Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Power Quality Disturbance Using Neural Network

Electric power utilities have launched comprehensive data collection programs to evaluate the Power Quality (PQ) problems in their systems. A machine learning based classification and characterization of the power system data will help in dealing with the voluminous quantities of the monitored data. An Artificial Neural Network (ANN) based machine learning approach is developed for classifying the PQ disturbances in a power system. This paper illustrates how a pattern recognition neural network can classify power quality events on the voltage data at a 132 kV bus obtained from Maharashtra State Electricity Transmission Company Limited. Important measure of neural network performance is its ability to generalize, i.e. to respond properly to new data after training. It is tested on a set of new data through important performance measures like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), correlation coefficient, regression analysis, error histogram, confusion matrix and Receiver Operating Characteristic (ROC) of the neural network. The results obtained demonstrate the power of ANN in classifying the commonly encountered disturbances in power system like sags, swells and interruptions.
Performance Analysis of Lightweight Cryptographic Algorithms Simulated on Arduino UNO and MATLAB using the Voice Recognition Application

In the era of Big-Data and IoT devices, it is important to use, efficient lightweight cryptographic algorithms for security reasons. No security algorithm satisfies all our needs. It is important to analyze the performance of lightweight cryptographic algorithms. Here in this paper, we have selected most frequently used lightweight cryptographic algorithms on IoT platforms. To check the performance, we have simulated all the algorithms in MATLAB and also tested the same on Arduino UNO. To check the performance of all the algorithms we have used common hardware and software platform. In this paper, we have used a voice recognition application, which is common for most of the IoT devices. Performance has been measured using key parameters like length of key, plaintext, and block, number of rounds, speed, memory consumption and floating point numbers. The results of this research help the future researchers and users to select a proper lightweight cryptographic algorithm along with its platform to implement.
An Automated System of Sentiment Analysis from Bangla Text using Supervised Learning Techniques

Sentiment analysis has become a leading context for scientific and commercial market research in the field of machine learning. Currently, it’s a more prominent research field of Bangla language processing system as there are few research works regarding sentiment analysis for this language. In essence, sentiment analysis is an automated process of text mining to determine the emotion from a given text. By using sentiment analysis, a given text can be categorized into several emotions. This paper deals with six individual emotion classes-happy, sad, tender, excited, angry and scared. Here, we proposed two methods of machine learning techniques- Naïve Bayes Classification Algorithm and Topical approach to extract the emotion from any Bangla text. Proposed methods have been applied for both article and sentence level of scope. A comparative analysis of the performance between these two methods has been done, and the topical approach achieved the best performance for both levels of magnitude.
Optimal Path Finding and Route Descriptor with Congestion and Air Quality

Traffic monitoring plays a vital role in the management of routes. Dynamic vehicle routing based on online map service is being used for the convenience of vehicle routing for the traffic experiences. Google Maps API is well suitable for obtaining shortest route information, here we build a system that gives the textual description of a route with less congestion and pollution using Google map API and Ant Colony Optimization. Congestion can be evaluated on the path and sometimes an alternative route with less congestion can take you faster to your destination than the shortest route. The congestion detection is based on the speed of the vehicle at some point of time, there can be a lack of velocity information in the segment under consideration therefore the weather information about a region also help in the selection of a route. Ant Colony Optimization can be applied in finding the optimal path considering the parameters distance, congestion and air quality for pheromone updation.
Sensorless Control Applying Signal Injection Methodology on Modified Model of Permanent Magnet Synchronous Machine

