Software Part Research Articles

PVACview: an interactive visualization tool for efficient neoantigen prioritization and selection

BackgroundNeoantigen-targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies have been initiated globally. Accurate identification and prioritization of neoantigens is crucial for designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development of computational tools that attempt to account for these complexities. While these tools generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. This often leads to over-simplification of pipeline outputs to make them tractable, for example, limiting prediction to a single RNA isoform or only summarizing the top ranked of many possible peptide candidates. In addition to variant detection, gene expression, and predicted peptide binding affinities, recent studies have also demonstrated the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature and number of salient neoantigen features, presenting all relevant information to facilitate candidate selection for downstream applications is a difficult challenge that current tools fail to address.ResultsWe have created pVACview, the first interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select, and export their neoantigen candidates. The tool allows users to visualize candidates at multiple levels of detail including variant, transcript, peptide, and algorithm prediction information.ConclusionspVACview will allow researchers to analyze and prioritize neoantigen candidates with greater efficiency and accuracy in basic and translational settings. The application is available as part of the pVACtools software at pvactools.org and as an online server at pvacview.org.

Research on Marine Environmental Noise Based on Acoustic Bait Domestication Technology

Acoustic domestication type of marine pastures are generally established in shallow waters with water depth not exceeding 200 m. In order to study the effect of acoustic domestication, this paper aims to investigate the environmental noise in the shallow waters of the sea. Unlike the stable deep sea environment, the environmental noise in the shallow sea is extremely complex, which has a direct impact on the effect of acoustic domestication. In this paper, in order to study the marine environmental noise, a hydroacoustic signal processing system is established, which is divided into two parts: the hardware part and the software part; the hardware part is mainly for signal sampling, and the software part is for signal processing. The hardware part of the hydroacoustic signal processing system includes three parts: acoustic-electric conversion, intermediate processing and signal recording. The study of ship noise showed that the fishing boat is about 5m long, with a power of 60hp, and passes about 10m away from the hydrophone, and it is more than 10dB higher than that without a ship, when there is a ship passing, in which the 400Hz is 17.82dB higher. Measurements of ocean ambient noise at different depths show that the noise decreases with depth, but when close to the seabed, the noise decreases with depth due to the reflection effect of the seabed on the sound and the biological noise of the seabed.

Ultrasensitive allele inference from immune repertoire sequencing data with MiXCR.

Allelic variability in the adaptive immune receptor loci, which harbor the gene segments that encode B cell and T cell receptors (BCR/TCR), is of critical importance for immune responses to pathogens and vaccines. Adaptive immune receptor repertoire sequencing (AIRR-seq) has become widespread in immunology research making it the most readily available source of information about allelic diversity in immunoglobulin (IG) and T cell receptor (TR) loci. Here we present a novel algorithm for extra-sensitive and specific variable (V) and joining (J) gene allele inference, allowing reconstruction of individual high-quality gene segment libraries. The approach can be applied for inferring allelic variants from peripheral blood lymphocyte BCR and TCR repertoire sequencing data, including hypermutated isotype-switched BCR sequences, thus allowing high-throughput novel allele discovery from a wide variety of existing datasets. The developed algorithm is a part of the MiXCR software. We demonstrate the accuracy of this approach using AIRR-seq paired with long-read genomic sequencing data, comparing it to a widely used algorithm, TIgGER. We applied the algorithm to a large set of IG heavy chain (IGH) AIRR-seq data from 450 donors of ancestrally diverse population groups, and to the largest reported full-length TCR alpha and beta chain (TRA; TRB) AIRR-seq dataset, representing 134 individuals. This allowed us to assess the genetic diversity within the IGH, TRA and TRB loci in different populations and to establish a database of alleles of V and J genes inferred from AIRR-seq data and their population frequencies with free public access through an online database.

