured in volume Bragg gratings inscribed in the glass, indicating that, in addition to the optical density changes, volume dilation changes of negative sign may also be associated with the 248 nm ultrafast irradiation. The Knoop hardness experimental results reveal that the glass matrix undergoes an observable initial hardening and then a reversing softening and volume dilation process for modest accumulated energy doses, where the Knoop hardness follows a nonmonotonic trend. Comparative results on the Knoop hardness trend are also presented for the case of 193 nm excimer laser radiation. The above findings denote that the positive or negative evolution of refractive index changes induced by the 248 nm ultrafast radiation in the glass is dominated by the counteraction of the color center formation and the volume modification effects.
An Improved Channel Estimation Method for Cell-Free Uplink Systems With Scalar Quantizers

The channel estimation problem for the uplink of a cell-free multiple-input multiple-output (MIMO) system is studied. Standard approach for fronthaul quantization and channel estimation is reviewed first, and its limitation is discussed. To improve the channel estimation accuracy, it is proposed that, at each time instant, each radio unit (RU) quantizes a linear combination of the currently and previously received samples. To maximize the impact of linear combination, i.e., pre-processing, we tackle the problem of jointly optimizing the pilot sequences, pre-processing matrices and linear estimation filters with the goal of minimizing the sum of mean squared errors (MSEs) of channel gains. Numerical results validate the performance gains of the proposed channel estimation scheme with the optimized pre-processing matrices.
Location-aware Task Assignment and Routing in Mobile Crowd Sensing

With the increasing number of smart devices, se-lecting proper workers for tasks is a major research challenge in mobile crowd-sensing (MCS) due to the heterogeneity of tasks and workers. In this paper, we formulate the task allocation problem as an optimization problem by taking into account various tasks requirements (i.e., sensor, location accessibility, reliability), and workers specifications (i.e., available sensors set, velocity) as well as task completion order. The objective of this study is to minimize the total distance moved by all workers. Furthermore, a tabu search algorithm is proposed to select workers, which uses asymmetric traveling salesman problem heuristics to find the task completion order. Simulations using real data sets show the performance of our proposed method.
Parallel stratified random testing for concurrent programs

For a concurrent program in Java, the reachable state space from each initial state is divided into <tex>$L$</tex> layers such that each layer depth is <tex>$D_{l}$</tex> for <tex>$l=1, \ldots, L$</tex>. If the program is exhaustively tested for each layer and there are <tex>$m$</tex> states at depth <tex>$D_{1}+\ldots+D_{l}$</tex>, then there are <tex>$m$</tex> sub-state spaces in layer <tex>$l+1$</tex> and each of the <tex>$m$</tex> sub-state spaces is exhaustively tested. Instead of exhaustively testing all the <tex>$m$</tex> sub-state spaces, we use a percentage <tex>$\delta_{l}$</tex> % for each layer <tex>$l$</tex>, randomly select about 0.01 <tex>$\times\delta_{l}\times m$</tex> ones among the <tex>$m$</tex> states at depth <tex>$D_{1}+\ldots+D_{l}$</tex> and test only about <tex>$0.01\times\delta_{l}\times m$</tex> sub-state spaces in layer <tex>$l+1$</tex>. For a Java implementation of the NSPK authentication protocol, even though the number of layers was 2 and each layer depth was 100, it did not complete in 3 weeks to exhaustively test the concurrent program. On the other hand, if the number of layers was 3, each layer depth was 100 and the percentages for layer 1 & layer 2 were 0.05% (or 0.1%) & 0.05% (or 0.1%), respectively, it completed in 19h to randomly test the concurrent program.
Unsupervised Algorithm for Brain Anomalies Localization in Electromagnetic Imaging

A brain anomaly localization algorithm in an unsupervised machine learning (ML) framework is presented for electromagnetic brain imaging. The method is based on expected value estimation and takes the advantage of the highly symmetrical human brain. The algorithm processes signals collected from pairs of antennas that are positioned symmetrically around the head, discretizes the imaging domain into pixels, and computes the statistical fields between the antennas on the left and right sides of the head. Then, it concatenates their intensities along the axis normal to the imaging domain to compute the expected value for every pixel. The computed expected values are merged into a matrix containing expected values for all pixels. Pixels with higher intensity show the likelihood of an anomaly being present at that location. The assumption on brain symmetry from the electromagnetic perspective was tested on healthy volunteers using a 14-element array system with a working frequency band of 0.5 - 2.0 GHz. The obtained average similarity is 92% and it confirms the validity of the assumption. The same system is used to test the algorithm on different scenarios in simulations and experiments using realistic 3D head phantoms designed based on MRIs of real patients. The imaging results demonstrate the capability of the proposed algorithm to localize bleeding and estimate its size with less than 10% error in less than a minute, which makes it suitable for real-time use in emergency stroke scenarios.
Making Sense of Occluded Scenes using Light Field Pre-processing and Deep-learning

