Execution Performance Research Articles

An Optimization Method for Manned/Unmanned Aerial Vehicle Collaborative Operation System Architecture Based on PGQNSGA-II

The architecture of the Manned/Unmanned Aerial Vehicle Collaborative Operation System (MAV/UAV COS) is crucial for improving combat effectiveness and resource utilization efficiency. Optimizing this architecture involves managing complex, interdependent components, which presents a constrained multi-objective optimization challenge. Initially, the elements of the MAV/UAV COS architecture were analyzed and formally expressed, transforming the architecture optimization problem into a multi-objective optimization problem, with the objectives of maximizing total system effectiveness, command-and-control performance, and system execution performance. Constraints were formulated based on mission and payload information. Subsequently, a Quantum Non-Dominated Sorting Genetic Algorithm based on Preference Guidance (PGQNSGA-II) was developed, incorporating an adaptive quantum gate mechanism based on preference information to enhance chromosome updating, ensuring that the probability amplitude of quantum bits aligns more closely with the optimal chromosome. The simulation results demonstrate that the proposed PGQNSGA-II algorithm significantly enhances the global search capability and efficiency compared to traditional quantum genetic algorithms, making it well-suited for optimizing MAV/UAV COS architectures.

Optimizing residential flexibility for sustainable energy management in distribution networks

In search of a sustainable and green economy, many initiatives have been undertaken to promote clean energy and enhance local flexibility. Residential flexibility, achieved through home appliances capable of adjusting their consumption profiles, offers a feasible solution for operators to address challenges such as congestion and balancing in distribution systems. This paper considered an improved approach for aggregators to provide flexibility in distribution systems. By leveraging load flexibility resources, the model facilitates the rescheduling of real-time and shifting appliances to meet the demands of Balance Responsible Parties (BRPs) or Distribution System Operators (DSOs). This study uses a number of approaches to solve the recommended model effectively despite the problem's inherent complexity. An extensive test case with twenty residential houses equipped with seven types of appliances each is run in order to confirm and compare the optimization algorithms' performance. The results show that by rescheduling home appliance loads across 24 hours, the aggregator may effectively accommodate flexibility requests from DSOs/BRPs while optimizing the expenses associated with user compensation. To further improve the optimization process, this study uses a new Reinforced Learning Quantum Inspired Grey Wolf Optimization (RLQIGWO). Through the integration of reinforcement learning and quantum mechanics principles into the original grey wolf optimizer, RLQIGWO achieves better performance in load balancing, resource utilization, and execution of tasks. The findings demonstrate that the proposed RLQIGWO improves the efficacy and competence of flexibility options in distribution networks, paving the way to a more adaptable and strong energy management strategy.

Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale.

We utilized remote sensing and ground cover data to predict soil organic carbon (SOC) content across a vast geographic region. Employing a combination of machine learning and deep learning techniques, we developed a novel data fusion approach that integrated Digital Elevation Model (DEM) data, MODIS satellite imagery, WOSIS soil profile data, and CHELSA environmental data. This combined dataset, named GeoBlendMDWC, was specifically designed for SOC prediction. The primary aim of this research is to develop and evaluate a novel optimization algorithm for accurate SOC prediction by leveraging multi-source environmental data. Specifically, this study aims to (1) create an integrated dataset combining remote sensing and ground data for comprehensive SOC analysis, (2) develop a new optimization technique that enhances both machine learning and deep learning model performance, and (3) evaluate the algorithm's efficiency and accuracy against established optimization methods like Jaya and GridSearchCV. This study focused on India, Australia, and South Africa, countries known for their significant agricultural activities. We introduced a novel optimization technique for both machine learning and deep neural networks, comparing its performance to established methods like the Jaya optimization technique and GridSearchCV. The models evaluated included XGBoost Regression, LightGBM, Gradient Boosting Regression (GBR), Random Forest Regression, Decision Tree Regression, and a Multilayer Perceptron (MLP) model. Our research demonstrated that the proposed optimization algorithm consistently outperformed existing methods in terms of execution time and performance. It achieved results comparable to GridSearchCV, reaching an R2 of 90.16, which was a significant improvement over the base XGBoost model's R2 of 79.08. In deep learning optimization, it significantly outperformed the Jaya algorithm, achieving an R2 of 61.34 compared to Jaya's 30.04. Moreover, it was 20-30 times faster than GridSearchCV. Given its speed and accuracy, this algorithm can be applied to real-time data processing in remote sensing satellites. This advanced methodology will greatly benefit the agriculture and farming sectors by providing precise SOC predictions.