This paper presents a novel estimation algorithm for speed and position of Permanent Magnet Synchronous Machine (PMSM) based on a High Frequency Injection (HFI) technique. The injected signal is added to the reference voltage applied to the PMSM stator through the Voltage Source Inverter (VSI). The invention in the proposed sensorless control combines the HFI methodology with a modified PMSM flux model expressed in the estimated rotating frame. In the current signal reconstruction, the introduced PLL topology has the advantage of reduced space harmonics. Only stator voltages and currents are required in the algorithm estimating modified stator and rotor magnetic flux. The control strategy principle is based on the advantage of PLL-type structure to successfully track the rotor flux position with the required stability and accuracy. Simulation results demonstrate the performance of the PMSM system verifying the effectiveness of the overall drive system in both steady state and transient response.
Study and Analysis of Various LFSR Architectures

In large scale integration process, lots of transistors are implanted on a single silicon substrate for setting up of intricate circuits. Accordingly, major problem of power dissipation comes into the picture. Linear Feedback Shift Register (LFSR) is a commonly used pseudo random sequence generator to generate random 1s and 0s. It is used in applications such as Built-in-self test (BIST), cryptography etc. LFSR’s are having high level of fault coverage so they are used in several coding schemes. In this paper, LFSR is implemented using Complementary Metal Oxide Semiconductor (CMOS), Gate Diffusion Input (GDI), modified GDI(mGDI) and Multi Threshold CMOS (MTCMOS) techniques in Cadence Virtuoso using 180nm technology. Simulations of these circuits are done and different parameters like power, propagation delay and area are compared. mGDI gives a reduction of 30%, 39%, 49% with respect to CMOS in power, delay and area respectively and a reduction of 6%, 14% with respect to GDI in power, and delay respectively. Thus, mGDI LFSR design can be employed for low power testing.
Lateral Leakage in Silicon Photonics: Theory, Applications, and Future Directions

This paper presents an overview of lateral leakage in silicon photonics due to polarization coupling between the guided mode of a ridge waveguide and unguided slab mode in the etched cladding. We explain the physical origins and provide insight into how this effect can be suppressed or harnessed. We show how lateral leakage can manifest as new resonant behaviour and explore this effect in the context of bound states in the continuum. We review a number of applications for devices based on lateral leakage and present outlook for a new generation of polarization manipulators, antennas and filters.
Development or Exacerbation of Head and Neck Dermatitis in Patients Treated for Atopic Dermatitis With Dupilumab.

The efficacy and safety of dupilumab, a humanized monoclonal antibody targeting the α subunit of the interleukin (IL)-4 and IL-13 receptors, has been assessed in adults with moderate-to-severe atopic dermatitis (AD). 1,2 Injection site reactions, nasopharyngitis, conjunctivitis, and transient increases in eosinophil counts from baseline were higher in the dupilumab groups than in the placebo groups in pivotal studies.1,2 A recent study by Zhu et al3 reported new regional dermatoses in 17 patients with AD treated with dupilumab, with facial area involvement in 14 cases, whereas Blauvelt et al4 reported an equal improvement of AD with dupilumab on different anatomic regions in a post hoc analysis of data extracted from 4 phase 3 clinical trials.
A Review of Intrusion Detection Systems for Industrial Control Systems

Industrial Control Systems are found often in industrial sectors and critical infrastructures to monitor and control industrial processes. Recently, the security of industrial control systems has gained much attention as these systems now exhibit an increased interaction with the Internet. In fact, classical SCADA systems are already lacking with security problems, and with the increased interconnectivity to the Internet, they are now exposed to new types of threats and cyber-attacks. Intrusion detection technology is one of the most important security solutions used today in industrial control systems to detect potential attacks and malicious activities. This paper summarizes previous work for Intrusion Detection Systems approaches in Industrial Control Systems and highlights challenges and opportunities in implementing such solutions. We believe that such insights are valuable for further research in the industrial security context.
Experimenting With Non-Interactive Range Proofs Based on the Strong RSA Assumption