Vision Systems for a UR5 Cobot on a Quality Control Robotic Station

This paper addresses the development of a vision system for the UR5 cobot and the corresponding operating algorithm of the robotic quality control station. The hardware–software architecture of the developed robotic station consisting of a UR5 cobot equipped with a web camera and a stationary industrial camera with a lighting system is presented. Image processing and analysis algorithms are described, the method of control and communication between the station components is discussed, and operating scenarios are presented as a single robotic station and as a part of the robotic line. Based on the results which were obtained, the level of measurement noise, accuracy, and repeatability of the developed vision system were estimated. A novel complete vision system architecture with hardware–software modules for the UR5 cobot quality control station is shown and discussed. The software part based on Python 3.12 language, OpenCV 4.7.0.68 libraries, and the PolyScope 1.8 environment incorporates modules for system calibration, image acquisition, preprocessing and image analysis (for objects’ location and geometric measurements) and for robotic cell communication and control. The hardware part of the UR5 cobot vision system is based on a PC with two independent and distinct cameras: one permanently affixed and the other mounted to the cobot’s flange. This innovative setup, combined with software architecture, broadens the scope of existing robotic quality control applications.

A hierarchical model of the information environment for railway power supply system critical objects control

It is proposed to consider the hierarchical model of the information environment as a structure according to its functions (implemented by the software part) and architecture (responsible for effective implementation of functional capabilities). It is proposed to improve the information management system of Ukrzaliznytsia’s critical power supply facilities, based on the developed hierarchical model of the information environment, which takes into account the presence of Ukrzaliznytsia’s own generation sources by integrating the appropriate control subsystem into the information system structure. The model emphasizes the integration of software and hardware components for data collection, pre-processing, and decision-making, contributing to a highly automated and intelligent control system. It proposes a multi-level structure incorporating artificial intelligence and advanced technologies to optimize electricity consumption and improve operational efficiency within the railway power supply system.

IMPLEMENTATION OF NATURAL EXPERIMENTS IN PHYSICS USING COMPUTER VISION

Laboratory experiments in physics are a fundamental basis for studying physical phenomena occurring in nature and a methodological tool that provides visibility of the learning process and conducting experiments is important for the formation of students’ scientific worldview, deep understanding of physical laws and increasing interest in the study of physics. Existing in universities and schools, in addition to traditional ones, modern tools, technologies and approaches, such as virtual reality, augmented reality, computer modelling, online laboratories, virtual laboratory and others, are additional tools for improving the quality of the learning process and teaching techniques, which do not replace full-scale experiments, but only supplement them. In our opinion, for better learning, laboratory installations in physics are needed, with the help of which students can carry out real-life experiments and can broadcast them using innovative computer technologies for distance learning. To implement this task, we reviewed and analysed existing laboratory installations, identified their advantages and disadvantages, and then designed and developed alternative digital experimental set-ups for studying physics phenomena in laboratory conditions of educational institutions based on computer vision technology and presented the results of the study in this article. In carrying out the research tasks, effective methods of conducting scientific research were used, such as theoretical substantiation of the issue, experimental testing of the developed hardware and software systems and computer final processing of experimental data. In summary, the research described in the paper presents an innovative mechanism for integrating object tracking based on computer vision to improve the quality of measurements and new ways of conducting physics experiments. The mechanical laboratory complexes we have developed consist of hardware and software parts. The software part consists of server and client parts. The hardware consists of the main part - the scene, where the physical process takes place, i.e. where a physical object is located, such as a mathematical pendulum, an inclined plane, etc., with the help of which many physical phenomena and processes in mechanics can be demonstrated, and an additional part where a microcomputer and a camera are located. The operating principle of the laboratory installation is based on the use of computer vision technology, i.e. a system for monitoring the ongoing physical process, consisting of a digital camera for image processing, object identification and data export, and a microcomputer for processing experimental data. The use of the experimental installations in the process of teaching physics is a new model of teaching with a promising future in secondary and higher education, and the installations themselves will become tools for offline and online learning, due to the use of computer vision technology, revealing new opportunities and approaches to teaching.