OptiFeat: enhancing feature selection, a hybrid approach combining subject matter expertise and recursive feature elimination method

Optimizing the performance of Java Virtual Machines (JVMs) (Sahin et al. in Proc IEEE Int Congr Big Data BigData Congr 410–417, 2016) is crucial for achieving efficient execution of Java applications. Feature selection plays a pivotal role in identifying the most relevant parameters for fine-tuning JVMs, thereby enhancing their overall efficiency. This paper presents a novel hybrid approach that integrates both subject matter expertise and Recursive Feature Elimination (RFE) (Yin et al. in J Big Data 10(1):15, 2023) model to refine feature selection for JVM fine-tuning using machine learning techniques. Traditional feature selection methods often lack the ability to incorporate domain-specific knowledge, resulting in suboptimal selections (Khaire and Dhanalakshmi in J King Saud Univ Comput Inf Sci 34(4):1060–1073, 2022). In contrast, the hybrid approach leverages the expertise of JVM administrators or developers to guide the feature selection process. By integrating domain knowledge into the feature selection pipeline, ensure the inclusion of crucial JVM parameters that may not be captured by automated techniques alone. Furthermore, employed the RFE model, a powerful recursive feature elimination algorithm, to iteratively identify and eliminate irrelevant features from the initial feature set. This iterative process enhances the efficiency of feature selection by systematically pruning less influential parameters, thereby improving the overall performance of the JVM. To validate the effectiveness of the hybrid approach, conducted experiments using real-world JVM datasets and compare the performance of the method against existing feature selection techniques. The results demonstrate that the approach not only achieves superior performance in terms of JVM fine-tuning but also provides insights into the significance of domain expertise in optimizing JVM performance (Menéndez and Bartlett in http://arxiv.org/abs/2310.16510, 2023). It contributes to the field of JVM optimization by proposing a novel hybrid approach that combines subject matter expertise with machine learning-based feature selection techniques. By leveraging both domain knowledge and automated algorithms, the approach offers a comprehensive solution for enhancing feature selection in JVM fine-tuning, ultimately leading to improved performance and efficiency in Java application execution.

Research Progress on Pole-Climbing Robots: A Review

Background: Pole-climbing robots are a type of application robot commonly used for quality inspection, cleaning, and maintenance of utility poles, high voltage lines, bridge cables, transmission pipelines, and other important facilities with a certain height. The working conditions are often harsh and dangerous, making manual participation unsafe. The pole-climbing robot can provide a platform for maintenance and cleaning work. Remote control operation of the pole-climbing robot is achieved through communication and control technology. It has been created to improve efficiency and reduce personnel accidents greatly. At the same time, the continuous development of science and technology has led to the expansion of robot design concepts. It has also brought about great changes and growth in the development of pole-climbing robots. Objective: The purpose of this paper is to report the latest progress in the research of pole-climbing robots from bionic and non-bionic perspectives and to provide a research reference for researchers in this field. Method: By analyzing academic theses and published patents, this paper presents a new classification of pole-climbing robots from the perspective of bionic and non-bionic. By summarizing the literature, the structural characteristics of various pole-climbing robots and their differences and applications are summarized. Results: The performance of the pole-climbing robots is analyzed from the viewpoint of structural characteristics and action execution methods. In turn, the characteristics of various types of pole-climbing robots are summarized. Finally, based on the above discussion, the future problems and development directions of pole-climbing robots are predicted. Conclusion: The pole-climbing robots can be divided into two categories, bionic and non-bionic, based on the analysis of design features. Both bionic and non-bionic pole-climbing robots have good development and applications in their respective directions. Also, bionic and non-bionic pole-climbing robots have different detailed classifications based on material, structure, and other perspectives. They have different advantages and disadvantages in terms of the performance of pole-climbing action execution. It provides a research reference for future researchers in this field.

Codepickle: Portable Python Grid Computing

We present Codepickle, a groundbreaking solution designed to overcome Python version constraints and enhance portability, especially within distributed Python grids and volunteer computing environments. Unlike existing serialization libraries such as Cloudpickle and Dill, which rely on Python bytecode and are prone to version conflicts, Codepickle offers a robust alternative. Our methodology includes innovative adjustments for function serialization and shared variable management. Experimental results reveal challenges like code sourcing and nonlocal variable handling. Performance benchmarks highlight Codepickle's significant advantages over Cloudpickle, including better portability and reduced message sizes. Notably, Codepickle achieves message sizes that are 84% of those produced by Cloudpickle especially for small code segments, with comparable execution performance. Proposed enhancements target critical issues such as lambda functions and cross-version compatibility. This comprehensive study not only demonstrates Codepickle's transformative potential but also underscores the ongoing quest for advanced serialization techniques in Python's distributed computing landscape.

