Search
69 articles for “Noise generation”
-
New Ideas in Quantum RF
Abstract: Quantum RF Innovations is leading the way in next-generation wireless technologies by connecting old radio frequency systems with new quantum- enabled solutions. Our goal is to change the way signals are processed, communicated, and sensed by using advanced quantum principles and cutting- edge RF engineering. We are creating a new class of devices and systems that can achieve ultra-low-noise signal amplification, unprecedented spectrum control, and quantum-secured communication through interdisciplinary research …
Published in International Journal of Radio Frequency Innovations · Vol. 3, Issue 2, 2025 · pp. 35–43 Read article
-
Impact of Semiconductor Material Properties on Device Behavior and Circuit Performance: A Comprehensive Review
Abstract: Semiconductor materials play a fundamental role in determining the performance, efficiency, and reliability of modern electronic devices and circuits. This review presents a comprehensive analysis of how intrinsic and engineered material properties influence device behavior and overall circuit functionality. Key parameters such as bandgap energy, carrier mobility, thermal conductivity, and defect density are examined to understand their direct impact on switching speed, power consumption, and operational stability. The paper explores …
Published in Journal of Semiconductor Devices and Circuits · Vol. 13, Issue 1, 2026 · pp. 19–26 Read article
-
Low-cost Air Quality Monitoring System for Dhaka City
Abstract: AbstractContamination is the expansion of any substance or any type of vitality to nature at a rate quicker than it very well may be scattered, weakened, deteriorated, reused, or put away in some innocuous structure. Pollution has become a crucial problem for busy cities all around the world. There are many types of pollution among them: air pollution, water pollution, soil pollution, food pollution, noise pollution, light pollution, thermal pollution …
Published in Journal of Electronic Design Technology · Vol. 10, Issue 3, 2019 · pp. 33–41 Read article
-
Multi-Parameter Biomedical Sensor-Based Mental State Classification Using EEG And Deep Learning Techniques
Abstract: With mental health concerns becoming increasingly widespread, there is a strong need for systems that can monitor conditions like stress, anxiety, and fatigue in a continuous and non- invasive manner. This research proposes a novel multi-parameter biomedical sensing framework for mental state classification by integrating electroencephalography (EEG) signals with physiological parameters, including body temperature acquired using LM35 sensors, heart rate from pulse sensors, and blood oxygen saturation (SpO₂) measurements. The …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 1–8 Read article
-
A Smart Framework that Combines Data Mining and Optimization for Different Applications
Abstract: Blending predictive data mining with metaheuristic optimization has become essential for tackling tough, real-world problems across all kinds of fields. Most existing methods stick to fixed algorithms, each focused on a tiny slice of the puzzle, barely budging when new variables or unpredictability show up—especially with messy, human-generated data. So, here’s the idea: a Unified Metaheuristic and Predictive Data Mining (UMPDM) framework that finally connects adaptive search methods with powerful …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 · pp. 10–14 Read article
-
Data Integration and Visualization in Bioinformatics: Techniques and Challenges
Abstract: Data integration and visualization play essential roles in bioinformatics, facilitating the thorough analysis, and interpretation of intricate biological datasets. In the field of bioinformatics, vast amounts of data are generated from various experimental platforms, such as genomic sequencing, proteomics, transcriptomics, and metabolomics. However, the heterogeneity of these datasets, coupled with their large scale and complexity, presents significant challenges in terms of integration, analysis, and visualization. Data integration techniques aim to …
Published in Research and Reviews : Journal of Computational Biology · Vol. 13, Issue 3, 2024 · pp. 1–8 Read article
-
AI Driven IoT based Satellite remote sensing system: KSK Approach in Satellite Remote Sensing
Abstract: The convergence of the Internet of Things (IoT) and satellite remote sensing has traditionally been bottlenecked by massive data latency and limited downlink bandwidth. This paper proposes a decentralized framework for an "AI-Driven IoT-based Satellite Remote Sensing System," which shifts the paradigm from raw data transmission to onboard edge-intelligence. By integrating lightweight convolutional neural networks (CNNs) directly into satellite payloads, the system performs real-time feature extraction and anomaly detection before …
Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 1, 2026 · pp. 50–57 Read article
-
Advancements in AI-Driven Sound Spectrogram Analysis: From Deep Learning to Quantum and Neuromorphic Processing
Abstract: The rapid advancement of artificial intelligence (AI) has significantly reshaped the field of audio signal processing, with sound spectrogram analysis emerging as a central research focus. Spectrograms provide a rich time–frequency representation of audio signals, making them particularly suitable for data-driven learning approaches. This paper presents an in-depth and original review of modern AI-based techniques applied to spectrogram analysis, highlighting their growing impact across critical application areas such as healthcare …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 1, 2026 · pp. 01–06 Read article