IoT Integrated Smart Parking Zone and Website with Stand-alone System and Wireless Technology

The research project works to digitalize the full parking management system, transform the manual parking methods, solve traffic and road congestion problem and show live updates of the designated parking slots. It is ecofriendly and eliminates human interaction. It also offers personal and corporate business earnings. In the project, anyone can book time slots for parking following their convenient schedule, pay bills via the online payment system, control the entrance while entering and exit gate in the parking area with the vehicle using the website system. Users do not have to use any extra identity instrument, like- RFID Cards, QR Codes etc. The system will verify them by online credentials. Moreover, people can see the live slot updates for better security. The project collaborated with 3 major technology parts which are practical hardware, software and database system. NodeMCU, Ultrasonic Sensors, Servo motors, and other technical components are used in the hardware part. PHP, HTML, JavaScript, CSS etc. codes are implemented for the website development. Firebase Realtime Database and MySQL are integrated for the cloud and local database management with the hardware and software parts for building the IoT technology environment and infrastructure.

An Approach to Digital Presentation of Traditional Bulgarian Embroidery Patterns

This paper studies problems related to the development of cross-stitch software as part of craft software. It treats an approach to the digital presentation of embroidery patterns. The software is designed for people engaged in traditional Bulgarian embroidery. In a digital way, the paper presents a variety of stitch types used in Bulgarian embroidery and a digital description of whole cross-stitch patterns. This study describes the entire pattern as a set of smaller groups of items. Often, some of these items are identical, which can facilitate pattern description. In general, the developed application is oriented toward Computer-aided Design stitch features of old cross-stitch patterns, which are found in ethnographic exhibits.

An Implementation of Communication, Computing and Control Tasks for Neuromorphic Robotics on Conventional Low-Power CPU Hardware

Bioinspired approaches tend to mimic some biological functions for the purpose of creating more efficient and robust systems. These can be implemented in both software and hardware designs. A neuromorphic software part can include, for example, Spiking Neural Networks (SNNs) or event-based representations. Regarding the hardware part, we can find different sensory systems, such as Dynamic Vision Sensors, touch sensors, and actuators, which are linked together through specific interface boards. To run real-time SNN models, specialised hardware such as SpiNNaker, Loihi, and TrueNorth have been implemented. However, neuromorphic computing is still in development, and neuromorphic platforms are still not easily accessible to researchers. In addition, for Neuromorphic Robotics, we often need specially designed and fabricated PCBs for communication with peripheral components and sensors. Therefore, we developed an all-in-one neuromorphic system that emulates neuromorphic computing by running a Virtual Machine on a conventional low-power CPU. The Virtual Machine includes Python and Brian2 simulation packages, which allow the running of SNNs, emulating neuromorphic hardware. An additional, significant advantage of using conventional hardware such as Raspberry Pi in comparison to purpose-built neuromorphic hardware is that we can utilise the built-in physical input–output (GPIO) and USB ports to directly communicate with sensors. As a proof of concept platform, a robotic goalkeeper has been implemented, using a Raspberry Pi 5 board and SNN model in Brian2. All the sensors, namely DVS128, with an infrared module as the touch sensor and Futaba S9257 as the actuator, were linked to a Raspberry Pi 5 board. We show that it is possible to simulate SNNs on a conventional low-power CPU running real-time tasks for low-latency and low-power robotic applications. Furthermore, the system excels in the goalkeeper task, achieving an overall accuracy of 84% across various environmental conditions while maintaining a maximum power consumption of 20 W. Additionally, it reaches 88% accuracy in the online controlled setup and 80% in the offline setup, marking an improvement over previous results. This work demonstrates that the combination of a conventional low-power CPU running a Virtual Machine with only selected software is a viable competitor to neuromorphic computing hardware for robotic applications.

Personal Finance Management Application

The paper presents a personal finance management mobile application developed for the Android operating system. The application is in the process of trials and test deployment among selected customers. It provides opportunities for managing a personal budget, retrieval of a financial status report for a certain period, working with expenses and income, report generation and visualization through charts, and barcode scanning. The methodology for designing and implementing the developed prototype includes pre-testing and preliminary interviews with potential customers who need convenient and easy access to manage their personal finances. Extensive research has been done on the availability of similar desktop and mobile applications. Appropriate technologies have been selected for the implementation of the program logic, software part and user interface. As a result, a mobile application has been developed that provides full integration with the web-based personal finance management system, previously created by the authors. The app is open to future improvements.

