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39 articles for “CPU”
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Design and Implementation of Low Latency 16-bit Full Adder
Abstract: In the new age world, modern technology has vast applications in various fields of engineering, medicine, and others. Digital electronics processing and systems play a vital role in the analysis and computation of data representation. Electronic systems have gradually become faster and smaller scale. The primary structural element of any modern ALU-based processor is an adder. Addition is a well-known extremely basic function that is employed in nearly all computing …
Published in Journal of VLSI Design Tools and Technology · Vol. 14, Issue 3, 2024 · pp. 21–31 Read article
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Optimizing Image Processing with Verilog on FPGA: Techniques and Performance Enhancements
Abstract: The integration of image processing algorithms into hardware platforms, particularly FPGAs, presents a compelling opportunity for achieving high performance, low latency, and power efficiency in real-time applications. This study focuses on designing and implementing Verilog HDL-based optimal image processing methods for FPGA-based systems. The study explores the development of core algorithms, including edge detection, image enhancement, and adaptive filtering, to maximize resource utilization and processing speed on hardware platforms. Key …
Published in Journal of Semiconductor Devices and Circuits · Vol. 11, Issue 3, 2024 · pp. 46–53 Read article
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Advanced Anomaly Detection in Cloud Infrastructures Using Deep Learning Algorithms
Abstract: It is critical to guarantee the stability and security of cloud environments as cloud computing is becoming the backbone of contemporary IT infrastructures. Neglecting to quickly identify and resolve anomalies, which might point to security breaches, performance problems, or system breakdowns, can lead to disastrous outcomes. The increasing size and complexity of cloud infrastructures are challenging the effectiveness of traditional anomaly detection methods. These approaches often depend on rule-based systems …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 1–11 Read article
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A Hybrid Algorithm for Processor Scheduling Using Game Theory Variants
Abstract: This study proposes a novel hybrid algorithm for processor scheduling in modern operating systems, integrating the strengths of traditional scheduling methods with game theory variants. Traditional schedulers often struggle to adapt to dynamic workload changes, leading to suboptimal performance. Our hybrid approach addresses this by treating processes as "players" in a game, where the "payoff" is CPU time. A base scheduler (e.g., Weighted Fair Queuing, Earliest Deadline First) provides a …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 1, 2025 · pp. 48–56 Read article
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Machine Learning Revolutionizing Server Management and Performance
Abstract: The modern data center is a complex and dynamic environment, grappling with ever-increasing workloads, stringent performance demands, and the constant pressure for cost optimization. As such, applying machine learning (ML) directly to the server infrastructure offers a powerful avenue for achieving advanced automation, resource optimization, and proactive problem resolution. This article explores the transformative potential of integrating machine learning into server systems, leveraging insights gleaned from the abstract and conclusion …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 36–44 Read article
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State of the Art: A Pandemic Big HealthCare Analytics Solution: Image Data Classification Using Quantum MAML
Abstract: The modern age is facing many pandemic healthcare problems, e.g., covid 19, infections, inflammations, and many more, leading to critical, deadly situations. Survival rate can be increased with proper diagnosis of such data. We have proposed one of the implementations based on a medical image dataset for classification using deep reinforcement learning (RL) with quantum computing. Deep RL is the combination of DL (deep learning), generative adversarial network (GAN), and …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 2, 2024 · pp. 1–9 Read article
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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
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TensorFlow: Architecture, Applications, and Future Challenges
Abstract: TensorFlow, an open-source machine learning platform created by Google, has revolutionized how artificial intelligence (AI) systems are built and implemented. Designed to support scalable and flexible model training across CPUs, GPUs, and TPUs, TensorFlow enables researchers and developers to construct advanced deep learning models with efficiency and precision. This study provides an in-depth examination of TensorFlow's architecture, including its use of dataflow graphs and tensor-based computation. We explore its adaptability …
Published in Journal of Open Source Developments · Vol. 12, Issue 2, 2025 · pp. 41–50 Read article
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Mood Mate: A Solid-State Edge-AI System for Real-Time Facial Emotion Recognition
Abstract: Recent progress in solid-state electronics and embedded vision systems has enabled real-time emotion-aware applications at the edge. This paper presents MoodMate, a solid-state edge-AI framework for real-time facial emotion recognition using camera-based sensing and embedded processing. The proposed system integrates a solid-state image sensor with an AI- driven emotion classification pipeline optimized for low-latency and resource-constrained environments. Intelligent, emotion-aware apps can now be deployed right at the network edge thanks …
Published in International Journal of Solid State Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 24–30 Read article
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SpecForesight: A Predictive Analytics Pipeline for Laptop Price Forecasting
Abstract: This paper frames laptop pricing as a supervised predictive analytics problem, transforming product specifications into feature-rich signals to forecast price with calibrated regression models and operational guardrails against drift. A structured pipeline ingests tabular listings, performs data cleaning, and engineers domain-informed features (e.g., central processing unit (CPU) family and clocks, graphics processing unit (GPU) tiering, memory/storage density, display, and touch capabilities), followed by encoding and normalization to optimize model learnability. …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 61–71 Read article
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Development of Polymer Based SRAM Cell with Enhance Low Power Performance