Continuing Safety Education and Workplace Efficacy of Student Workers

This study aims to evaluate students' awareness of safety education and its impact on work effectiveness. By analyzing background variables such as students' gender, age, grade, work experience, student status, and college affiliation, this study explored the relationship between safety education and work effectiveness. The results showed that students' awareness of safety education directly affects their task execution and situational performance on campus and in the workplace. In addition, the study showed that strengthening the popularization and effective cooperation of safety education on campus can help improve students' work effectiveness and campus safety management. This study recommends further improving the quality of safety education by strengthening training, promoting safety knowledge, and improving safety facilities, and strengthening the cultivation of safety awareness during students' internships and employment.

Transforming academic assessment: The metaverse‐backed Web 3 secure exam system

AbstractMetaverse—a three‐dimensional computational environment—combines physical and virtual reality to enable social relationships and immersive experiences by mimicking real‐world scenarios. Metaverse is considered the third wave of the internet revolution (exploiting Web 3.0), leveraging upcoming technologies such as extended reality and artificial intelligence shaping a new era of human–‐machine interactions. In recent years, increased research and development on educational technologies (EduTech) based on blockchain technology has seen substantial growth of metaverse‐based solutions within the higher education context. This research aims to synergize blockchain technology and metaverse environments to conduct online exams (metaExam) in a trustworthy, reliable, and secure way. The synergy between blockchain and the metaverse brings various benefits, such as improved security, cost effectiveness, and increased efficiency in the online examination process. One of the central features of the proposed solution metaExam is to leverage cryptographic protocols via blockchain to control data access, making verification faster and protecting against misuse. Exam scores and grades are stored on a blockchain ledger using a digital signature method to enhance security. We validated the proposed solution by testing a prototype on the Ethereum platform using the Sepolia Testnet network using Microsoft Windows environment. Evaluation results indicate (i) query response time (10–50 ms), (ii) and query execution performance (CPU utilization between 1%–5%) offering computationally feasible solution. This research contributes by integrating blockchain and metaverse technologies to offer a solution metaExam that can offer improved security and immersive user experience for exam management. The proposed solution and its validation can provide insights into transforming online exams, offering a fresh perspective on addressing concerns about exam grade authenticity and verifying academic credentials in EduTech.

PenGym: Realistic training environment for reinforcement learning pentesting agents

Penetration testing, or pentesting, refers to assessing network system security by trying to identify and exploit any existing vulnerabilities. Reinforcement Learning (RL) has recently become an effective method for creating autonomous pentesting agents. However, RL agents are typically trained in a simulated network environment. This can be challenging when deploying them in a real network infrastructure due to the lack of realism of the simulation-trained agents.In this paper, we present PenGym, a framework for training pentesting RL agents in realistic network environments. The most significant features of PenGym are its support for real pentesting actions, full automation of the network environment creation, and good execution performance. The results of our experiments demonstrated the advantages and effectiveness of using PenGym as a realistic training environment in comparison with a simulation approach (NASim). For the largest scenario, agents trained in the original NASim environment behaved poorly when tested in a real environment, having a high failure rate. In contrast, agents trained in PenGym successfully reached the pentesting goal in all our trials. Even after fixing logical modeling issues in simulation to create the revised version NASim(rev.), experiment results with the largest scenario indicated that agents trained in PenGym slightly outperformed, and were more stable, than those trained in NASim(rev.). Thus, the average number of steps required to reach the pentesting goal was 1.4 to 8 steps better for PenGym. Consequently, PenGym provides a reliable and realistic training environment for pentesting RL agents, eliminating the need to model agent actions via simulation.