METHODS OF MATHEMATICAL ANALYSIS AND PROCESSING OF EEG SIGNALS: OVERVIEW OF THE CURRENT STATE AND LATEST TRENDS

Aim. With the help of retrospective analysis, establish the current level of achievements in the field of applying methods of mathematical analysis and processing of biomedical signals, in particular EEG, and characterize the main trends. Materials and methods. The research was conducted using theoretical methods, by studying and analyzing freely available literature, publications, materials of working groups of domestic and foreign scientists over the past few years. Results. The analysis of scientific works devoted to the development and development of EEG showed that over the past five years there has been a deepening of the tendency to separate the methods of improving the hardware and software parts of electroencephalographic complexes. The last of them, in turn, can be divided into several main directions. The first direction is the improvement of real-time EEG analysis methods, including automatic processing included in the software of EEG complexes. The second direction combines mathematical methods of signal analysis to detect existing pathologies and predict the onset of pathological conditions, such as statistical, spectral-correlation analysis, building mathematical models. The next direction brings together the application of mathematical methods for EEG signal processing and further use in deep learning, the use of convolutional neural networks to detect epileptic seizures, sleep disorders, differentiation of neuropsychiatric disorders, etc. The direction of using EEG in prosthetics deserves special attention. Methods of mathematical processing of the EEG signal are widely implemented in the development of bionic prostheses. The main achievements in these directions are considered in detail in the work. Conclusions. While the improvement of the material and technical part of the EEG is focused on the issues of improving the methods of recording biological signals, finding optimal methods of suppressing noise without losing the useful signal and does not currently have breakthrough results, the methods of analysis and processing of the already received signal have developed rapidly. The use of EEG to detect pathological conditions, the study of the neurophysiological basis of cognitive processes, etc., has always attracted the interest of scientists, but the use of neural networks for EEG processing and analysis and their implementation in robotics developments has given this direction a new impetus.

A Design and Construction of the Vibration Perception Threshold Measurement Device for Diabetes

A Design and Construction of the Vibration Perception Threshold Measurement Device for Diabetes

Phylodynamics and Phylogenetic Analysis of Recent SARS CoV 2 Viral Strains from Pune, Maharashtra

In December 2019, medical practitioners from China identified a novel strain of severe acute respiratory syndrome-CoV (SARS-CoV). The disease was allotted a zoonotic origin and the spillover event is associated with Huanan Wholesale Seafood Market in Wuhan City, Hubei Province, China. On 30 January 2020, the World Health Organization (WHO) declared COVID- 19 as an international public-health emergency concern. In India, the index case was reported on 30th January in the district of Thrissur, state of Kerala and by 25th March 2020, the country was lockdown. This research aims to analyze the viral phylogenetic and phylodynamics of strains from Pune, Maharashtra, followed by a comparison against real time data and an analysis of the potency of government prevention strategies. Publicly available SARS-CoV-2 sequences, specific to Pune, Maharashtra, were downloaded from GISAID during the time frame of the epidemics. Phylogenetic analysis of the sequences, including Maximum Likelihood trees and the nucleotide substitution model, were done using IQ-TREE software. Phylodynamic tools available as part of the BEAST2 software was used to estimate the evolution of R number through time. GTR+F+I was found to be the best-fit nucleotide substitution model for the data acquired. ML trees constructed reported a log likelihood of 44842.9931. Analysis of trace estimates reported R number with an average of 1, indicating transmission of virus. The data contains controversial elements which can only be clarified upon further sequencing of the SARS- CoV-2 strains, however, the results obtained are parallel to real time statistics.

Applications of AI

Abstract: Artificial Intelligence (AI) is a revolution in computer science that will become an integral part of all modern software in the coming years and decades. This situation creates opportunities as well as threats. Artificial intelligence will be used to improve defense and cybercrime. Additionally, new cyberattack techniques have been developed to exploit specific weaknesses in intelligence technology. Artificial intelligence is a broad field of computing that enables understanding, reasoning and action. The amount of information created by humans and machines today exceeds the human ability to absorb this information, interpret it and make complex decisions based on it. This article explores the characteristics, uses, development and success of artificial intelligence.

