Miracle-dz/UniFAR-FTUs huggingface dataset:
A Model for Optimal Assignment of Non-Uniquely Mapped NGS Reads in DNA Regions of Duplications or Deletions

Massively parallel sequencers have enabled genome sequences to be available at a very low cost and price, which opened huge scope on analyzing human genome sequences from different perspectives, thereby the association of diseases with genetic alterations gets further enlightened. However, the sequencing process and alignment of NGS technology based short reads suffer from various sequencing biases which needs to be addressed. In this work, the mappability bias occurring with respect to repeat rich regions of the DNA have been addressed in a novel approach. A model has been designed which considers all non-uniquely mapped reads and performs a pipeline of computations to allocate the reads to an optimal location, due to which the precise detection of breakpoints in the region of duplications and deletions are obtained. In addition, the application of this model for mappability bias correction, prior to the detection of structurally altered regions of the genome, leads to a better sensitivity value.
Unsupervised STDP-based Radioisotope Identification Using Spiking Neural Networks Implemented on SpiNNaker

This paper presents a spiking neural network (SNN) implementation which employs unsupervised feature extraction using spike timing dependent plasticity (STDP) to classify 8 different radioisotopes. With the implementation, the accuracy could reach 80% during training and overall testing accuracy of 72%. The whole network was implemented on SpiNNaker, a spiking neural network emulation platform. This work shows that unsupervised STDP, an SNN native training method, can be applied to the classification task of RIID to provide event-based training as well as inference.
Optimal Placement and Sizing of Various C-Rate Type of BESS for Minimizing the Impact of Intermittent DG Output on Hourly Peak Load Variation in Radial Distribution Network

The motivation of contribution of renewable energy resources for national grid support are increasing by distribution utilities. The intermittent output behavior of these DERs during a day is always been an important concern for power energy engineers which eventually increase the dependency on energy storage units. In this paper optimal BESS placement and sizing is done by Teacher Learner Based Optimization (TLBO), for reduction of intermittent DG output impact on hourly peak load variation. Six different C-Rate types of batteries namely 0.5C, 0.08C, 0.25C, 0.33C, 0.167C and 1C are evaluated for voltage profile improvement with power loss reduction in a day. With the optimal located and sized BESS in distribution side of grid will leads to have a reliable with efficient grid support and reduced power loss help to grid load power demand diversification. In this paper Reliability test system model is used for the reliability analysis per hour for Modified radial IEEE 33 bus distribution grid network, and results are verified and simulated in MATLAB 2019b software.
Optimal Design for the Torsional Elastic Element Topology of a Rotary Series Elastic Actuator

The series elastic actuator improves the compliance of exoskeleton by introducing a elastic element and ensures its safe interaction ability. Therefore, it is regarded as a new driving mechanism by scholars. Suitable elastic element is conducive to realize the miniaturization and lightweight of SEA, but its topology design is complex and lack of guidance. Based on the finite element simulation environment and three-dimensional parametric CAD environment, this paper analyzes the influence of different size parameters and topology on the pressure of the elastic element, summarizes the characteristics of three topology structures, and puts forward the optimal design method of a torsional elastic element topology. Through the research, we can conclude that the compression effect of elastic beam with specific shape is better than that of curved elastic beam, and the width of elastic beam has a great influence on the torsional elastic element.
Color Optimization for Reducing Artifacts on Barrier-Based Multi-View Displays

Ghosting artifacts unexpectedly appear resultant of crosstalk in multi-view displays. In this paper, we propose an algorithm that controls ghosting, assuming that crosstalk occurs. The target of this paper is a barrier-based multi-view display. Our method generates a pattern for the back layer on the display. Many previous methods attempted to reduce crosstalk, and consequently, it reduces ghosting. In contrast, we will focus only on reducing ghosts in displaying images. We assume that ghosting occurs when crosstalk makes a distribution of colors in displaying images. So, if the distribution will become small, then the ghosts are reduced. From our assumption, we develop an optimization that does not change structures in displaying images. In this paper, we only discuss how to generate the pattern on the back layer. We will show the results of our method as a simulation.
Quantitative Evaluation of Digital Orthophoto Map

There is no comprehensive and scientific standard and system for the classification, selection and use of digital orthophoto map quality evaluation indexes. There are some problems in the evaluation, such as unclear selection of indexes and inconsistent evaluation results. In this paper, the quantitative evaluation indexes of position accuracy, edge quality, texture quality, fusion quality and other quality characteristics are selected, the calculation formula is sorted out, the program is written, and compared with the subjective evaluation of the inspector, the best evaluation scheme of digital orthophoto map is discussed. The results of the digital orthophoto map quality evaluation experiment in Lusong District show that the position accuracy detection based on speed up robust feature (SURF) dense matching algorithm can reflect the overall geometric accuracy of the image, and the radiation consistency, geometric consistency contrast and clarity indexes such as gray contrast coefficient, uniform brightness distribution, mean gradient, error relative global adimensionnelle synthese (ERGAS) index can effectively reflect the edge quality, texture quality and fusion quality of digital orthophoto map.
Hydrogen Production Technologies – A Comparative Overview and Future Developments