-
Passive Digital Phenotyping for Longitudinal Burnout and Occupational Mental Health Surveillance: A Transformer-Based Explainable Deep Learning Approach Using Smartphone Behavioral Streams
Abstract: Occupational burnout constitutes a pervasive yet chronically under-surveilled public health threat, its insidious temporal evolution rendering episodic self-report instruments structurally inadequate for early detection. This paper introduces BurnoutSense, a passive digital phenotyping framework that continuously harvests eight heterogeneous smartphone behavioral data streams encompassing application usage ecology, communication metadata, geospatial mobility, screen interaction dynamics, inferred sleep rhythmicity, keystroke kinematics, ambient noise exposure, and battery/charging cadence to construct individualized multivariate behavioral signatures …
Published in International Journal of Behavioral Sciences · Vol. 3, Issue 2, 2026 · pp. 44–53 Read article
-
A Next-Generation IoT-Enabled Smart Cane for the Visually Impaired: Integration of Advanced Navigation, Context-Aware Obstacle Detection, and Real-Time Voice Guidance
Abstract: The rapid growth of Internet of Things (IoT) technologies, combined with advances in embedded systems and artificial intelligence, has opened new possibilities for developing assistive mobility solutions tailored to the needs of individuals with visual impairments. This paper introduces an IoT- enabled smart cane designed to enhance independent mobility through intelligent environmental interpretation and context-aware navigation. Unlike traditional canes that rely solely on tactile feedback, the proposed system incorporates multiple …
Published in Journal of Advancements in Robotics · Vol. 13, Issue 1, 2026 · pp. 12–17 Read article
-
Optimization of Machining Parameters in Electric Discharge Machining of Al 65032
Abstract: Electric discharge machining has the potential to maintain surface topography and produce less stresses and destruction to the components. In this machining there is no contact between the tool and the work piece. The spark generated due to the electrostatic field generation between tool and the work piece. The objective of this experimentation was to create guidelines for electric discharge machining of Al 65032. Aluminum alloy 65032 is one of …
Published in Journal of Materials & Metallurgical Engineering · Vol. 5, Issue 3, 2015 · pp. 15–22 Read article
-
Fractal-Entropy Guided Adaptive Signal Reconstruction for Non-Stationary Biomedical and Communication Systems
Abstract: This paper presents a novel Fractal-Entropy Guided Adaptive Signal Reconstruction (FEG- ASR) framework designed for accurate processing of non-stationary signals in biomedical and communication systems. The proposed approach integrates fractal dimension analysis with entropy- based feature evaluation to capture the intrinsic complexity and irregularity of time-varying signals. By dynamically adapting reconstruction parameters based on fractal-entropy measures, the method effectively separates noise from meaningful signal components while preserving critical information. The …
Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 · pp. 22–33 Read article
-
A Review on Two-Wheeled Self-Balancing Robot Using Spartan-3E FPGA for Sensor Fusion and Real-Time Motor Control
Abstract: Two-wheeled self-balancing robots (TWSBR) are a popular application of embedded control and robotics because they operate on the inverted pendulum concept, which is naturally unstable. The main objective of such robots is to continuously maintain balance by estimating the tilt angle and applying corrective motor action in real time. In most practical systems, low-cost inertial sensors such as accelerometers and gyroscopes are used for tilt measurement. However, accelerometer readings are …
Published in Journal of VLSI Design Tools and Technology · Vol. 16, Issue 1, 2026 · pp. 17–27 Read article
-
Machine Learning–Guided Cognitive RF System with Dynamic FFT Resolution and Multiplier Reconfiguration for Adaptive Anti-Jamming Communication
Abstract: This paper presents a hierarchical adaptive RF communication system that integrates signal quality-based pre- processing with machine learning-driven signal classification to achieve robust and resource-efficient operation in dynamic, interference-prone environments. Unlike prior art that addresses adaptive RF, ML classification, or anti-jamming individually, this work uniquely combines real-time SNR/RSSI-based signal strength estimation with dynamic FFT size selection (64-, 256- , or 512-point) and arithmetic-level multiplier reconfiguration (CORDIC, Distributed Arithmetic, and hybrid …
Published in International Journal of Radio Frequency Innovations · Vol. 4, Issue 1, 2026 Read article
-
Load Balancing Approach for Proposed Hybrid Scheduling Algorithm in Heterogeneous Networks
Abstract: AbstractRecent developments in multiple technologies evolving nowadays provide users with broadband services of high quality and seamless mobility. Wireless networks comprising of third generation (3G) and fourth generation (4G) are on the verge to provide wide coverage and good mobility capabilities. All these high quality wireless networks have been deployed within one region overlapping each other, hence generating a heterogeneous network for wireless access which can be represented as 4G …
Published in Current Trends in Signal Processing · Vol. 8, Issue 1, 2018 · pp. 1–9 Read article
-
Deep Learning Enhanced Compressive Sensing for Wireless IoT Data Optimization and Weather Monitoring.