CIPerf: a benchmark for continuous integration services performance and cost analysis

Continuous Integration is a crucial practice in modern software development, enabling teams to automate the process of building, testing, and merging code increments to ensure continuous delivery of high-quality software. Despite its growing adoption, the cost and performance of Continuous Integration services often go unexamined in sufficient detail. This paper presents CIPerf, a comprehensive benchmark designed to analyze both the performance and cost of cloud-based and self-hosted Continuous Integration services. The study centers on a comparison between two specific services: Bitbucket Pipelines, a cloud-based offering by Atlassian, and Hetzner, a self-hosted solution. By focusing on these platforms, the research aims to provide practical insights into the real-world costs and execution performance of Continuous Integration services. To achieve this, CIPerf conducted automated tests on an hourly basis over a two-month period, measuring critical timeframes such as resource provisioning, environment setup, and the actual test execution times. The results showed significant differences in both the cost efficiency and the consistency of performance between the two services. For instance, Bitbucket Pipelines, while convenient in its cloud-based offering, demonstrated higher variability in provisioning times compared to the stable, predictable performance of Hetzner’s self-hosted environment. The analysis also explored how these performance metrics influence key software development metrics, including deployment frequency and developer productivity. CIPerf provides a clear methodology for developers and organizations to objectively assess their Continuous Integration service options, ultimately helping them optimize their workflows. Moreover, this benchmark can serve as an ongoing tool to monitor service performance over time, identifying potential degradations or improvements in service quality, thus offering longterm value for teams that rely on Continuous Integration for their development processes.

String-level compact modeling of erase operations in the body-floated vertical channel of 3D charge trapping flash memory

In this study, we examined the use of string-level compact modeling as an effective framework for circuit simulation focusing on erase operation in 3D charge trapping flash (CTF) memory devices. We analyzed the behaviors of the accumulated hole from p-type bulk (p-well) and the corresponding difference of channel electrostatic potential in the CTF cell string, which is attributed to the variation in hole barrier height in the channel of the ground select line (GSL) transistor during erase operation. We derived a formula for the hole current delivering positive potential from the p-well to the channel region and established a modeling procedure. Technology computer-aided design (TCAD) simulation results were used to extract model parameters and analyze channel electrostatic potential during the erase operation. Additionally, experimental data for erase speed were verified using simulation program with integrated circuit emphasis (SPICE) results. Because the erase efficiency is strongly related to hole behaviors based on the conditions of the GSL transistor, the proposed compact modeling is an effective tool for circuit designers and system architects to achieve better performance in erase execution and optimizing the design of 3D CTF memory devices.

Inter-agency coordination during public events in City Mall Kalibo, Aklan

This study delves into the dynamics of inter-agency coordination during public events within CityMall Kalibo, focusing on young adults aged 18-34, predominantly female, and encompassing various educational and occupational backgrounds among event management respondents. Employing a descriptive research design and quantitative approach, the study investigates the perceptions of respondents regarding factors influencing inter-agency coordination, including verbal and non-verbal communication, coordination, collaboration, cooperation, and resource allocation. Findings reveal that effective inter-agency coordination leads to positive outcomes such as increased profitability, work efficiency, and a therapeutic work environment during public events. Notably, there were no significant differences in the factors affecting coordination among respondents. The study underscores the pivotal role of inter-agency coordination in public event management, emphasizing its impact on event execution and organizational performance. The shared perception of its significance across different respondent groups reinforces its importance in facilitating successful event execution. This research, conducted in Poblacion, Kalibo, Aklan, sheds light on the intricate dynamics of inter-agency coordination, providing valuable insights for event management practitioners and policymakers alike.

Software patterns and data structures for the runtime coordination of robots, with a focus on real-time execution performance.

This paper introduces software patterns (registration, acquire-release, and cache awareness) and data structures (Petri net, finite state machine, and protocol flag array) to support the coordinated execution of software activities (also called "components" or "agents"). Moreover, it presents and tests an implementation for Petri nets that supports real-time execution in shared memory for deployment inside one individual robot and separates event firing and handling, enabling distributed deployment between multiple robots. Experimental validation of the introduced patterns and data structures is performed within the context of activities for task execution, control and perception, and decision making for an application on coordinated navigation.