Range proofs are proofs that a committed number <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> belongs to a range <inline-formula> <tex-math notation="LaTeX">$[a,b]$ </tex-math></inline-formula> for public constants <inline-formula> <tex-math notation="LaTeX">$a,b$ </tex-math></inline-formula>, without leaking any information about the value <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>. In this work, we evaluate and analyze the performance of existing techniques for range proofs based on the strong RSA assumption while varying the range sizes. We first group the techniques into two classes. Our experiments show that the first class, being built on finding sums of squares (e.g., Groth’s range proof), has sharply decreasing performance trends as the range size increases. Thus, solutions in this class seem to be useful primarily for small ranges. The second class, which relies on a direct proof (e.g., Boudot’s range proof), exposes that the performance degradation slopes are not as steep as the range size grows, compared to solutions in the first group. However, this class’s main drawback is that these methods require considerably more modular arithmetic than the first class. Concretely, the Groth and Boudot protocols achieve the best performance when the range sizes are less than and greater than 1410 bits, respectively. As part of this work, we consider an extension by combining the strong points of existing solutions and examine the result efficiency. Interestingly, however, our experimental results report that this extension outperforms either Groth’s or Boudot’s protocol for certain ranges, but there is no range for which the extension outperforms both.
Fine-Grained Classification via Hierarchical Bilinear Pooling With Aggregated Slack Mask

Extracting discriminative fine-grained features is essential for fine-grained image recognition tasks. Many researchers utilize expensive human annotations to learn discriminative part models, which may be impossible for real-world applications. Recently, bilinear pooling has been frequently adopted and has shown its effectiveness owing to its learning discriminative regions automatically. However, most bilinear pooling models still utilize the all convolutional part/region features for recognition, including those noisy or even harmful feature elements. In this paper, we devise a novel fine-grained image classification approach by the Hierarchical Bilinear Pooling with Aggregated Slack Mask (HBPASM) model. The proposed model generates a RoI-aware image feature representation for better performance. We conduct experiments on three frequently used fine-grained image classification datasets. The experimental results demonstrate that HBPASM achieves competitive performance or even match the state-of-the-art methods on CUB-200-2011, Stanford Cars, and FGVC-Aircraft, respectively.
Studying the Impact of Gamification on Learning and Engagement of Introverted and Extroverted Students

The gamification of education can help generate levels of involvement and dedication similar to games, enhancing specific skills, engaging students, and maximizing learning. However, some studies have shown detrimental results depending on the user's characteristics. It is still necessary to study the relationship between the user's profile, activity performed and the gamification elements applied. With this goal, this work incorporated gamification with points, ranking and badges into a programming learning environment to study the effects of these gamification elements on students' learning, behavior and en-gagement according to their personality trait of extroversion. An experimental evaluation was carried out during four months with 40 undergraduates students of first-year introductory courses on programming, in which half of the students used the gamified version of the learning environment and the other half the same learning environment with no gamification. We have found evidence that gamification affected the engagement of introverted and extroverted participants differently. Introverted students who used the gamified version of the educational learning system submitted a greater number of correct solutions, had shown to be more engaged to gain points and badges, and had highest ranking visualization when compared to extroverts of the gamified group. These results contribute to the understanding that the effect of gamification depends on users' specific characteristics, such as the personality trait.
Docosahexaenoic Acid and Arachidonic Acid Supplementation and Sleep in Toddlers Born Preterm: Secondary Analysis of a Randomized Clinical Trial.