American Sign Language Translation from Deaf-Mute People Based on Novel System

This paper presents a system to translate gestures from the American Sign Language alphabet using an instrumented wearable glove. This system represents an attempt to utilize a slide potentiometer in sign language translating. The hardware part of the system consists of five slide potentiometers and two force-sensitive resistors, which are best positioned on a glove, based on the analysis of American Sign Language (ASL) letters. In the software part a neural network is used, which was built and trained using Google Colab and Python as the programming language. The performance of the system was tested on three data sets with different numbers of samples. After that, the letters corresponding to the gestures performed are displayed on a computer screen in real time.

An experimental study of the effect of solar tracking technology on the performance of a single phase open thermosyphon: a case study

ABSTRACT Recently, an increase in the demand for evacuated tube solar water heaters has been observed in Iraq due to their acceptable thermal efficiency, which reduces the dependence on electrical energy to heat water for domestic uses. The efficiency of the evacuated tube increases with the amount of received solar radiation. Therefore, in the present work, the evacuated tube has been installed at the center of the parabola cylinder to focus more sunlight on its surface. Since the sun has a relative movement concerning the earth, which is variable from one season to another, and during the day from sunrise to sunset, heating water with an evacuated tube solar water heater (ETSWH) has fluctuating temperatures. An effective technique for increasing the efficiency of a solar water heating system is solar tracking technology, which exploits solar radiation continuously during the day. The LDRs were utilized as sensors, and a 12 V linear actuator have been used to guide the position of the modified trough solar water heater (MTSWH). A microcontroller board (Arduino Uno) implements the program code (software part). The MTSWH was experimentally tested, and the results showed that installing a single-phase open thermosyphon evacuated tube at the focus of the parabolic cylinder (MTSWH) increased the thermal efficiency by 16.5%. Moreover, the implementation of solar tracking system led to an increase in the outlet water temperature by 32% and ensured a continuous supply of water throughout the day at high temperatures compared to the conventional trough solar water heater (CTSWH). This study revealed that the MTSWH performance results showed good agreement with previous studies.

Improving user security during a call

The recent development of mobile networks has led to the emergence of new threats and methods of implementing existing ones. Phishing attacks, including robocalls, are causing record losses to both individual users and large corporations. At the same time, existing countermeasures cannot provide protection against such attacks because most existing solutions focus on device authentication, whereas user authentication does not occur during a call. Another problem with mobile networks is that there is no point-to-point encryption, i.e., the speech is encrypted only on the segment from the subscriber to the base station. The subject of study in this article is the process of ensuring user security during a call. The purpose of this study is to develop a model of mutual user authentication and end-to-end data encryption in a mobile network during a call. The main objectives are the protection of users from spoofing and vishing and the proposal of a protection method by implementing mutual authentication of users during a call without storing confidential information on the side of a "trusted third party". Method of secure key exchange and end-to-end encryption during a call in the mobile network was proposed. It prevents the interception of calls by the operator for circuit-switched and packet networks. The methods used are mathematical modelling, ontological approach, and multi-criteria optimization models. Because of this research, an algorithm for mutual authentication of users is proposed by introducing biometric authentication methods and modifying the sequence of messages during a call. The proposed approach can be implemented for CS-call and VoLTE/VoWiFi calls. A call cannot be received without user biometric authentication; such as ear pattern or bone conduction methods. Modified SETUP and CONNECT ACK messages are used to inform the other party about the user verification result. This prevents user spoofing, call masquerading, and robocalls. A combination of the proposed asymmetric encryption, a short authentication string, and hashes of previous calls provides a higher level of confidentiality, integrity, and additional resistance to man-in-the-middle attacks. Conclusions. The scientific novelty of the obtained results is the integration of the above methods into the sequence of call flow messages for providing mutual authentication, end-to-end encryption, and counteraction to the number of network attacks. The proposed methods allow one level to increase the provision of services of privacy and observation groups and can be implemented in the software part of user equipment.