Abstract: This research explores the application of deep learning and compressive sensing in order to optimize data traffic in non-orthogonal multiple access (NOMA)-based wireless internet of things (IoT) networks and weather monitoring. Such a framework would be very effective and overcome pilot attacks and reconstruction losses for secure data transmission. In this regard, a strong communication model has been adopted based on power-domain NOMA for simultaneous wireless transmission by multiple IoT …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 20–36 Read article
-
Reverse Engineering’s Neural Network Approach to the Human Brain
Abstract: The organic components of the brain are capable of performing consistently and successfully in noisy situations. Brain components with highly adaptive or flexible interactions that could be low-precision, unpredictable, or excessively simultaneous are used to construct biological circuits. Two of the most remarkable characteristics of brain networks are their propensity to self-organize and their pattern organization. Recent research on neural networks, including artificial neural networks and convolutional neural networks (CNN), …
Published in Journal of Communication Engineering & Systems · Vol. 12, Issue 2, 2022 · pp. 17–24 Read article
-
Simulation Results of Harmonic Compensation by Using PQ Based PI Controller for Grid Connected Current Controlled DG Unit
Abstract: With the development of new technologies and electronic devices, one of the main topics of special concern is the aspect of power quality, amongst which control and prediction of harmonics are of utmost importance. The presence of harmonics caused by nonlinear loads which are common in industrial plants and large scale office buildings means a threat to the sensitive equipments like that of computers, adjustable speed drives, power electronics load …
Published in Trends in Electrical Engineering · Vol. 6, Issue 2, 2016 · pp. 64–72 Read article
-
A Review of Blocking Side-Channel Threats in Parallel Cloud Systems
Abstract: Side-channel attacks (SCAs) pose a critical security threat to parallel computing systems, particularly in shared cloud environments where multi-tenancy and resource contention create exploitable vulnerabilities. This study presents a comprehensive review of SCAs in parallel architectures, analyzing attack vectors such as cache-based exploits (e.g., Prime + Probe, Flush + Reload), timing attacks, power analysis, and network-based covert channels. We examine real-world cases including Spectre and Meltdown vulnerabilities that exposed fundamental …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 2, 2025 · pp. 15–25 Read article
-
Evaluation of Mechanical Property and Optimization of the Dry Sliding Wear Behavior of Carbon Black-Alumina-Epoxy-Glass Composite by using Design of Experiments
Abstract: The research work is mainly focused on investigating the effects of wear parameters: filler content (0%, 4% CB, 4% Al2O3,and 2% CB + 2% Al2O3), applied load (4.905, 9.810, 14.710, and 19.62 N), sliding distance (20, 30, 40, and 50 mm) by using Design of Experiments (DOEs) approach. The experimental results were analyzed using MINITAB V16 scientific graphical and data analysis software. Signal-to-Noise (S/N) ratio, analysis of variance (ANOVA) proved …
Published in Journal of Materials & Metallurgical Engineering · Vol. 4, Issue 2, 2014 · pp. 21–31 Read article