Hydrogen is becoming a more significant energy source supported by the sustainable policies. The gas can be produced from a variety feedstock and With the usage of renewable and conventional source of energy. This paper reviews the hydrogen production technologies, which are present and commercially available together with the future trends for their development. The production methods are compared in terms of efficiency and environmental impact.
A More Effective Way of Target Segmentation of Multispectral Imaging Data Using Kmeans Algorithm

Conventional multispectral system are carried in aerial or satellite systems and are limited in temporal resolution and can only measure static targets. In this paper, we design a novel multispectral device that uses a camera array with different filters to continuously and dynamically capture 12-bit images with $960 \times 540$ resolution in 35 bands. Algorithms are also designed to correct for pixel shifts in different channels of images, as well as a method to cluster the captured multispectral images using the K-means algorithm.
Revivify: A Depression Detection and Control System using Tweets and Automated Chatbot

Mental Health is a stigma in India and on a global scale. Ignorance of mental health on our part has created a world where those suffering from it cannot talk about it openly and often feel uncomfortable disclosing it to others or even professional therapists. To address this problem, we propose a digital system that detects signs of anxiety and also suggests methods for depression control. Revivify performs a comprehensive analysis of a user’s mental state using different techniques. We have used tweets, patient health questionnaires, depression anxiety stress scale (DASS), and personalized responses as our dataset. Our system uses Feed Forward Neural Networks, Latent Dirichlet Allocation, and Random Forest Classifier algorithm to classify the user responses and tweets into one of the nine levels of anxiety and depression. Random Forest Classifier gives the highest accuracy. Further, the chatbot also suggests various blogs and provides helpline numbers for damage control. This system is a cost-effective solution to detect depression.
Privileged and Unprivileged Groups: An Empirical Study on the Impact of the Age Attribute on Fairness

Recent advances in software fairness investigate bias in the treatment of different population groups, which are devised based on attributes such as gender, race and age. Groups are divided into privileged groups (favourable treatment) and unprivileged groups (unfavourable treatment). To truthfully represent the real world and to measure the degree of bias according to age (young vs. old), one needs to pick a threshold to separate those groups. In this study we investigate two popular datasets (i.e., German and Bank) and the bias observed when using every possible age threshold in order to divide the population into “young” and “old” groups, in combination with three different Machine Learning models (i.e., Logistic Regression, Decision Tree, Support Vector Machine). Our results show that age thresholds do not only impact the intensity of bias in these datasets, but also the direction (i.e., which population group receives a favourable outcome). For the two investigated datasets, we present a selection of suitable age thresholds. We also found strong and very strong correlations between the dataset bias and the respective bias of trained classification models, in 83% of the cases studied. CCS CONCEPTS • Social and professional topics → User characteristics; • General and reference → Empirical studies. ACM Reference Format: Max Hort and Federica Sarro. 2022. Privileged and Unprivileged Groups: An Empirical Study on the Impact of the Age Attribute on Fairness. In International Workshop on Equirable Data and Technology (FairWare ’22), May 9, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3524491.3527308
Super-resolution beamformer with coherence factor treatment

Digita1 beamforming is widely used in underwater acoustical imaging systems, such as side-scan and multi-beam sonars, for subsea surveying, anti-terrorist and diver detecting. Without increasing the system complexity, e.g., enlarging the aperture size of the transducer array, the super-resolution beamformer (BF) seems attractive to improve the resolution and the resultant image quality. However, super-resolution BF approaches generally suffer from a rapid increase in terms of computation load as a price, e.g., invoked power spectrum estimators and compressive sensing solvers. To overcome such issue, a coherence factor (CF) is employed to refine the BF result (CF-BF). In this paper, CF is redefined to incorporate the Fresnel approximation, which can be implemented by FFT readily and efficiently. Generally, an extra FFT and three Hadamard matrix product are required in addition to the conventional BF. According to the simulation and experimental results, our proposed CF-BF enhances the resolution and performs well in suppressing the background noise level, performing on-per with the other state-of-the-art super-resolution BF approaches in terms of image quality while being much faster.
A social-cognitive analysis of a female-focused hackathon

