Real Time Surge Voltage Control using Smart IoT
Efficient Transmission of FECG Signal using MIMO – OFDM
Deep Convolutional Auto-Encoders for Emotion Recognition from Facial Expressions
A Survey on Network Admission Control Solution in 6LoWPAN using Cryptographic Mechanism
A Comprehensive Review of Visible Light Communication (VLC)
Cognitive Radio Simulator for Mobile Networks: Design and Implementation
Reduced End-To-End Delay for Manets using SHSP-EA3ACK Algorithm
Light Fidelity Design for Audio Transmission Using Light Dependent Resistor
Dynamic Digital Parking System
Performance Analysis of Multi User Transmit Antenna Selection Systems over TWDP Fading Channels
Comparison of Wavelet Transforms For Denoising And Analysis Of PCG Signal
Video Shot Boundary Detection – Comparison of Color Histogram and Gist Method
Curvelets with New Quantizer for Image Compression
Comparison of Hybrid Diversity Systems Over Rayleigh Fading Channel
Design of Close Loop Dual-Band BPF Using CascadedOpen Loop Triangular Ring Resonator Loaded With Open Stubs
As the demand for energy is increasing exponentially due to the growing population, it needs to be properly monitored and controlled. On the other hand, nowadays, we are facing a lack of technical knowledge in monitoring and controlling energy consumption. Over the past several years, there has been a rapid increase in both the utilization of energy loads and the use of various forms of power electronic equipment. As a result, contamination of the electricity supply is becoming an increasingly critical issue. The purpose of this study is to provide the design and implementation of a virtual instrument tool that uses LabVIEW for real-time online monitoring of power quality. Readings are traditionally extracted manually from devices of this kind. A smart energy meter can help overcome these problems, allowing people to take readings from any location globally, thanks to technology that makes use of the Internet of Things. In addition to the amount of energy being consumed, the smart energy meter will provide information on other characteristics such as voltage, current, power, and frequency. This enables us to achieve the ideal level of load control. For load management, LabVIEW is the tool of choice. Users can tailor their pricing plans to their financial situations using this technology. The PZEM-004T sensor is utilized for measuring parameters, and the Node MCU is the component that transmits the data to the server. Users can exert influence over the system through both mobile applications and web applications. This system also helps detect surge conditions of appliances and can break them using a relay.
The extraction of Fetal Electrocardiogram (FECG) during labor or prenatal phases of pregnancy holds significant importance for early prediction of heart abnormalities. The accuracy of the extracted FECG is crucial for effective diagnosis, but the presence of external noises, particularly from the Maternal ECG (MECG), poses a major challenge in obtaining precise information. To address this issue in biomedical data processing, the study employs Finite Impulse Response (FIR) filters using an array multiplier. One notable challenge encountered in this process is the interference caused by external noises, leading to higher delay and power dissipation. In response, a modified High-Performance Multiplier (HPM) based modified booth multiplier is thoroughly reviewed and validated. This modification aims to enhance overall performance and enable high-speed operations in filtering the FECG signals. The effectiveness of these modified multipliers is assessed using ECG signal information collected from the MIT-BIH Arrhythmia Database, comprising 120 samples. In addition to noise filtering, the study explores the validation of Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) transceivers. These transceivers play a crucial role in ensuring the effective transmission of the extracted FECG signals. The research reveals a significant reduction, up to 80.4%, in both area and power dissipation during simulations conducted in Xilinx ISE 9.1 and Cadence Virtuoso. This achievement highlights the potential for improved efficiency and reliability in the processing and transmission of FECG signals, paving the way for advancements in early detection of fetal heart abnormalities.
One of the unsolved problems in computer vision is recognizing or understanding other people's emotions and feelings. Deep Convolutional Neural Networks (CNNs) have attempted to be effective in addressing emotion recognition issues. The significant level of performance achieved by these classifiers can be attributed to their ability to self-learn a downsampled feature vector that retains abstraction information through filter kernels in convolutional layers. Despite these advancements, challenges persist in capturing subtle and context-dependent emotional nuances, hindering the development of more nuanced emotion recognition systems. Ongoing research explores multimodal approaches, integrating various sensory inputs, to enhance the accuracy and reliability of emotion detection in real-world scenarios. In this paper, we explore the impact of training the initial weights in an unsupervised manner. We study the results of pretraining a Deep CNN as a Convolutional Auto-Multiplexer (CAM) in a greedy layer-wise unsupervised fashion for emotion recognition using facial feature images. When trained with randomly initialized weights, our CNN emotion recognition model achieves a performance rate of 92.16% on the Karolinska Directed Emotional Faces (KDEF) dataset. In contrast, by using this pre-trained model, the performance increases to 93.52%. Pre-training our CNN as a CAM also marginally reduces training time.
This paper introduces a robust network admission control (NAC) solution tailored specifically for 6LoWPAN Wireless Sensor Networks (WSN), aiming to enhance the overall security infrastructure. By implementing stringent measures, this solution not only safeguards legitimate nodes but also fortifies the network against potential security threats originating from unauthorized nodes attempting to communicate with both legitimate nodes and the Internet. The solution enhances security through innovative features for administrative nodes, including node presence detection and robust authentication. It incorporates data filtering to swiftly identify and discard unauthorized frames, strengthening network resilience. Established protocols like Neighbor Discovery and DSDV facilitate seamless integration. This strategic utilization minimizes the reliance on additional control messages, thereby optimizing the network's efficiency and resource utilization. The solution utilizes cryptographic methods, particularly the XOR algorithm, to enhance data security in wireless sensor networks. This algorithm ensures authenticity, integrity, and freshness of transmitted data frames, providing robust protection against manipulation and bolstering network security. The proposed network admission control solution addresses security risks in 6LoWPAN WSN and integrates advanced features for a strong and efficient overall security infrastructure in wireless sensor networks.
Visible Light Communication (VLC) represents a revolutionary approach in the realm of wireless communication, capitalizing on the dual functionality of Light Emitting Diodes (LEDs) for both illumination and high-speed data transmission. The advent of LEDs has not only transformed lighting systems but has also paved the way for innovative communication technologies. The seamless integration of illumination and data transmission in VLC offers a unique solution to the growing demand for efficient and high-speed wireless communication. In recent years, the field of visible light communication has witnessed remarkable progress, fueled by the exploration and refinement of its constituent elements. Researchers have been actively addressing challenges associated with VLC, such as signal interference, mobility issues, and the need for standardized protocols. This ongoing pursuit of solutions has led to the development of increasingly robust VLC systems, positioning them as promising contenders for future communication technologies. This paper endeavors to provide a comprehensive overview of VLC, delving into the intricacies of its underlying challenges and the innovative solutions that researchers have proposed to overcome them. By examining recent research trends, the paper aims to highlight the evolving landscape of VLC technology, showcasing its potential for revolutionizing optical wireless communication. As VLC continues to mature, it is essential to explore the current state of the technology, anticipate future advancements, and contribute to the collective knowledge shaping the trajectory of visible light communication.
