Because of the prohibitive costs and limited availability of traditional energy sources, alternative energy sources are used increasingly by many countries. Renewable energy, particularly solar energy, has become more popular because it is sustainable, available in abundance, and is eco-friendly. Solar power systems are grid-connected, but their efficiency depends heavily on maximizing energy harvest from sunlight, which varies throughout the day. To address this, researchers compare three Maximum Power Point Tracking (MPPT) methods Perturb and Observe (P&O), Incremental Conductance (INC), and Fuzzy Logic Control to determine the most effective approach for optimizing power extraction. These techniques are simulated in MATLAB Simulink using a DC-DC converter connected to a load, where MPPT algorithms adjust the converter's gate pulses to keep the system operating near its maximum power point (MPP) under changing conditions. By finetuning the voltage and current dynamically, these strategies help solar systems achieve peak performance and improve overall energy yields. The techniques are analyzed based on performance: response time, overshoot, and oscillation effect. First, we evaluated the Perturb and Observe (P&O) method and identified its limitations. Then we evaluated the Incremental Conductance (INC) approach, which showed better performance. Finally, we implemented a fuzzy logic controller that demonstrated superior accuracy and stability. Through graphical and statistical analysis, we compared all three methods and found the fuzzy logic solution delivered the highest efficiency and most reliable operation, making it the best choice for maximum power point tracking
A significant number of people who have impairments are dependent on other people for help with day-to-day activities, particularly with regard to mobility. Users in wheelchairs, in particular, often need assistance in order to operate their chairs. Their freedom may be increased via the use of a wheelchair control system that gives them the ability to control their mobility through the use of speech recognition technology. Microcontroller, motor control interface board, and Google Assistant are all components that are included into this system to provide voice command capabilities. Users are able to manoeuvre the wheelchair by just speaking orders to Google Assistant. These commands include turning left or right, going forward or backward, and turning left or right. Through the usage of this system, users are able to become more self-sufficient, the stress placed on carers is lessened, and they are given the ability to engage more actively in day-to-day life
Background: Cochlear Implant (CI) partially replaces the functions of the cochlea, converting sound energy into electrical signals, enabling electrical stimulation of the auditory nerve and transmission to the cerebral cortex. This study investigates auditory nerve Recovery Time (REC) and introduces the Frequency Following Response (FFR) with stimulus |da| as tools to assess auditory system integrity in CI users, particularly concerning speech perception in quiet and noise. This research aims to explore the correlation between REC, neural conduction in the brainstem, and speech recognition performance in CI users, contributing to the understanding and enhancement of this technology. Materials and Methods: This was a prospective, cross-sectional, exploratory study, approved by the Institutional Review Board (IRB) of the hospital where the study was conducted. It involved 06 adults with postlingual deafness who underwent cochlear implant surgery, three women (mean age 67 years) and three men (mean age 70 years), three right ears and three left ears. Participants exhibited free-field auditory thresholds not exceeding 25dBHL from 250Hz to 6000Hz, with 70% speech recognition in quiet, stable electrode impedances, and present neural response (evoked compound action potential). Data collection included sentence recognition in quiet and noise, recovery time parameters involving absolute refractory period "T0," relative refractory period "tau", and saturation amplitude "A", assessed in three cochlear regions (apical electrode 16, medial electrode 11, and basal electrode 6). FFR included the investigation of wave "V" and valleys "A, C, D, E, F, and O”. Results: The results revealed moderate to strong positive and negative correlations between REC parameters, FFR latencies, and speech recognition in quiet and noise. Statistically significant correlations were particularly observed between REC ("T0, tau, and A"), in electrodes 11 and 6, and valleys "D and E". Conclusion: This study demonstrated statistical correlation between auditory nerve recovery time and brainstem neural conduction for speech in CI users