A Scalable Multi-FPGA Platform for Hybrid Intelligent Optimization Algorithms

The Intelligent Optimization Algorithm (IOA) is widely focused due to its ability to search for approximate solutions to the NP-Hard problem. To enhance applicability to practical scenarios and leverage advantages from diverse intelligent optimization algorithms, the Hybrid Intelligent Optimization Algorithm (H-IOA) is employed. However, IOA typically requires numerous iterations and substantial computing resources, resulting in poor execution efficiency. In complex optimization scenarios, IOA traditionally relies on population partitioning and periodic communication, highlighting the feasibility and necessity of parallelization. To address the challenges above, this paper proposes a general hardware design approach for H-IOA based on multi-FPGA. The approach includes the hardware architecture of multi-FPGA, inter-board communication protocols, population storage strategies, complex hardware functions, and parallelization methodologies, which enhance the computing capabilities of H-IOA. To validate the proposed approach, a case study is conducted, in which an H-IOA integrating genetic algorithm (GA), a simulated annealing algorithm (SA), and a pigeon-inspired optimization algorithm (PIO) are implemented on a multi-FPGA platform. Specifically, the flexible job-shop scheduling problem (FJSP) is employed to verify the potential in industrial applications. Two Xilinx XC6SLX16 FPGA chips are used for hardware implementation, encoded in VHDL, and an AMD Ryzen 7 5800U was used for the software implementation of Python programs (version 3.12.4). The results indicate that hardware implementation is 13.4 times faster than software, which illustrates that the proposed approach effectively improves the execution performance of H-IOA.

Multi-agent reinforcement learning for integrated manufacturing system-process control

The increasing complexity, adaptability, and interconnections inherent in modern manufacturing systems have spurred a demand for integrated methodologies to boost productivity, improve quality, and streamline operations across the entire system. This paper introduces a holistic system-process modeling and control approach, utilizing a Multi-Agent Reinforcement Learning (MARL) based integrated control scheme to optimize system yields. The key innovation of this work lies in integrating the theoretical development of manufacturing system-process property understanding with enhanced MARL-based control strategies, thereby improving system dynamics comprehension. This, in turn, enhances informed decision-making and contributes to overall efficiency improvements. In addition, we present two innovative MARL algorithms: the credit-assigned multi-agent actor-attention-critic (C-MAAC) and the physics-guided multi-agent actor-attention-critic (P-MAAC), each designed to capture the individual contributions of agents within the system. C-MAAC extracts global information via parallel-trained attention blocks, whereas P-MAAC embeds system dynamics through permanent production loss (PPL) attribution. Numerical experiments underscore the efficacy of our MARL-based control scheme, particularly highlighting the superior training and execution performance of C-MAAC and P-MAAC. Notably, P-MAAC achieves rapid convergence and exhibits remarkable robustness against environmental variations, validating the proposed approach’s practical relevance and effectiveness.

Optimization Applications as Quantum Performance Benchmarks

Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm and Quantum Annealing can potentially demonstrate significant run-time performance benefits over current state-of-the-art solutions. Inspired by existing methods to characterize classical optimization algorithms, we analyze the solution quality obtained by solving Max-cut problems using gate-model quantum devices and a quantum annealing device. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications. The framework generates performance profiles through compelling visualizations that show performance progression as a function of time for various problem sizes and illustrates algorithm limitations uncovered by the benchmarking approach. As an illustration, we explore the factors that influence quantum computing system throughput, using results obtained through execution on various quantum simulators and quantum hardware systems.

Dynamic Bandwidth Allocation for Collaborative Multi-Robot Systems Based on Task Execution Measures

Multi-robot systems (MRSs) is a growing field of research that focuses on the collaboration of multiple robots to achieve a common global objective. Managing these systems poses several challenges, including coordination, task allocation, and communication. Among these challenges, a major area of focus is devising an effective communication scheme that ensures robots’ cooperation and adapts to varying conditions during task execution. In this paper, we develop a novel communication management framework tailored for MRSs, specifically addressing dynamic bandwidth distribution in networked teleoperated robotic systems. The algorithm is combined with semi-autonomous formation control based on the Artificial Potential Fields (APF) algorithm, which allows each individual robot to avoid local obstacles autonomously and tries to maintain a desired formation with its neighbors, while the operator is in charge of high-level control only. Common Dynamic Bandwidth Allocation (DBA) algorithms allocate bandwidth to different units based on network conditions and requirements. On the other hand, our proposed DBA scheme dynamically distributes the available bandwidth on communication streams based on factors related to task execution and system performance. In specific, bandwidth is allocated in a way that adapts to changes occurring in the system’s environment and its internal state, including the effect of the autonomous action taken by the path planner on the MRS and the performance of the controller of each individual robot. By addressing the limitations of existing approaches through shaping the communication behavior of the MRS based on performance measures, our proposed algorithm offers a promising solution for improving the performance and efficiency of MRSs. The proposed scheme is tested through simulations on a group of six unmanned aerial vehicles (UAVs) in the Robot Operating System (ROS)-Gazebo simulation environment. The obtained results show the scheme’s capability for enhancing the robotic system’s performance while significantly reducing bandwidth consumption. Experimental testing on two mobile robots further demonstrates the effectiveness of the proposed scheme.