Hackathons are thematic events of short duration that focus on the development of software prototypes. These events normally last from one to three days and they are increasingly popular. However, the interest of women in these events is still low. This paper aims to investigate, from a social-cognitive perspective, how experiences of more inclusive hackathon formats affect interest in hackathons themselves. We adopted autoethnography to systematize the first author's experience as a team participant in a female-focused hackathon. The results suggest that event formats can affect participants’ self-efficacy beliefs, outcome expectations and goals. These beliefs may influence perceptions of their potential performance on the team, confidence in their own skills in the technology and perceptions of opportunities in the technology market. CCS CONCEPTS • Social and Professional Topics → Women • Human-centered computing → Empirical studies in collaborative and social computing. ACM Reference format: Luciana A. Almeida and Cleidson R. B. de Souza. 2022. A social-cognitive analysis of a female-focused hackathon. In GE@ICSE'22: Third Workshop on Gender Equality, Diversity, and Inclusion in Software Engineering Proceedings. ACM, Pittsburgh, PA, USA, 4 pages. https://doi.org/10.1145/3524501.3527597
Person Identification based on ECG Signals using Continuous Wavelet Transform

The development of biometric methods has been given significant importance in recent years. Accurate and unassuming identification and authentication of persons is a crucial fundament in protecting personal or industrial property. This work presents the person identification based on electrocardiographic (ECG) signals, where several approaches are investigated. The own database of ECG signals acquired from 19 subjects is created for this purpose. Signals are split utilizing blind segmentation and R peak centered segmentation with four different segment lengths. They are further represented in the time-frequency domain in the form of scalograms, incorporating the Morse, Morlet, and Bump wavelet. The convolutional neural network (CNN) models are proposed for the person identification, while the highest average classification accuracy of 99.60% is achieved.
Impacts of Energy Storage Facilities on Resilient Operation of Multi-Carrier Energy Hub Systems

Nowadays, with respect to the new strategic insight into the management of power systems, the grids are inevitably restructuring from centralized networks toward multiple sub-systems encompassing many local networks and microgrids. Hence, during the recent decade, the necessity for the deployment of multi-carrier energy hub schemes has assumed special attention. An energy hub scheme is a kind of multi-carrier system which is capable of converting, storing, and transmitting energy carriers from inputs toward outputs. The energy hub system acts as a connector and interface between consumers and energy infrastructures to maintain the demand reliably and optimally in terms of economy. Due to the uncertainty of parameters, volatile conditions, and widespread pervasiveness, the accurate modeling of hub systems seems to be indispensable. Such schemes have witnessed significant revolutionary developments and have undergone innovative progress during recent years. In this study, a new model for the hub scheme is proposed encompassing two types of thermal and electrical storage units in addition to an objective function for operation under normal and faulty conditions. Thus, the impacts of the presence of storage facilities on the optimal operation of the hub scheme regarding resilience objectives and total operation cost are investigated. This non-linear, non-convex, and non-smooth problem is optimized using a novel metaheuristic solver called Slime Mould Algorithm (SMA), which has demonstrated a stunning performance in terms of accuracy and computation metrics. The results allude to the prominent virtues of the proposed hub scheme in diminishing operation cost and augmenting reliability and resilience of supply.
Monitoring of Crop Growth Parameters using Arduino and ESP8266

India is an Agriculture dependent country. With the significant percentage of its population depending on farming as their livelihood. Hence the monitoring of crop growth parameters is important for efficient yielding. This Project is regarding Smart Irrigation System by using Sensors. This smart irrigation system provides Automatic and remote monitoring of the fields. In this system we are using sensors like pH, Temperature, soil moisture, rain, Low-power image Sensors. The soil moisture sensor is used to monitor the moisture of the Soil. The temperature sensor will monitor the temperature level. The pH Sensor will check the pH range of the soil. Rain sensor is used to detect the rain. If the values which are measured hit the threshold value, a message is forwarded to the user's mobile number. Subsequently a relay can be activated to switch on the sprinkler water motor to wet the crops. An Arduino Uno is used as the brains of the system to interface with the sensors and IoT facility is provided using a Node MCU and Thingspeak. So, we are enhancing our project with an additional feature by measuring pH of our crop and soil with the help of pH sensor. By observing this pH values, we can monitor and control our land from crop failures. The conclusion of our project is to reduce the workload of farmer by saving his time, money, water usage and providing him a healthy and better proportion of crop yield.
Compact Complementary Symmetric Ring Resonator Band Stop Filter for Ku band applications

A band stop filter is proposed in this paper using a novel complementary symmetric ring resonator for Ku band applications. The designed filter consists of a microstrip transmission line with periodically loaded complementary symmetric ring resonator structures. Two adjacent rectangular symmetric ring resonators are used as complementary structures for the first time to design a bandstop filter. These structures have resulted in deep rejection in stopband and broad stopband. The bandstop filter is designed, simulated, and fabricated. The HFSS simulation, equivalent circuit simulation, and measured results are compared and observed with good similarity. The measured results have shown a 3 dB Fractional bandwidth of 12.4% over the center frequency 12.87GHz. The maximum attenuation obtained in the stopband is nearly 31dB.
Development of a system for hybrid detection of time series anomalies