Industrial air pollution refers to the release of hazardous substances into the atmosphere by manufacturing units such as cement factories, thermal power stations, pharmaceutical plants, and fertilizer units. These emissions often include toxic gases and fine particulate matter, which can lead to serious health concerns, particularly respiratory illnesses like asthma, bronchitis, and even lung cancer. Typically, pollutants are expelled into the air through tall exhaust chimneys. In this proposed concept, a specially designed air purification chamber is installed at the top of such exhaust pipes. This chamber incorporates a two-stage filtration system made from cellulose acetate fiber filters and activated carbon filters. A suction fan mounted at the chamber’s outlet draws the polluted air through the filters, allowing cleaner air to be released into the atmosphere. The chamber is equipped with two air quality monitoring sensors to assess pollution levels before and after the filtration process. These readings are processed and displayed on an LCD screen, providing a clear comparison of air quality improvement. Additionally, a Wi-Fi module transmits this data in real time to the factory owner's mobile device, enabling continuous monitoring. The control system is powered by an Arduino Nano microcontroller, and MQ135 gas sensors are employed to detect air contaminants. This setup allows factory personnel to stay informed about the effectiveness of the air purification system, without needing to physically inspect the exhaust areas. For demonstration purposes, a scaled-down prototype of an industrial exhaust pipe integrated with the air cleaning mechanism will be constructed. This model will visually and functionally validate the working of the system. The effectiveness of air purification is evaluated using the Air Quality Index (AQI), which represents the level of pollutants in the air. In this project, AQI values are interpreted as percentages to simplify real-time analysis. Essentially, air quality assessment helps determine how clean or polluted the air is, both before and after the implementation of the cleaning system.
The rapid advancements in the power electronics sector have led to the evolution of multilevel inverters (MLIs) for various applications. Today, MLIs are preferred over conventional two-level inverters due to several advantages, such as lower voltage stress, reduced electromagnetic interference, and smaller filter size requirements. However, traditional MLIs often require a higher number of components to generate more voltage levels. To address this, this paper introduces a novel 7-level MLI with a reduced switch count, designed specifically for standalone energy systems. To efficiently control the system and reduce harmonics, a fireflyassisted Glowworm Swarm Optimization (GSO) algorithm is applied for selective harmonic elimination (SHE). The Moth-Flame Optimization (MFO) algorithm is utilized to eliminate low-order harmonics from the output voltage of the proposed MLI. Additionally, the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) are implemented to compare their effectiveness with the MFO algorithm. An Incremental Conductance (IC) algorithm is used to maximize power extraction from the energy system. The overall system is simulated in the MATLAB environment, with individual results discussed in detail. Finally, an experimental test setup validates the integrated MLI's performance with the SHE PWM control scheme, and the results are compared with those from traditional PWM control techniques
This study presents a comprehensive intelligent vehicular safety and monitoring system designed within the Internet of Things (IoT) paradigm to enhance real-time driver and vehicle condition assessment. The proposed system integrates a heterogeneous network of sensors including alcohol detection, eye-blink monitoring, accelerometers, and GPS/GSM modules with edge-computing-enabled microcontrollers to provide continuous, real-time situational awareness. Leveraging machine learning algorithms, the system improves detection accuracy for critical events such as driver inebriation, fatigue-induced micro-sleep, and collision impacts, while minimizing false alarms in dynamic operational environments. The architecture addresses key challenges such as sensor drift, noise filtering, and context-aware processing within constrained hardware resources. Automatic alerting mechanisms facilitate timely emergency response, while telematics connectivity supports remote monitoring and vehicle theft prevention. The modular design and cost-effective hardware choices position this solution as viable for both commercial fleet management and private passenger vehicles, offering a scalable approach to intelligent transportation system integration and autonomous vehicle ecosystems.