Decoder-ROI based Versatile Video Coding for Multi-Object Tracking Vision Task

The video encoding standards High Efficiency Video Coding (HEVC) and, more recently, Versatile Video Coding (VVC) have introduced significant advancements in multimedia communication applications, such as video conferencing, broadcasting, and notably, E-learning. However, recent developments in artificial intelligence (AI) and big data have given rise to an urgent need for a specialized video encoding model designed specifically for image and video analysis applications using machine vision. In this paper, we propose a novel video encoding approach that effectively combines the ROI Coding algorithm and the VVC encoding model. The proposed method identifies regions of interest within video frames through fundamental and deep features. Based on this, we propose an adaptive compression method for each frame block, ensuring both the execution performance of machine learning applications and minimal data encoding requirements. To achieve new coding scheme without adding bitrate, New feature extraction approach are utilizing only decoded information (Decoder-ROI). The results demonstrate that the Decoder-ROI achieved significant compression rate improvement when compared to standard and relevant VCM schemes. Furthermore, ROI exploitation contributes to a 3.25\% reduction in encoding time compared to the baseline VVC encoding standard.

Intuitionistic fuzzy generalized eigenvalue proximal support vector machine

Generalized eigenvalue proximal support vector machine (GEPSVM) has attracted widespread attention due to its simple architecture, rapid execution, and commendable performance. GEPSVM gives equal significance to all samples, thereby diminishing its robustness and efficacy when confronted with real-world datasets containing noise and outliers. In order to reduce the impact of noises and outliers, we propose a novel intuitionistic fuzzy generalized eigenvalue proximal support vector machine (IF-GEPSVM). The proposed IF-GEPSVM assigns the intuitionistic fuzzy score to each training sample based on its location and surroundings in the high-dimensional feature space by using a kernel function. The solution of the IF-GEPSVM optimization problem is obtained by solving a generalized eigenvalue problem. Further, we propose an intuitionistic fuzzy improved generalized eigenvalue proximal support vector machine (IF-IGEPSVM) by solving standard eigenvalue decomposition resulting in simpler optimization problems with less computation cost which leads to an efficient intuitionistic fuzzy-based model. We conduct a comprehensive evaluation of the proposed IF-GEPSVM and IF-IGEPSVM models on UCI and KEEL benchmark datasets. Moreover, to evaluate the robustness of the proposed IF-GEPSVM and IF-IGEPSVM models, label noise is introduced into some UCI and KEEL datasets. The experimental findings showcase the superior generalization performance of the proposed IF-GEPSVM and IF-IGEPSVM models when compared to the existing baseline models, both with and without label noise. Our experimental results, supported by rigorous statistical analyses, confirm the superior generalization abilities of the proposed IF-GEPSVM and IF-IGEPSVM models over the baseline models. Furthermore, we implement the proposed IF-GEPSVM and IF-IGEPSVM models on the USPS recognition dataset, yielding promising results that underscore the models’ effectiveness in practical and real-world applications. The source code of the proposed IF-GEPSVM and IF-IGEPSVM models are available at https://github.com/mtanveer1/IF-GEPSVM.

Design of Approximate Tree Multiplier Using Approximate Compressor for Image Processing Applications

Approximate computing enables high performance and efficiency of execution by using the overhead on compute units of a processor to give high speed. The speed and system delay are inversely proportional, necessitating massive parallel processes with massive hardware and power dissipation. Energy and area-efficient systems can be realized by reducing system accuracy and dependability. Approximate computing techniques can be used to reduce the number of computing resources needed for a given task and improve the performance of a system. This can be accomplished by lowering a computational model's accuracy and decreasing complexity, reducing power consumption, latency, and area requirements. This study examines an electrical circuit that performs multiplication of two equal integers and it proposes the use of a carry-in adder with two approximation compressors to reduce the size, latency, and power consumption while maintaining the same level of accuracy as current designs. The suggested algorithms were implemented in Xilinx Vivado and their effectiveness was evaluated using numerical performance metrics, including error rate, power consumption, delay, and power delay product in output result. The compressors suggested are used to implement 8 × 8 and 16 × 16 Dadda multipliers that perform comparably to state-of-the-art approximation multipliers.