Miracle-dz/UniFAR-FTUs huggingface dataset:
An Integrated Wireless Aquaculture Monitoring and Feed Management System

Shrimp farming is one of the most significant aquaculture activities in India, vannamei shrimp is a type of shrimp that is widely cultivated in India. However, there are a number of issues contributing to the failure of vannamei shrimp production, including poor water quality, improper feeding during the maintenance period, particularly in open ponds. Feeding is the primary factor determining efficiency and cost in aquaculture, so knowing quantity and frequency of feeding is critical for maximizing feed conversion ratio and production efficiency. In this context an integrated system for overall monitoring of aquaculture and feed management is proposed in this paper. The long-term sustainability of shrimp production can be achieved by monitoring water quality, managing feed appropriately, and managing the cultivation environment, according to a survey of farmers in various regions about their farming practices. In order to solve these issues, this study suggests a compact design of automatic integrated feeding system comprising feeder, sensors, aerator, check nets etc. The proposed system provides both in situ and remote monitoring of crucial water quality parameters such as pH, temperature, and turbidity. A customized mobile app is integrated to monitor water quality and manage the feed remotely according to the requirements of the pond and the choice of the user.
Stratospheric balloon earth observation gathered imagery classification through deep learning

Earth observation, also known as remote sensing, is the collection of data about the Earth’s surface and atmosphere using various remote sensing platforms, such as satellites equipped with imaging instruments. The field of computer vision has been increasingly employed for satellite imagery analysis to extract meaningful information from the data collected. However, the cost of launching and maintaining space-based missions can be prohibitive for certain applications, particularly those requiring low-cost testing. An alternative approach that has gained traction in recent years is the use of stratospheric balloons, which are capable of collecting data at high altitudes at a fraction of the cost and time required for space-based missions. This article presents a workflow for implementing a deep learning-based image classification system for stratospheric balloon imagery. In that sense, the proposed system aims to determine the quality of the images captured, with the ultimate goal of utilizing them for science communication and promoting aerospace projects.
CMOS Schmitt Trigger Circuit and Oscillator Design: The Impact of NBTI Degradation

CMOS Schmitt trigger is a widely used circuit in numerous applications including those for removing noise from signals, to speed up slow edge signals and in the design of oscillators. Aging related degradation in CMOS circuits is a key concern which limits their performance over longer periods (years). This article examines the performance degradation in a CMOS Schmitt trigger circuit over a period of 10 years under the effect of NBTI degradation. The CMOS Schmitt trigger circuit has been used as an oscillator and its output has been examined Variation in dvth with time in p-MOSFETs of the Schmitt trigger circuit and the oscillator circuit have been evaluated. The transient analysis of the Schmitt trigger shows a notable degradation in circuit performance. At year 0 the falling edge transition takes places at 34.8 ns while at year 10 the falling edge transition takes place at 34.6 ns showing a shift of 0.2 ns.
Fish Recognition Using Deep Neural Network

Fish recognition is the various essential factors of fishery studies applications, where a massive quantity of facts is gathered quickly. Due to bad picture quality, uncontrollable objects, and the environment, in addition to the problems in getting consultant samples, underwater picture popularity poses particular challenges. The primary purpose of this study is to create a supervised feature learning-based fish recognition framework. The required data is provided for further analysis based on medical and fish market usage. The system modules in this work are built using deep neural networks. Neural networks will increase accuracy in a variety of circumstances involving input photographs and targets. Experiments demonstrate that the suggested framework achieves great accuracy while balancing high uncertainty and sophistication on both sides: Public and self-collected underwater fish photos. Finally, the recognized fish type and medicinal uses are called out by utilizing voice instructions on MATLAB plateform.
Parameter Identification for Mixed Blur Image with Motion Blur and Defocus Blur

For the problem of identifying motion blur length,motion blur angle and defocus blur radius in mixed blur image,a method for parameter identification for mixed blur image with motion blur and defocus blur was proposed.Firstly,the blur image was processed with Fourier transformation,then Radon transformation was adopted to identify motion blur angle.Secondly, filter was adopted for the spectrum,the amplitudes in the central region of the spectrum were summed,and the sum was inputted into the BP neural network.The amplitudes were summed up vertically when estimating motion blur length and were summed up circularly when estimating defocus blur length.The simulation results show that the error of parameter identifications of noise free mixed blur images is smaller than 6%.
A Hybrid Gradient Boost Model for Intrusion Detection