The article describes the results of the development of a software system that implements the method of finding anomalies using a neural network and their subsequent analysis based on intelligent systems. The developed system processes the initial data of the time series and outputs a set of anomaly values. The system uses neural network technologies and ontological analysis. The ontology uses a pre-designed class and SWRL rules. The proposed approach of hybridization of anomaly search methods makes it possible to fill the lack of explainability of neural networks with data from ontology
Phased Array Antenna for Millimeter-Wave 5G Mobile Phone Applications

The design of a compact 8-element linear phased array is presented for millimeter-wave 5G applications. To provide a cost-effective antenna solution, both the radiator and the ground plane are placed on the same side of the printed circuit board. A small notch is designed between the radiators to achieve high isolation between the antenna elements. The results show that, according to −6 dB bandwidth criteria, the designed phased array provides an impedance bandwidth of 8 GHz (24–32 GHz) and, according to −10 dB bandwidth criteria, the impedance bandwidth is equal to 5 GHz (25–30 GHz). It is also observed that the isolation between adjacent antenna elements is > 15 dB. Moreover, the proposed array can have beam steering characteristics for different scanning angles with omni-directional radiation characteristics.
Diabetic Retinopathy Detection: A Deep Learning Approach

Diabetic Retinopathy,is a well-known consequence of diabetes that results in retinal degeneration and eye problems. If it isn't caught early enough, it can lead to sight loss. Diabetic Retinopathy, however, is not a treatable condition, and treatment can only assist maintain vision. The risk of vision damage can be considerably reduced if DR is diagnosed and treated early. Traditional conclusion interaction of DR retina fundus photos by doctors, unlike PC-assisted decision frameworks, requires time, effort, and money, and is prone to mistakes. Machine learning is a relatively new method for improving execution in a variety of sectors, particularly clinical image investigation and design. Clinically, fully-connected brain networks are becoming more prominent as a deep learning technique. The fresh cutting-edge methods for discovering and arranging DR shading fundus visuals using deep learning academic skills have been evaluated and investigated for this piece. In addition, the shading fundus retina records that are accessible via DR have been analysed.
A Novel CMOS Structure Composed of Junctionless n-FinFET with the Same n-Channel and Common Gate

In this paper, a novel CMOS inverter with the same n-channel and same gate work function has been proposed. This new structure is composed of junctionless (JL) n-FinFET and inversion-mode (IM) p-FinFET. Compared with the conventional CMOS inverter, the novel CMOS device can be fabricated on the same SOI substrates with the same gate material, which simplifies the fabrication process and reduces fabrication costs. The logic performance of CMOS inverters is evaluated using 3D numerical simulation at sub-5 nm technology nodes.The ION/ IOFF ratio of the JL n-FinFET improved up to 3.8%, the intrinsic gain improved up to 4.8% as compared to the IM n-FinFET. The rise time, fall time and RO frequency of the novel CMOS inverter are improved up to 1.8%, 8.5% and 7.6% respectively, compared with the traditional CMOS inverter.
Study on Linear Ccd Acquisition Rate for Variable Speed Moving Objects

In practical engineering applications, linear charge coupled device (CCD) imaging detection is a very common application. Because there is always a certain time difference between the velocity acquisition of the measured object and the linear array image acquisition, it is necessary to make certain velocity prediction and approximation for the measured object. The paper present a new velocity prediction method which can effectively predict the velocity change of a variable speed moving object in the course of high speed motion. The time series model has good prediction effect, but when the sample size is small, the prediction result of the time series model differs greatly from the real value, and the model has hysteresis. Therefore, a weighted time series prediction algorithm based on hybrid model is proposed in this paper. This algorithm can effectively solve the problem of insufficient data sample size and improve the accuracy of model prediction, so as to effectively reduce the linear array image distortion caused by the time difference problem. Experimental simulation and field test results show that the proposed method has good application value.
Imperative Methodology to Detect the Palm Gestures (American Sign Language) using Y010v5 and MediaPipe

Humans place a high importance on the ability to interact. People with hearing or speaking difficulties had trouble expressing themselves. Despite the fact that sign language solved the problem, they were still unable to engage with the general populace. , necessitating the development of sign language detectors. A variety of sign language detection algorithms are effectively open. This research investigates two well-known models for recognizing American Sign Language Gestures for alphabets: MediaPipe-LSTM and YOLO v5-PyTorch. They were given custom datasets. , and the outcomes were inferred and compared to see how accurate and effective the models were.