A combined approach of low-complexity light field depth filtering and deep learning is proposed for object classification in the presence of partial occlusions. The proposed approach exploits depth information embedded in multi-perspective four-dimensional (4-D) light fields via low-complexity 4-D sparse depth filtering and deep-learning. The proposed 4-D depth filter, designed using numerical optimization techniques by formulating as an ℓ1 − ℓ∞ minimization problem, is shown to outperform typical light field refocusing based on 4-D shift-sum averaging filters. Experiments conducted using a light field dataset acquired by a Lytro camera verify 45% and 27% better performance in terms of object classification accuracy compared to the cases when no depth filtering is employed and standard shift-sum refocusing is employed, respectively.
PITI: Protecting Internet of Things via Intrusion Detection System on Raspberry Pi

Nowadays, Internet of Things (IoT) is a ubiquitous technology that connects a large number of devices together for people to be more convenient from using of new technologies. However, having these devices connected to the Internet may expose them to various risks malicious and suspicious attacks. In addition, after the device installation, these devices normally do not receive full security measurement, resulting in becoming the attacking and intruding targets. This research studies and focuses on the security threats in smart home networks by using Raspberry Pi which is low cost and portable. Our system, which is called PITI, performs hybrid detection by combining Snort IDS and behavior-based profile analysis together in order to efficiently detect malicious and suspicious traffic. The current version of PITI can alert anomaly traffic by showing a message on the screen and also alert by sounds. For the future work, PITI device should be developed into Intrusion Prevention System (IPS) which can drop the suspicious packets and detect more type of attacks.
Implementation and Analysis of Routing Protocols for LoRa Wireless Mesh Networks

In a world getting more and more connections, the growth in the number of devices sharing information with each other is evident. Therefore, as the number of connected devices and their applications grows, the amount of data shared by these devices also increases considerably. Thus, in order to be able to send data from one device to another and that data passing through intermediate devices, it is necessary to define from which path the data should be sent, characterizing the routing concept. In this context, this work aims to implement and analyze routing protocols for ad hoc wireless network called DVR (Distance Vector Routing), AODV (Ad Hoc On-Demand Distance Vector) and DSR (Dynamic Source Routing) using the Cupcarbon network simulator. LoRa wireless communication technology and a network topology distributed along avenues in the city of João Pessoa, Brazil, were considered and simulated. The quantitative analysis criteria were the packet delivery rate, the average end-to-end delay and throughput. The energy consumption profiles of each network node were also obtained according to each protocol. The results showed that the DSR protocol had lower energy consumption and AODV obtained better general performance, but with higher consumption. The DVR protocol showed better results in terms of latency.
Load Balancing Scheme in Hybrid WiGig/LiFi Network

Recently, Light fidelity (LiFi) is proposed as a high-speed wireless communication technology. A LiFi access point provides the service in an area of a few square meters known as LiFi attocells. Therefore, by utilizing frequency reuse, LiFi networks provide a high spatial spectral efficiency. Unfortunately, beside to uplink and mobility problems, the LiFi networks suffer more difficulties with increasing of the number of mobile devices. As a solution, hybrid LiFi and radio frequency (RF) networks are proposed. In this article, a hybrid network, that combines LiFi with RF Wireless Gigabit Alliance (WiGig) networks, is proposed. The WiGig access points will provide gigabit-per-second data rates as a result of the availability of large bandwidth at the millimeter wave (mmWave) frequency ranges. Such hybrid networks need an efficient load balancing (LB) scheme. In this article, two modified versions of the separate optimization algorithm (SOA) are proposed; Assign WiGig First SOA (AWFS) algorithm and Consecutive Assign WiGig First SOA (CAWFS) algorithm. The simulation results show that the two proposed algorithms offer better achievable data rates and outage probability performances compared with the SOA algorithm.
Arsenite and arsenate adsorption on ferrihydrite : Surface charge reduction and net OH- release stoichiometry

Arsenite [As(III)] and arsenate [As(V)] are highly toxic inorganic arsenic species that represent a potential threat to the environment and human health. Iron oxides including poorly crystalline oxides, e.g., ferrihydrite, play a significant role in controlling dissolved As concentration and limit the mobility and bioavailability of As(III) and As(V). Adsorption occurs by ligand exchange of the As species for OH2 and OH- in the coordination spheres of surface structural Fe atoms. The objective of this study was to evaluate H+/OH- release stoichiometry and changes in surface charge properties of the adsorbent during the adsorption of arsenite and arsenate on ferrihydrite in the pH range of 4−10. This information, which is not directly accessible through spectroscopic studies, provides important clues to bonding mechanism. While arsenate adsorption resulted in the net release of OH- at pH 4.6 and 9.2, arsenite adsorption resulted in net OH- release at pH 9.2 and net H+ release at pH 4.6. The amount of H+ or.