STUDY OBJECTIVES This secondary analysis characterized sleep patterns for toddlers born preterm and tested effects of docosahexaenoic acid (DHA)+ arachidonic acid (AA) supplementation on children's caregiver-reported sleep. Exploratory analyses tested whether child sex, birth weight, and caregiver depressive symptomatology were moderators of the treatment effect. METHODS Omega Tots was a single-site 180-day randomized (1:1), double-blinded, placebo-controlled trial. Children (n = 377) were age 10 to 16 months at enrollment, born at less than 35 weeks' gestation, assigned to 180 days of daily 200 mg DHA + 200 mg AA supplementation or placebo (400 mg corn oil), and followed after the trial ended to age 26 to 32 months. Caregivers completed a sociodemographic profile and questionnaires about their depressive symptomatology (Center for Epidemiologic Studies Depression Scale) and the child's sleep (Brief Infant Sleep Questionnaire). Analyses compared changes in sleep between the DHA+AA and placebo groups, controlling for baseline scores. Exploratory post hoc subgroup analyses were conducted. RESULTS Eighty-one percent (ntx = 156; nplacebo = 150) of children had 180-day trial outcome data; 68% (ntx = 134; nplacebo = 122) had postintervention outcome data. Differences in change between the DHA+AA and placebo groups after 180 days of supplementation were not statistically significant for the entire cohort. Male children (difference in nocturnal sleep change = 0.44, effect size = 0.26, P = .04; sleep problems odds ratio = 0.36, 95% confidence interval = 0.15, 0.82) and children of depressed caregivers (difference in nocturnal sleep change = 1.07, effect size = 0.65, P = .006; difference in total sleep change = 1.10, effect size = 0.50, P = .04) assigned to the treatment group showed improvements in sleep, compared to placebo. CONCLUSIONS Although there is no evidence of an overall effect of DHA+AA supplementation on child sleep, exploratory post hoc analyses identified important subgroups of children born preterm who may benefit. Future research including larger samples is warranted. CLINICAL TRIAL REGISTRATION Registry: ClinicalTrials.gov; Identifier: NCT01576783.
A Performance Evaluation for Parameter Estimation Method Suitable for Machine Noise Analysis

The parameter estimation of a signal containing multiple random periodic signals is applicable to a variety of fields such like the identification of the number of machines through the analysis of vibrations or sounds. However, it is not easy to achieve it with low computational load. A computationally-efficient approach termed as the accumulation for real-time serial-to-parallel converter (ARS) has been proposed for vital sensing, just as a computationally efficient parameter estimation method for a composite signal of multiple periodic signals. Although its computational efficiency has been analyzed, in this paper, the performance of ARS in terms of the identification of multiple random periodic signals is investigated compared with the fast Fourier transform (FFT).
Evaluation of Alarm System Performance and Management in Semiconductor Manufacturing

Industrial alarm system management and enhancement have recently become a subject of high interest in the industrial domain as well as in the scientific research area. Alarm system performance is the key point to look at for the examination of whether they work correctly. Few research papers so far have focused on the semiconductor manufacturing process area. This paper summarizes some interesting work applied not long ago in other industries and especially those adapted to semiconductor manufacturing. It represents a concise description of semiconductor manufacturing processes, toward an understanding of the problems and challenges encountered. Furthermore, a basic statistical analysis employed to show some of alarm system performance metrics that have used, as shown in some well-known standards, to assess an alarm system performance as well as highlighting misconfigured alarms based on their recorded histories.
Multi-task Classification scheme for Cognitive Imagery EEG Acquired using a Commercial Wireless Headset

Cognitive imagery refers to the mental perception of a task with or without its physical significance. Human mind can create imagery more rapidly than actions and results in the generation of electrical potentials in frontal and occipital regions. Several wired neuro-headsets available in market now-a-days can perceive these electroencephalographic (EEG) signals, but the reception is noisy due to motion and electrical impedance artifacts, hence limit its use for practical applications. This paper presents a customized algorithm for classification of the raw EEG data acquired through a commercially available wireless headset (Emotiv EPOC+). The implemented approach included four imagery actions viz. cube lift, push, rotate and disappear. Experimental study has investigated the approach of common spatial patterns (CSP) for feature extraction while dividing it two sets i.e. E-1 and E-2. The two sets differed in terms of training data samples used for generating the initial classification model. E-1 set considered the subject specific (intra) training samples only whereas E-2 considered the samples from all the available subjects (inter) for model generation. The developed algorithm has been successful in classifying four imagery actions with the reported prediction accuracy of 47.83% and 61.24%, with standard deviation of 8.48% and 10.12% for E-1 and E-2 sets, respectively.
Traffic Flow Multi-model with Machine Learning Method based on Floating Car Data

