Performance Metrics Research Articles

Mapping for stable bilateral teleoperation of manipulators considering time delays

Teleoperation of manipulators has allowed extending the human skills to several areas, where the commands generated by a human operator on the leader robot are followed by a remote robot. For this purpose, position-position tracking requires a mapping given the mismatch between robot workspaces. This paper compares a pair of functions for mapping the haptic device commands to handle a remote robot, considering non-identical workspaces and a classic P+d control scheme. As part of the theoretical analysis, a Lyapunov-Krasovskii-based stability analysis is developed in steps, allowing us for determining the boundedness of velocities and coordination errors. Subsequently, the performance of the teleoperation system is compared using two mapping functions. To achieve a fair comparison of results, a test protocol is developed, where different levels of haptic device damping, time delays, and workspace scales are used to evaluate the time to complete the task and the average coordination errors. Based on the results, the mapping function influences the calculation of force feedback, stability of the system, and performance metrics. During a pick-and-place task, a reduction in coordination error is observed when the linear function is chosen, while the time to complete the task is lower when a non-linear function is considered.

Polymeric piezoelectric accelerometers with high sensitivity, broad bandwidth, and low noise density for organic electronics and wearable microsystems

Piezoelectric accelerometers excel in vibration sensing. In the emerging trend of fully organic electronic microsystems, polymeric piezoelectric accelerometers can be used as vital front-end components to capture dynamic signals, such as vocal vibrations in wearable speaking assistants for those with speaking difficulties. However, high-performance polymeric piezoelectric accelerometers suitable for such applications are rare. Piezoelectric organic compounds such as PVDF have inferior properties to their inorganic counterparts such as PZT. Consequently, most existing polymeric piezoelectric accelerometers have very unbalanced performance metrics. They often sacrifice resonance frequency and bandwidth for a flat-band sensitivity comparable to those of PZT-based accelerometers, leading to increased noise density and limited application potentials. In this study, a new polymeric piezoelectric accelerometer design to overcome the material limitations of PVDF is introduced. This new design aims to simultaneously achieve high sensitivity, broad bandwidth, and low noise. Five samples were manufactured and characterized, demonstrating an average sensitivity of 29.45 pC/g within a ± 10 g input range, a 5% flat band of 160 Hz, and an in-band noise density of 1.4 µg/Hz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{{Hz}}$$\\end{document}. These results surpass those of many PZT-based piezoelectric accelerometers, showing the feasibility of achieving comprehensively high performance in polymeric piezoelectric accelerometers to increase their potential in novel applications such as organic microsystems.

A comprehensive analytical and computational assessment of soil water characteristics curves in Atlantic Canada: Application of a novel SelectKbestbased GEP model

For sustainable agriculture, irrigation is one of the most important management practices in a semi-arid climate. Determining the site-specific soil type-based irrigation water requirements requires the development of a soil water characteristics curve (SWCC). This study focuses on exploring the suitability of analytical SWCC models based on the observed data of SWCC developed using the pressure membrane apparatus. A novel SelectKbest gene expression programming (GEP) model is used to create the generalized machine learning SWCC model for Atlantic Canadian soils. The composite soil samples with representative soil textures of loamy sand, loam, and sandy loam are collected from the selected locations in Prince Edward Island and New Brunswick provinces of Canada. The suitability of models at three regions shows that at all sites, including PEI-1 (loam soil) 1, PEI-2 (loamy sand), and NB-3 (sandy loam) if SWCC data has four measured suction potential levels, then McBee & Bomb and Tani exponential models may be selected. Van Genuchten, double exponential, Ruso, Kosuki, and Gardner's four parameters models should be a good choice for five-section potential. Similarly, for six measured suction potential values, the Fredlund & Xing, Omuto, and Van Genuchten models having parameters should be selected for determining the optimum soil hydraulic parameters. The HydroMe program with Brooks and Corey, four, and five parameters Van Genuchten models performed well. The HydroMe program is calibrated and validated with standard RETC for selected regions of Atlantic Canada, which suggests that the HydroMe program is flexible in developing efficient agricultural water management algorithms. Various performance metrics like R2 and RMSE are employed to assess the robustness of the model. A generalized SWCC model (R2=0.976 and RMSE=0.04) is developed using a novel SelectKbest-based GEP modeling approach for Atlantic Canadian soils. These novel approaches can significantly improve site-specific agricultural water management and decision support systems.

Data analytics in the football industry: a survey investigating operational frameworks and practices in professional clubs and national federations from around the world.