Due to the advancement of network threats at present, it is crucial to conduct research on identifying and preventing network anomalies. Machine learning (ML) is one strategy for Intrusion Detection System (IDS). Finding a reliable system to act as a networking shield is still difficult despite the fact that various IDS are suggested utilizing ML. This paper suggests a hybrid model combined with the gradient boost methods XGBoost and Lightgbm to forecast the various attacks that are urging in the network. To obtain the higher precision, hyperparameters of the algorithms are tuned. The proposed system is trained using the UNSW-NB15 dataset, which contains attacks for Generic, Exploits, Denial of Service (DoS), Shellcode, Fuzzer, and Reconnaissance. The system has an average accuracy of 99.89%. Because of the recent dataset training, the proposed system is relevant to modern Intrusion Detection Systems used in current network systems.
Predicting Early Withdrawal of University Students: A Comparative Study between KNN and Decision Tree

“The rising trend of students dropping out of universities without completing their degrees is becoming a concerning issue for institutions. To address this problem, the reasons behind this phenomenon need to be explored. However, most educational data sets have small sample sizes and varying patterns. Currently, there are few machine learning approaches for Pakistani higher education student performance. This study presents a machine learning-based approach to predict student withdrawals and identify the reasons behind them. The proposed approach compares two supervised ML algorithms, K-N earestNeighbors (KNN) and Decision-Tree (DT). The most important attributes affecting student retention are also determined using the ExtraTreesClassifier ensemble learning algorithm. In our experimental evaluation, the accuracy of KNN was 75%, and 70% for DT. ”
A Review of Agile Methods for Requirement Change Management in Web Engineering

Web-based systems are essential in many different applications, which makes web-based development different from traditional software development. The agile methodology has gained lots of popularity in the last 15 years and become very traditional and acceptable widely among software engineers; nevertheless, the use of agile methods has extended to cover other areas of software engineering field including Requirement Engineering (RE) that fit the need of web engineering. The pace of the current web's software development is fast and dynamic such that the changes of the requirements during software development and after turning to production phase are possible and recurrent. Therefore, agile software engineering conceptuality has evolved as an adequate approach to overcome changes in the web's software's requirements; due to frequent changes in requirements, web engineers call for help of agile software engineering methods, which strive to truly manage changes in requirements rather than preventing these changes. This paper provides a review on the available agile methodologies that used to assess requirement change management in web engineering.
Automated Driving Systems Operating as Mobility as a Service: Operational Risks and SAE J3016 Standard

SUMMARY & CONCLUSIONSLevel 4 (L4) Automated Driving Systems (ADS) development and testing efforts target not only personal use, but also its deployment within a Mobility as a Service (MaaS) context. MaaS operations introduce additional safety aspects related to passenger behavior, fleet operators, communication between fleet operators, ADS developers, and vehicle manufacturers. However, the operational safety of L4 ADS vehicles operating within a MaaS context has not been fully addressed.The Society of Automotive Engineers (SAE) J3016 standard provides a solid foundation for the concepts related to ADS operations, including safety-related ones such as how the ADS vehicle should behave in case of a system failure or when exiting the domain in which it is designed to operate – DDT Fallback. This paper discusses J3016 essential concepts and the challenges posed by the use of ADS vehicles, within a MaaS operation, to some of these concepts. Through Event Sequence Diagrams, new goals for the ADS DDT fallback are proposed, and the roles of remote monitoring operators for safety are discussed.
Multi-Task Cascaded Convolutional Neural Networks (MTCNN) based Driver Drowsiness Detection

Road accidents are most commonly caused by tired and fatigued drivers. They increase the number of fatalities and injuries globally each year. The proposed method uses three network layers of Multi-Task Cascaded Convolutional Neural Networks (MTCNN) to detect faces. To prevent traffic accidents while driving, it is possible to monitor the driver's level of weariness by observing how frequently his eyes blink. An additional alarm is sounded to signal the automated sidelining and breaking of the vehicle if the driver continues to feel sleepy after the siren. Once the picture pyramid design is supplied to the three network layers, the eye ratio will be calculated by using visual data and Open CV.
Model Predictive Control of Alternate Arm Converter with a Short-Overlap Period Under Steady-State and Transient Conditions

Alternate arm converter (AAC), as one of the novel multilevel converter structures, offers many advantages over conventional modular multilevel and VSC-based converters, e.g., the ability to block the dc fault current as the most noteworthy feature. In AACs, the director switches (DSs) are responsible for alternating the conduction period between each phase's upper and lower arms. This causes reducing in the number of sub-modules to produce the necessary ac power at the output. But the main challenge in AAC is energy balancing and control of differential currents in overlap mode. In this paper, AAC modeling is carried out in non-overlap and overlap modes, and an individual model predictive controller is designed for both operation modes. Also, the zero-current switching (ZCS) concept is employed to derive the reference value of the differential phase-leg currents. The proposed controller is tested in the steady-state and fully active power reversal conditions considering the short-overlap mode operation of the AAC. The simulation results in MATLAB/Simulink confirm the superiority of the proposed controller in achieving ZCS of the DSs during inverter and rectifier operation, energy balancing, and lowering the total harmonic distortion (THD) of the ac-side currents.
Variable Parameter Based New Control Approach for Three- Phase Four Wire Grid-Tied PV System