An enhanced Phase Frequency Detector (PFD) and Voltage-Controlled Oscillator (VCO) are designed to improve performance in frequency synthesis and clock generation. The PFD achieves reduced dead zone, faster response times, and lower power consumption, ensuring greater accuracy and efficiency in phase detection. The VCO offers a wide tuning range, low phase noise, and high sensitivity, all while maintaining a low power profile, making it suitable for high-frequency applications. Designed and evaluated using Cadence EDA tools, the performance of these components is validated through simulations and experiments, demonstrating their suitability for integration into advanced communication and signal processing systems
Information should be shared effectively and securely to protect users' personal identifiable information. Using a chatbot, or a computer application to share information presents a likelihood of identity theft if not properly secured. In communicating with chatbots, the user’s information is held in the chatbot application database. The availability of user information on this application may give opportunity to attackers to hijack personal user information to perpetrate crime. Unfortunately, current chatbot applications do not have a mechanism to delete or remove personal identifiable information of users. Some chatbots accept users' personal identifiable information with no recourse to the user on what happens after the chat session. To prevent a third party from trading, selling, or hacking the data, this research designed a decision system to be added to the chatbot application that will enable the user to consciously decide on how their personal identifiable information should be treated or kept. The chatbot was implemented in Hypertext Markup Language (HTML), using PHP and MySQL for standard web development. Also, a PHP server was installed for the system to relate properly with the database server in fetching and storing the data. The system was tested using likelihoods that may lead to the success of the program. This was done repeatedly with different test data. The new system provides users consent on whether their personal information is left on the chatbot app or removed completely.
In this digital world automation is most effective and essential in all aspects. Getting and maintaining classroom attendance for every hour in a day at school or college by calling names and marking on book is very hectic and time-consuming, there are some chances of proxy attendance. There are many automated human identification techniques such as biometrics, RFID, voice recognition still those techniques some issues systems are vulnerable to proxies. But we are implementing the multiple face recognition technique to get all candidate in single or multiple frames to get attendance conformation. The algorithms are implemented using a series of signal processing methods including Harr-cascade classifier, Local Binary Pattern (LBP), Haar-like feature, facial image pre-processing and Principal Component Analysis (PCA). The Ada Boost algorithm is implemented in a cascade classifier to train the face and eye detectors with robust detection accuracy. Faces are detected and recognized from live streaming video of the classroom. Attendance will be updated student details with date and time to online database
Background: With the advancement of Industry 4.0, the automation of industrial processes has shifted from wired communication technologies to wireless networks, enabling greater flexibility, efficiency, and scalability. Technologies such as LoRaWAN and NB-IoT have been widely adopted due to their low energy consumption and long-range communication capabilities, which are essential for industrial applications. However, the increasing density of connected devices has introduced new challenges, particularly related to electromagnetic interference (EMI), which can degrade network performance, cause packet loss, and delay communication. EMI, caused by various sources such as electronic devices and natural phenomena, is a critical issue in industrial environments. EMI simulations and analyses allow for network parameter adjustments and the implementation of mitigation techniques, ensuring robust communications. This study's primary objective is to test the radiated electromagnetic immunity of the LoRa protocol, assessing its ability to operate in environments subject to electromagnetic interference. The research emphasizes the importance of understanding EMI impacts to preserve data accuracy, improve communication performance, and ensure regulatory compliance, fostering a safe, reliable, and efficient industrial environment. Materials and Methods: The study evaluates the radiated electromagnetic immunity of Heltec LoRa ESP32 V2 boards, essential for IoT applications, in compliance with the IEC 61000-4-3 standard, which establishes methods for testing immunity to high-frequency electromagnetic fields. The boards were tested in an anechoic chamber to analyze the impact of radiated fields on LoRa communication. During the tests, conducted at frequencies from 80 MHz to 6 GHz and with a field intensity of 10 V/m, a transmitting board continuously sent LoRa packets while the receiving board recorded RSSI values and packet reception rates. The results demonstrated the sensitivity of the LoRa protocol to electromagnetic interference, evidenced by RSSI variations and packet losses under certain conditions. The tests highlighted the importance of robust configurations to maintain data integrity and device functionality in challenging industrial environments. The study provides valuable insights for improving electromagnetic compatibility in Industry 4.0 and IoT applications, meeting the criteria established by the IEC 61000-4-3 standard. Results: The study identified critical communication disruptions in the LoRa system at specific frequencies (305 MHz and 730 MHz) under radiated electromagnetic fields with a power of 1.5 dBm, characterized by packet loss and performance degradation. At 3 dBm, total communication failure occurred, attributed to internal resonances in the RF components and insufficient shielding or filtering of the modules. The interference likely overlapped with harmonics of the LoRa signal, reducing its ability to distinguish useful signals from noise. These findings highlight the system's vulnerability to specific EMI conditions, emphasizing the need for improved filtering, shielding, and protocol adjustments to enhance robustness in industrial environments. Conclusion: The study finds LoRa unstable at 305 MHz and 705 MHz with 1.5 dBm, causing packet loss and potential communication failure, highlighting the need for mitigation in high-interference environments.