The use of data and analytics in professional football organisations has grown steadily over the last decade. Nevertheless, how and whether these advances in sports analytics address the needs of professional football remain unexplored. Practitioners from national federations qualified for the FIFA World Cup Qatar 2022™ and professional football clubs from an international community of practitioners took part in a survey exploring the characteristics of their data analytics infrastructure, their role, and their value for elaborating player monitoring and positional data. Respondents from 29 national federations and 32 professional clubs completed the survey, with response rates of 90.6% and 77.1%, respectively. Summary information highlighted the underemployment of staff with expertise in applied data analytics across organisations. Perceptions regarding analytical capabilities and data governance framework were heterogenous, particularly in the case of national federations. Only a third of national federation respondents (~30%) perceived information on positional data from international sports data analytics providers to be sufficiently clear. The general resourcing limitations, the overall lack of expertise in data analytics methods, and the absence of operational taxonomies for reference performance metrics pose constraints to meaningful knowledge translations from raw data in professional football organisations.

Study on Lean Six Sigma methodology from the Indian context: an investigation of literature

Purpose India liberalized its economy in 1991, which resulted in intense global competition, quality-conscious and demanding customers. Additionally, significant technological advancements lead to enhancements in products and processes. These forced Indian organizations to adopt innovative business strategies in the past 30 years. Meanwhile, the Lean Six Sigma methodology has significantly grown with vast applicability during the past 30 years. Thus, the purpose of this study is to develop the learning on Lean Six Sigma methodology in the Indian context through investigation of literature. Design/methodology/approach A three-stage systematic literature review approach was adopted to investigate the literature during the present study. In total, 187 articles published in 62 journals/conference proceedings from 2005 to 2022 (18 years) were shortlisted. The first part of the article summarizes the significant milestones towards the quality journey in the Indian context, along with the evolution of the Lean Six Sigma methodology. The second part examines the shortlisted papers on Lean Six Sigma frameworks, their applicability in industrial sectors, performance metrics, outcomes realized, publication trends, authorship patterns and leading researchers from the Indian perspective. Findings Lean Six Sigma has emerged as a highly acclaimed and structured business improvement strategy worldwide. The Indian economy has seen remarkable growth in the past decade and is one of the fastest-growing economies in the 21st century. Lean Six Sigma implementation in India has significantly increased from 2014 onward. The study revealed that researchers have proposed several different frameworks for Lean Six Sigma implementation, the majority of which are conceptual. Furthermore, the balanced applicability of Lean Six Sigma in manufacturing and service sectors was observed with the highest implementation in the health-care sector. Additionally, the widely adopted tools, techniques along with performance metrics exploring case studies were reported along with a summary of eminent and leading researchers in the Indian context. Research limitations/implications This study is confined to reviewed papers as per the research criteria with a significant focus on the Indian context and might have missed some papers due to the adopted papers selection strategy. Originality/value The present study is one of the initial attempts to investigate the literature published on Lean Six Sigma in the Indian context, including perspective on the Indian quality movement. Therefore, the present study will provide an understanding of Lean Six Sigma methodology in the Indian context to graduating students in engineering and management and entry-level executives. The analysis and findings on Lean Six Sigma frameworks, research approach, publications details, etc., will be helpful to potential research scholars and academia. Additionally, analysis of case studies on Lean Six Sigma implementation by Indian industries will assist the managers and professionals in decision making.

Monetary Policy, Digitalisation and Banking Industry Performance in Nigeria

In the evolving financial landscape of Nigeria, the interplay between monetary policy and digitalization significantly influences the performance metrics of banks. This study explores the multifaceted impact of these two critical factors on the Nigerian banking sector. Monetary policy, orchestrated by the Central Bank of Nigeria, regulates the economy's money supply, affecting banks' lending behaviors and liquidity. Digitalization, on the other hand, offers a transformative potential for banks to enhance operational efficiency, customer experience, and service delivery through technological advancements. The study examines how money supply, lending rate and digitalization (proxied by mobile cell subscription) affect banks’ financial performance, focusing on bank credit and liquid assets, while controlling for inflation and exchange rate variables. It also examines the interaction between digitalization and monetary policy variables on bank performance. Employing an interactive multiple regression model, the study analyzes time series data from 1996 to 2022 sourced from the World Bank. Findings indicate that while stringent monetary policies can constrain bank performance by tightening credit and reducing liquidity, digitalization provides an opportunity for banks to counteract these effects through enhanced efficiency. However, the successful integration of digital technologies is contingent upon adequate investment, regulatory support, and strategic alignment with the banks’ core objectives. The study concludes that Nigerian banks must adeptly manage monetary policy fluctuations and leverage digitalization to sustain and enhance performance. These insights could guide policy formulation and strategic decision-making within the banking industry.