The present study proposes the variable-parameter zero attracting adaptive based new least mean square control algorithm for 3-phase 4-wire grid-incorporated PV system. The proposed control algorithm performance is good for changing environment temperature due to inclusion of feed forward term and variable parameter zero attractive term over the conventional control algorithm. The PV system i.e. combination of PV array and MPPT utilize the two-stage topology as it offers more stabilized DC voltage at input end of the Inverter. Besides the exchange of real power from PV system to grid and vice-versa, the proposed novel control also improves the power quality problems and compensates the neutral current as well. The 4-wire system is utilized to compensate the neutral current caused by unbalanced non-linear load and to save the system and customers from the severe damage. The system is simulated in MATLAB/Simulink at steady state, changing insolation and load unbalancing conditions. The performance is analyzed and found satisfactory under different operating conditions.
Packet scheduling in rechargeable wireless sensor networks under SINR model

Two packet scheduling algorithms for rechargeable sensor networks are proposed based on the signal to interference plus noise ratio model. They allocate different transmission slots to conflicting packets and overcome the challenges caused by the fact that the channel state changes quickly and is uncontrollable. The first algorithm proposes a priority-based framework for packet scheduling in rechargeable sensor networks. Every packet is assigned a priority related to the transmission delay and the remaining energy of rechargeable batteries, and the packets with higher priority are scheduled first. The second algorithm mainly focuses on the energy efficiency of batteries. The priorities are related to the transmission distance of packets, and the packets with short transmission distance are scheduled first. The sensors are equipped with low-capacity rechargeable batteries, and the harvest-store-use model is used. We consider imperfect batteries. That is, the battery capacity is limited, and battery energy leaks over time. The energy harvesting rate, energy retention rate and transmission power are known. Extensive simulation results indicate that the battery capacity has little effect on the packet scheduling delay. Therefore, the algorithms proposed in this paper are very suitable for wireless sensor networks with low-capacity batteries.
Combination of Porous Layer and Jet impingement in an Annular Heat sink

Employing porous materials is common in heat sinks for cooling electronic devices and energy management. Heat transfer and fluid flow behavior of semi-filled porous (AI metal foam) heat sink with jet impingement is investigated in this article. The designed heat sink has an annular shape with a single inlet at the center, and metal foam is located at the lower base of the channel. The $\text{Re}=5000$ and the $k-\varepsilon$ realizable turbulent model are used for modeling. Metal foam increases the heat transfer coefficient and pressure drop in the heat sink simultaneously. It was found that employing a thin layer of porous material enhances heat transfer (up to 82%) and the overall performance of the heat sink. Increasing the height of the porous layer has adverse impacts on the heat transfer coefficient and pressure drop compared to lower porous layer heights.
Effects of Multi-phasing on the Performance of Flux-intensifying IPM Machines

The reliability of electric motors is an indispensable aspect of their performance in safety-critical applications. A Common application which requires great safety is electric vehicle (EV) application. There are many aspects which considering them can increase the reliability of the system in these applications and makes the system more fault-tolerant. Designing the system with a good geometry can enhance torque profile and decrease the risk of demagnetization. One of the common ways of enhancing the reliability is employing a multi-phase winding in electric machines. Due to The recent advances in the field of power electronics and drives the chance has been provided to apply multi-phase winding in electrical machines. Multi-phasing can bring about some great benefits such as lower back-EMF and phase inductance and a better performance in flux-weakening area. Therefore, in this paper a six-phase flux-intensifying IPM machine is designed and its electromagnetic performance and operation in fault condition are evaluated and compared with the conventional three-phase machine of the same type, size and current density.
Energy of Knots and Conformal Geometry

Just like a minimal surface is modeled on the “optimal surface” of a soap film with a given boundary curve, one can ask whether we can define an “optimal knot”, a beautiful knot which represents its knot type. Energy of knots was introduced for this purpose. The basic philosophy due to Fukuhara and Sakuma independently is as follows. Suppose there is a non-conductive knotted string which is charged uniformly in a non-conductive viscous fluid. Then it might evolve itself to decrease its electrostatic energy without intersecting itself because of Coulomb’s repulsive force until it comes to a critical point of the energy. Then we might be able to define an “optimal embedding” of a knot by an energy minimizer, which is an embedding that attains the minimum energy within its isotopy class. Thus our motivational problem can be stated as:
Research on Force Transmission Control of Bowden Cable-Fabrics Based on FEM and Fuzzy ADRC