This paper presents an ESP32-based Home Automation System designed for efficient management of household components, enhancing convenience, safety, and resource optimization. The system integrates various sensors and devices, including relay-controlled bulbs and fans, motion-sensor lights, and automated nightlights. Environmental monitoring is achieved via a DHT22 sensor for temperature and humidity, and MQ2/MQ135 sensors for gas detection, promoting real-time safety alerts. For smart gardening, a soil moisture sensor coupled with a submersible pump enables automated irrigation. Overheat protection ensures safety by automatically adjusting devices to prevent overheating. All components are managed through a mobile app using Wi-Fi, offering intuitive control and real-time monitoring. This project demonstrates the potential of ESP32 in building connected, intelligent home environments, driving the evolution of smart living systems
With the continuous development of radio and aviation technology, the research on Flying Ad-hoc Networks (FANET) has become the hotspot. FANET is a distributed network composed of multiple Unmanned Aerial Vehicles (UAVs) in a self-organized form, which has great potential for application in both military and civil fields. Different application scenarios have different requirements for FANET channel resource allocation. To facilitate future in-depth research on FANET, a comprehensive investigation into FANET routing protocols and related knowledge was conducted. Firstly, common mobility models and routing techniques in routing protocols are introduced. Then, FANET routing protocols are reviewed and analyzed based on existing knowledge of routing protocols. Routing protocols are classified into five categories, and each discussed routing algorithm is introduced in detail from the perspectives of principles, strengths and weaknesses, and applicability scenarios. Finally, the problems and current status of optimization of OLSR routing protocols are discussed.
The huge cost and high labor demand associated with the current deep litter system of poultry farming poses a great challenge to poultry farming. These drawbacks which currently threatens this system of farming, have been attributed to the over-involvement of humans in almost all aspects of operation of this farming system. The consequences are increased disease outbreak, weakness and poor bird feeding. To ameliorated some of these challenges, an automated poultry feeding system is developed and presented in this paper. The proposed system mimics the roles of real-life poultry attendants in delivery of feed and water to birds at specified interval of time. The system controls the dispensing of poultry feed (liquid and solid) through a program written on ATmega328P microcontroller via the Arduino Uno. The system senses the level of feed and water in the respective trough and intelligently dispense water and feed in response to the sensed levels. The current system is able to effectively sense and predict the level of feed and water in the farm at an accuracy of 98.79% and dispense same within a response time of 50-60ms.
Cardiovascular diseases are becoming increasingly prevalent due to lifestyle factors such as poor diet, lack of exercise, and conditions like diabetes and hypertension. Electrocardiography (ECG) is a widely used diagnostic tool for detecting various heart conditions, including arrhythmias and myocardial infarction. However, manual analysis of ECG signals is often subjective, time-consuming, and prone to variability. To address these challenges, this paper proposes a comprehensive system that integrates an Arduino-based ECG acquisition module with neural networks for automatic analysis and a robotic system for autonomous sensor placement and intervention. The main objective is to create a smart, real-time ECG monitoring and Computer-Aided Diagnosis (CAD) system capable of early detection and management of heart diseases. The system utilizes machine learning and deep learning techniques to enhance diagnostic accuracy, focusing on implementing a neural network model for ECG classification. A robotic arm is also integrated into the system to ensure precise sensor placement and emergency response
In today's world, there are many new technologies changing the way we do things. One interesting technology is the home automation system. It helps people control things in their homes from far away, making life more comfortable, saving money, and being easy to use. But some people find it a bit tricky to use these systems well. To make it simpler for everyone, we can use something called a flex sensor-based home automation system. This system lets you control things by moving your hands, which is really helpful for older people or those who might find it a bit hard to move. It's also great for people who haven't had much learning. And if we add voice help, it becomes even easier. This way, people who are in bed or dealing with physical problems can use it too. This makes technology not just cool but helpful for everyone in different situations. In this paper, we explore the integration of flex sensors onto hand gloves to facilitate a dynamic interaction between hand movements and technological outputs. The flex sensors, akin to miniature potentiometers, are strategically affixed to the fingers, registering changes in value corresponding to the bending action. As the finger bends, the sensor's resistance alters, influencing the output in an inversely proportional manner. This innovative system allows for nuanced control, where specific angles of finger bending lead to calibrated adjustments in output, demonstrating a responsive and intuitive interface between human gestures and technology. The implementation of flex sensor- based gloves introduces a versatile means of capturing and translating hand movements into actionable data. The project leverages the concept of resistance modulation to precisely interpret the degree of finger bending, creating a reliable framework for diverse applications such as gesturecontrolled devices or assistive technologies for individuals with limited mobility. The abstracted communication between human hand gestures and technology opens avenues for accessible and intuitive interfaces, promising potential applications across various domains.