Deep Reinforcement Learning for Ecological and Distributed Urban Traffic Signal Control with Multi-Agent Equilibrium Decision Making

Multi-Agent Reinforcement Learning (MARL) has shown strong advantages in urban multi-intersection traffic signal control, but it also suffers from the problems of non-smooth environment and inter-agent coordination. However, most of the existing research on MARL traffic signal control has focused on designing efficient communication to solve the environment non-smoothness problem, while neglecting the coordination between agents. In order to coordinate among agents, this paper combines MARL and the regional mixed-strategy Nash equilibrium to construct a Deep Convolutional Nash Policy Gradient Traffic Signal Control (DCNPG-TSC) model, which enables agents to perceive the traffic environment in a wider range and achieves effective agent communication and collaboration. Additionally, a Multi-Agent Distributional Nash Policy Gradient (MADNPG) algorithm is proposed in this model, which is the first time the mixed-strategy Nash equilibrium is used for the improvement in the Multi-Agent Deep Deterministic Policy Gradient algorithm traffic signal control strategy to provide the optimal signal phase for each intersection. In addition, the eco-mobility concept is integrated into MARL traffic signal control to reduce pollutant emissions at intersections. Finally, simulation results in synthetic and real-world traffic road networks show that DCNPG-TSC outperforms other state-of-the-art MARL traffic signal control methods in almost all performance metrics, because it can aggregate the information of neighboring agents and optimize the agent’s decisions through gaming to find an optimal joint equilibrium strategy for the traffic road network.

An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle’s onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle’s onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems.

Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building’s energy consumption.

Animal welfare science, performance metrics, and proxy failure.

In their target article, John et al. make a convincing case that there is a unified phenomenon behind the common finding that measures become worse targets over time. Here, we will apply their framework to the domain of animal welfare science and present a pragmatic solution to reduce its impact that might also be applicable in other domains.

Fuzzy based trusted malicious unmanned aerial vehicle detection using in flying ad-hoc network

The communication security of unmanned aerial vehicles (UAVs) or drones based on flying ad hoc networks (FANETs) can be enhanced and maintained by assessing vulnerabilities, threats, and attacks. Thus, trust, high mobility, and complex communication interfaces are crucial for effective coordination and segregation of malicious drones from the genuine. A new efficient honesty-based detection scheme for malicious drones has been proposed to distinguish between intentional and unintentional misbehavior of the UAVs. The proposed scheme configures a drone system for packet transmission within the FANET, specifying buffer size and packet size variables to control data flow and prevent congestion. It then computes energy used for packet reception and transmission, optimizes energy consumption, and evaluates the drone’s mobility through the link stability index (LSI) and honesty UAV. The honesty UAV parameter categorizes UAVs into unintentional malicious and intentional malicious categories. Fuzzy logic helps identify intentional and unintentional misbehaving drones and improves performance in the network. It continuously generates the malicious UAV detection mechanism by assessing the parameters i.e., energy, pattern of movements, and the honesty score about the UAVs as a whole. Thereby a dynamic rating system to adeptly identify and differentiate the cooperative and non-cooperative nature of drones from the network is assimilated in the paper. Simulation results show the proposed scheme approximately aligns with the actual cases for varying numbers of malicious drones. Finally, the observation of the simulation result reflects substantial enhancement in performance metrics, with a significant cutback in end to end delay and packet delivery ratio by 10–30% and 20–50% respectively, contrasting with the existing techniques.

Performance analysis of the FSO communication system with random jamming over a composite Málaga turbulence fading channel

The performance analysis of a free space optical (FSO) communication system in the presence of random jamming is presented over a Málaga (M) distributed channel model with pointing errors and atmospheric attenuation. Firstly, the probability density function expressions of the transmission channel, signal-to-jamming ratio, and signal-to-noise ratio are derived. Then, considering the probability of the jammer and Gaussian white noise, the closed-form expressions for the ergodic channel capacity, outage probability, and average bit error rate are derived. Moreover, asymptotic expressions for the aforementioned performance metrics are also derived to ascertain the diversity gain of the system. Extensive Monte Carlo simulations are performed to demonstrate the credibility of this theoretical analysis. Results indicate that the adverse impact of random jamming is higher than that of Gaussian noise for the FSO communication system. Besides, this observation highlights the pulsating nature of the jamming effect, showcasing that within high signal-to-jamming ratio regions, a low probability jammer exerts the most significant impact on the FSO system.