Abstract: Low-power memory technologies are in high demand with the rapid growth of portable electronics and energy-efficient computing systems. Static random-access memory (SRAM) plays a crucial role in processors, cache memories, and system-on-chip applications due to their speed and reliability. Static Random Access Memory (SRAM) is typically implemented using complementary MOS (CMOS) technology, which integrates both PMOS (P-channel Metal–Oxide–Semiconductor) and NMOS (N-channel Metal–Oxide–Semiconductor) transistors. However, as technology scales to deep submicron …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1439–1448 Read article
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Lightweight Models for Per-PC Energy Consumption Forecasting: Comparative Study with ML and DL Approaches
Abstract: We have collected primary data from automated logging of parameters like CPU utilization, estimated power, active or idle state, user logging activity, and the type of day. Additionally, survey data showed user awareness, energy-saving behaviour, and PC usage patterns. The data is pre-processed and merged by applying processes such as data cleaning, normalization, and feature extraction, i.e., determining the peak active timings and downtime. Developed lightweight prediction models based on …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 17, Issue 1, 2026 Read article
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Design and Optimization of Domain-Specific Languages for High-Performance Computing Applications
Abstract: The accelerating demand for computational power in scientific, engineering, and data-intensive domains has driven High-Performance Computing (HPC) systems toward unprecedented levels of parallelism and architectural complexity. Contemporary HPC platforms integrate multicore CPUs, many-core GPUs, accelerators, and deep memory hierarchies, creating significant challenges for software development and performance optimization. Traditional general-purpose programming languages and parallel programming frameworks provide low-level control over hardware resources but require extensive manual tuning, resulting in poor …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article
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Comparison of Models of Machine Learning and Hyperparameter optimization methods on various datasets
Abstract: The most likely phase in achieving powerful and robust machine learning models is probably the hyperparameters tuning step. The traditional exhaustive methods of search (Grid Search and others) ensure that the search space is covered, but are computationally very inexpensive; random search is less expensive and can still miss good regions; and lastly, the modern model-based and population-based methods (Bayesian Optimization, Tree-structured Parzen Estimator (TPE), Genetic Algorithms) are thought to …
Published in Recent Trends in Programming languages · Vol. 13, Issue 1, 2026 Read article
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64 Bit ALU DESIGN USING VEDIC MATHEMATHICS
Abstract: High-speed arithmetic operations are crucial for better performance in contemporary digital systems. Particularly for high bit-width operations, conventional arithmetic logic units (ALUs) frequently experience increased latency and complexity. This work presents the design and implementation of a 64-bit Arithmetic Logic Unit (ALU) using notions from Vedic mathematics. The suggested design makes use of a Kogge-Stone Adder for effective addition and the Urdhva Tiryagbhyam sutra for quick multiplication. Among other mathematical …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 4, Issue 1, 2026 Read article
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Optimizing Multi-Cloud Infrastructure: Advanced Bash-Based Automation for Automated Security Patching and Health Monitoring in Hybrid Linux Environments
Abstract: The proliferation of multi-cloud and hybrid Linux environments has introduced significant operational complexity, particularly in maintaining security compliance and system reliability across diverse infrastructure silos. Traditional patch management approaches, relying on manual interventions or disparate vendor-specific tools, suffer from latency, configuration drift, and limited visibility. This article presents a novel, lightweight automation framework constructed entirely in advanced Bash scripting to address automated security patching and real-time health monitoring across hybrid …
Published in Journal of Advances in Shell Programming · Vol. 13, Issue 2, 2026 Read article
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Transfer Learning Based High-Precision Multi-Class Object Detection for Real-Time UAV Autonomous Landing via YOLOv8l in Unstructured Scenarios
Abstract: A significant challenge for autonomous drone landings in unstructured environments is that of reliably detecting and identifying objects in real-time to ensure safety and accuracy of the landing area. This paper presents a well-founded method for solving this problem using the YOLOv8l object detection framework to detect landing zones, obstacles and people in the relevant vicinity of the landing area. The dataset used for the training of the model contained …
Published in Journal of Aerospace Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
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Real-Time System Monitoring and Resource Optimization Using Shell Scripts in UNIX/Linux Environments
Abstract: Real-time system monitoring is a fundamental aspect of system administration in UNIX/Linux environments, as it ensures optimal system performance, reliability, and continuous availability of services. In modern computing infrastructures, systems are expected to operate efficiently under varying workloads, and any degradation in performance, such as CPU overload, memory exhaustion, disk bottlenecks, or network congestion, can significantly impact user experience and system stability. Regular observation and prompt action are crucial for …
Published in Journal of Advances in Shell Programming · Vol. 13, Issue 1, 2026 · pp. 08–15 Read article
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Efficient Data Backup and Recovery Automation Using Shell Scripts in UNIX Systems
Abstract: Data backup and recovery constitute essential components of system administration in UNIX-based environments, playing a critical role in maintaining data integrity, ensuring continuous availability, and providing resilience against various forms of data loss. In modern computing systems, data is a vital asset, and its loss—whether due to hardware failures, software corruption, human error, or cyber threats—can lead to significant operational disruptions and financial consequences. Therefore, implementing reliable and efficient backup …
Published in Journal of Advances in Shell Programming · Vol. 13, Issue 1, 2026 · pp. 48–54 Read article