In the field of signal processing, a new area of research has been introduced namely genomic signal processing (GSP). GSP processes genes, proteins, and DNA sequences using various hidden signals. As some genetic abnormalities turn into cancer diseases, proper understanding, and analysis of genes and proteins may lead to a new horizon in cancer genomic study. In genomic signal processing, identifying and classifying the diseased gene is a great challenge to researchers. Hence in the present paper, the crucial job of gene identification and classification is attempted for cancer detection. Our project is implemented in MATLAB R2019a using the bioinformatics toolbox. Where the DNA sequences obtained from the NCBI are processed and numerically mapped before extracting the exons using the period-3 property which is done using an anti-notch filter and STFT. Digital filters are used for noise reduction and increased accuracy.
This paper presents a survey of data related to development of the communication satellites for TV broadcasting, located in the geostationary orbit, for the period starting with the emergence of the first commercial satellite television broadcasting in 1965 up to 2023. It presents development of the satellite network technology through the number of satellites and active satellite transponders in the Ku and C bands at the end of each year for the mentioned period. These numbers are presented graphically for differentgeographical zones
This research paper investigates the relationship between solar wind intensity and cosmic ray flux in the outer heliosphere, utilizing data from Voyager 1 and the Interstellar Boundary Explorer (IBEX). Voyager 1’s measurements beyond the heliopause, where the solar wind significantly weakens, show a marked increase in cosmic ray intensities, suggesting that solar wind acts as a modulating force. Meanwhile, IBEX's all-sky maps of energetic neutral atoms (ENAs) offer complementary data, demonstrating how the solar wind interacts with the interstellar medium at the boundary of the solar system. By analyzing the inverse correlation between solar wind strength and cosmic ray penetration into the heliosphere, this study provides a comprehensive understanding of the dynamic processes occurring at the solar system’s edge. The combined data from these missions not only enhance our understanding of space weather but also provide valuable insights into the broader interaction between the heliosphere and the interstellar medium. This research underscores the significance of continuous monitoring and multi-mission data integration for advancing our understanding of cosmic ray modulation and solar wind behavior in the outer reaches of the solar system.
Plagiarism has been the problem of era in different acknowledge fields, particularly in the academic community, theses a battle between the plagiarism epidemic and detection create a rivalry between the machines and humans on both sides negative and positive, i.e.In plagiarisms cases and protecting and detecting the plagiarism. In this paper the work was on some human calculations for detecting the plagiarism and similarity in text documents and their relationship for automatically detecting the plagiarism mentioned tools and results of plagiarism detection tools for Arabic and English speeches, with calculate the precision and recall and F-meter for the mentioned software.
This work investigated the optimum location of Erbium Doped Fiber Amplifier (EDFA) in an optical system based on analysis of BER analyzer metrics by simulation approach using Optisystem software.The simulation model will be studied based on many parameters as input power (dBm), gain of Amplifier (dBm), fiber cable length (km) and attenuation coefficient (dB/km), there are two different parameters will be analyzed at five different locations of EDFA which are Q-Factor and Bit Error Rate (BER) and also Eye Diagram, which Q-factor and BER are measurement parameters used to measure the quality of received signal at receiver.