Research of the optimization problem of structure hierarchical communication network with changing its parameters

The article is devoted to the study of the problem of optimizing the hierarchical structure of a multicommodity communication network with discrete flows when changing its important parameters, such as the capacity of network arcs in transport blocks and the size of the transport block for transportation of the discrete small-lot cargo or of the data transmission in a digital communication network. The network has three levels of hierarchy – a backbone, a zonal and an internal and four types of nodes - backbone nodes of the first, second and third types, forming the backbone and zonal levels of the network, and nodes of the fourth type, which subordinate to each backbone node and forming the internal levels of the network. Types of nodes differ from each other in terms of functionality. The main task of the study is to establish how the structure of the backbone network changes (the number and location of backbone nodes of the first, second, and third types), the flow processing and distribution scheme, and the technical and economic indicators of the network's functioning for different values of its parameters. The principles of organization of sorting and distribution of flows in a three-level network and its mathematical model are given. A mathematical model of the optimization problem of the backbone network structure and flow sorting and distribution scheme is formulated. Algorithms for solving the problem are based on the discrete analogue of the local descent method proposed by the authors earlier, when the neighborhoods of the metric space of possible solutions are chosen based on heuristic considerations, taking into account the specifics of the problem being solved. Computer modeling of the problem on a network containing 10 nodes at the specified change in the both parameters is carried out. The modeling was carried out on the example of the transport network of cargo transportation using a computer program that is part of the instrumental software of the Information and Analytical Decision Support System (IA DSS), which is being developed at the Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine. An experimental study of solution the problem showed that the structure of the network is weakly dependent on the change in the carrying capacity of the arcs and the size of the transport block. The proposed computer technology for solving problem when the network parameters are changed allows you to interactively modeling various options of a network, changing the topology, hierarchical structure, flows, parameters and constraints of the model and from the set of the obtained solutions to choose a best option, taking into account the selected a goal function and the accepted constraints; calculate preliminary technical and economic indicators of the network's functioning, estimate the cost of additional resources and plan the amount of investment required for the modernization and construction of its structural elements, which ultimately makes it possible to increase the efficiency functioning of the network by optimizing use of its resources and reducing the operating costs for the processing and transportation of flows.

Msf, a forecasting library to predict short-term electricity demand based on multiple seasonal time series

Transmission system operators have a growing need for more accurate forecasting of electricity demand. Current electricity systems largely require demand forecasting so that the electricity market establishes electricity prices as well as the programming of production units. The companies that are part of the electrical system use exclusive software to obtain predictions, based on the use of time series and prediction tools, whether statistical or artificial intelligence. However, the most common form of prediction is based on hybrid models that use both technologies. In any case, it is software with a complicated structure, with a large number of associated variables and that requires a high computational load to make predictions. On many occasions, the resources to reach these predictions are not available to researchers. In this paper we present a MATLAB® toolbox created to predict electrical demand. The toolbox implements multiple seasonal time series forecasting methods such as new generalized multiple seasonal Holt–Winters exponential smoothing models and neural network models, among others. The models presented include novel discrete interval mobile seasonalities (DIMS) to improve forecasting on special days. The paper also describes the computational analysis conducted to apply the toolbox in various electrical systems in Europe, where the results obtained can be seen. The use of this library opens a new way of research for the use of models with discrete and complex seasonalities in other fields of application.

Fabrication, Design, and Analysis of Dual Axis Solar Tracking System

Energy crisis is one of the prime issues in the third world developing country like Bangladesh. There is an enormous gap between generation and demand of electrical energy. Nearly 50% of the population of the country is extremely isolated from this blessing. Renewable energy is the only answer to solve this issue. Solar energy is one of the most effective resources of renewable energy which could play a significant role in solving this crisis. This research presents a performance analysis of the dual axis solar tracking system using Arduino. The main objective of this research is whether a static solar panel is better than a solar tracker or not. This work is divided into two parts, hardware, and software system. In the hardware part, four light dependent resistors (LDR) is used to detect the utmost light source from the sun. Two servo motors conjointly used to move the solar panel to maximum light source location perceived by the LDRs. In the software part, the code is written by using C programming language and has targeted to the Arduino UNO controller. The outcome of the solar tracker system has analyzed and compared with the fixed or static solar panel found better performance in terms of voltage, current and power. Therefore, the solar tracker has proved more practical for capturing the maximum sunlight supply for star harvesting applications. The result showed dual- axis solar tracking system produced extra 10.53-watt power compared with fixed and single axis solar tracking system. Key Words: Solar Pannel, Servo Motor, LDR