The friction force inside the Bowden cable will lead to the gap, hysteresis and non-linearity in the force transmission system, The fabrics wrapped around the limbs has the hysteresis and internal friction characteristics of polymer, which will cause the hysteresis and loss of the force generated by the system. In this paper, the static Coulomb friction math model of the Bowden cable and the finite element model of the fabrics are established for analysis, and the force compensation obtained is added to the control system. Then, based on the model, a fuzzy Active Disturbance Rejection Control (ADRC) method is proposed, which is compared with the traditional PID control method through simulation experiments. The simulation results show that the force tracking error is significantly reduced after the mixed force compensation of Bowden cable and fabrics, and the force tracking error is reduced by 1.96% after the use of fuzzy ADRC.
Design & Analysis of PV Interface Hybrid Two-Stage Transformerless Inverter

Transformerless inverters are fetching more dominance for grid-connected solar usage due to their simplicity, decreased size, weight, cost, and improved efficiency. This conference reports a two-stage transformerless single-phase grid-connected inverter design. The suggested architecture has two parts that include the front-end DC-DC converter using a switched capacitor unit followed by a conventional boost network to give a quadruple voltage boost. The second part comprises a cascaded H-bridge topology. The proposed architecture has advantages like the reduced requirement for a DC link, lesser components that enhance the reliability and robustness of the system along with reactive power capability. The simulation for complete topology is carried out in the Simulink environment. Lastly, an effective comparison is carried out with the existing two-stage inverter topologies.
Automated Algorithm for QRS Detection in Cardiac Arrest Patients with PEA

Pulseless electrical activity (PEA) is one of the most common rhythms during a cardiac arrest (CA), and it consists in lack of palpable pulse in presence of electrical activity in the heart. The main treatment for a CA is the cardiopulmonary resuscitation (CPR), including chest compressions and ventilations, together with defibrillation shocks and drugs when necessary. The therapy of PEA depends on its characteristics, mainly the morphology of the QRS complex. Well known algorithms for QRS complex detection and delineation were designed for hemo-dynamically stable patients with pulsed rhythm (PR). The aim of this study was to develop an automatic method for QRS complex detection in patients with PEA during CA. The database for this study consists of 5128 PEA segments from 264 in-hospital CA patients. The ECG signal was decomposed using the stationary wavelet transform, a peak detector was applied on the third detail component and a multicomponent verification was set to detect the peaks. Finally, a time alignment of the detected QRS complexes was performed using the original ECG signal. The proposed method presents median (IQR) Se/PPV/F1 values of 92.4(15.2)/88.5(15.4)/88.8(15.6) for PEA segments.
Extraction of River Networks from Satellite Images using Image Processing & Deep Learning Techniques

River networks are widely observed and scrutinized for various purposes, which incorporate determining the terrestrial positions of water bodies, examining the gauge levels of the river, predicting river flows, and conserving sustainable energy resources as a consequence of Global warming. Extraction of these River networks on digital imagery systems are executed by various segmentation and machine learning model integration. In this paper, distinct datasets are used from Kaggle and Google Earth Engine, Segmentation methods such as Image segmentation, gray scaling, enhancement, global thresholding, and Deep Learning UNet Architecture are integrated with contemplation of extracting river networks from satellite images which result in achieving 80.98 % dice score for the developed UNet Model. Hence, these developed techniques can further be used for river extraction from satellite images. And can be applied to various semantic segmentation detection datasets.
Compressive Sensing Mapping System for Spatial Characterization of Photovoltaic Devices

Photocurrent mapping (PM) is a non-destructive characterization method of solar cells and panels. It is used in the industry for quality control in the production of photovoltaic devices, in scientific laboratories to characterize new materials, and in photovoltaic facilities to find module failures. The PM consists of applying a laser light beam perpendicularly on a photovoltaic device, scanning the surface point by point, and measuring the induced current as a function of the position of the beam. These systems require a high degree of mechanical stability, which leads to an increase in the characterization times when the area is large. In recent years, it has been made progress in the use of compressive sensing algorithms applied in PM tests in order to reduce moving parts and the measuring times. In this work we apply the compressive sensing technique to obtain a photocurrent map of Si photovoltaic devices. Results are compared with the ones obtained by the conventional technique.