The traffic flow measurement is one of the most important components in the traffic management systems. The existing traditional measurement methods are highly time-consuming and costly to continuously gather the required data, such as loop detectors and video cameras. However the travel duration provided by the emerging Floating Car Data (FCD) on Google Maps offers a novel way to estimate traffic flows. Therefore, this work presents a novel multi-model for urban traffic flows by applying a Gaussian Process Regressor (GPR) tuned using machine learning method based on FCD. The FCD on roads, requested through the Google Maps API, only provides information as congestion and travel duration. Traffic flows is estimated with GPR, including different models built by aggregating together data from days sharing similar configuration. The aggregation is performed manually or using unsupervised classification. At last, a series of experiments are conducted to compare the estimated traffic flow and the real one from actual sensors data. The obtained results show that, the proposed modeling can always reproduce and capture the tendency of real traffic flow. The aggregation permits effectively to increase the performance and to conclude on the capability of the approach to replace traditional loop detectors for the measurement of traffic flows.
IOT based Smart Waste Bin to Track Dustbin and Public Complaint Management System

Internet or net of effects is concerned in every one parts of human life, work, health and social zones, which will importantly affect the prospect development of the worldwide financial society. Internet of Things (IoT) permits association among gadgets utilizing web with the capacity to accumulate and trade data. The net of objects refers to a kind of n/w to link everything with the net based on specific protocols during info sensing tools to perform info substitute & communications in order to complete smart re-cognitions, positioning, tracing, monitoring, and administration. Recent scientific advance have led to an increase in the carbon footprint. Energy efficiency in the IoT has been magnetize a lot of thought as of researchers & designers above the last pair of years, concrete the way for an emerging area called green IoT.
Energy Balanced Clustering using Balancing Nodes for Wireless Sensor Networks

To prolong lifespan, substantial energy saving to make balanced cluster is a challenge of Wireless Sensor Networks (WSN). Proper cluster formation may be one of solution to overcome this challenge. We have presented here a simple balanced clustering method that will save substantial energy as well as increase lifespan of the network. In various clustering approach, clusters size may be. So energy may be wasted due to too high or too low cluster size. We have mitigated this limitation by ensuring energy balanced. In our protocol, nodes will be joined to cluster head by ensuring cluster size not too high nor too low. For that we used some nodes as balancing nodes that may be joined freely to ensure energy balancing. We have evaluated the proposed protocol and compared with LEACH-C and CH-Leach. We have simulated our protocol and found major improvement of performance with respect to 1st Node Death (1ND), End Node Death (END), consumption of energy vs rounds, alive nodes vs rounds and dead nodes vs rounds.
Split Ring Resonator Loaded Z-Slots Antenna for On-body Communication

Split Ring Resonator (SRR) loaded antennas are indispensable for on-body monitoring applications. The core inconveniences of convention antennas have poorer gain and bandwidth. The main characteristic SRR loaded types of antenna have high gain and bandwidth. In this design conductor backed coplanar wave guide fed line (CB-CPW) used. The core advantages of CPW fed have lower attenuation and dispersion. The prototype resonated frequencies are 2.4GHz and 3.5GHz. The 2.36GHz to 2.48GHz are used for ISM band applications. The 3.3GHz to 3.6GHz are used for WiMAX/5G medical service applications. The simulated reflection coefficient results consist of −20dB at 2.38GHz and −22dB at 3.55GHz for Metamaterial loaded antenna, −24dB at 2.43GHz and −15dB at 3.5GHz for slot1 antenna,−23dB at 2.43GHz and −14dB at 3.5GHz for slot2 antenna with bio-muscle. −34dB at 2.43GHz and −15dB at 3.5GHz for non-Metamaterial loaded antenna.