Preclinical side effect prediction through pathway engineering of protein interaction network models.

Modeling tools aim to predict potential drug side effects, although they suffer from imperfect performance. Specifically, protein-protein interaction models predict drug effects from proteins surrounding drug targets, but they tend to overpredict drug phenotypes and require well-defined pathway phenotypes. In this study, we used PathFX, a protein-protein interaction tool, to predict side effects for active ingredient-side effect pairs extracted from drug labels. We observed limited performance and defined new pathway phenotypes using pathway engineering strategies. We defined new pathway phenotypes using a network-based and gene expression-based approach. Overall, we discovered a trade-off between sensitivity and specificity values and demonstrated a way to limit overprediction for side effects with sufficient true positive examples. We compared our predictions to animal models and demonstrated similar performance metrics, suggesting that protein-protein interaction models do not need perfect evaluation metrics to be useful. Pathway engineering, through the inclusion of true positive examples and omics measurements, emerges as a promising approach to enhance the utility of protein interaction network models for drug effect prediction.

Improving Throughput and Sum Secrecy Rate in Wireless Networks Against Illegal Intelligent Reflecting Surface (IIRS) Based Jamming Attacks

Illegal intelligent reflecting surfaces (I-IRSs), i.e., the illegal deployment and utilization of IRSs, impose serious harmful impacts on wireless networks. The existing I-IRS-based illegal jammer (IJ) requires channel state information (CSI) or extra power or both, and therefore, the I-IRS-based IJ seems to be difficult to implement in practical wireless networks. Due to continued sharing of resources, wireless networks often come under security attacks, most common of which are eavesdropping attacks. In the case of eavesdropping attacks, deliberately designed random eavesdropping data is added to the channel. These eavesdropping along with noise result in packet losses and low throughput, degrading the overall performance of the cognitive network. In this work, a security aware jamming and eavesdropping rejection mechanism is proposed which detects suspicious signals in the channel frequency response and employs discrete equalization to recover transmitted data. The proposed approach uses a chaos based FFH protocol for possible adversarial eavesdropping attacks. The error rate and average sum rate show that the proposed system outperforms the exiting systems in terms of the performance metrics. Keywords:- Illegal Intelligent Reflecting Surface (IIRS), Jamming Attack, Packet Intercept. Error Rate, Sum Rate

Web analytics and performance metrics: assessing the effectiveness of digital strategies

Web analytics involves applying data analysis to the information collected by a website to understand traffic patterns and user behavior. The evaluation of web analytics in the tourism sector in Cuenca, Ecuador, sought to improve digital strategies through a mixed qualitative-quantitative approach. A correlation was observed between age and the frequency of consulting statistics on tourism websites, with greater participation of young people. In addition, a significant interest in receiving personalized recommendations was highlighted, with about 40 % expressing definite interest. The willingness to provide feedback and participate in the improvement of online services was widespread. The Internet was positioned as the main source of tourism information, with an emphasis on the use of social networks. These findings highlight the importance of digital marketing strategies and optimization for mobile devices in the tourism industry.

A Novel Framework on Book-Recommendation System

With so many books available these days, effective book recommendation engines are crucial to pointing readers in the direction of choices that suit their interests. This paper introduces a prediction algorithm that uses user ratings to improve book recommendations. The program attempts to precisely forecast and rank the top 50 books for specific users by examining user-provided book ratings. The research methodology utilized in this study generates tailored recommendations by combining machine learning algorithms with collaborative filtering techniques. In order to provide predictions, collaborative filtering looks for trends in user behavior and preferences and compares users and books. To be more precise, the model finds books that people with similar preferences have liked by using similarity measures and user-item matrices. The effectiveness of the predictive model is evaluated using a sizable dataset of user-rated novels. The model is guaranteed to be resilient and adaptable to a large range of literary interests due to the dataset's broad scope in terms of genres, authors, and publishing years. Recall, accuracy, and precision are among the performance metrics used to assess how well the model can recommend books to readers based on their interests. The predictive model's ability to produce individualized book recommendations based on user ratings is demonstrated by the results. The model's prediction of the top 50 books is highly relevant and aligned with users' likes, which improves readers' browsing and selecting experience. The concept also exhibits scalability and flexibility, making it possible to accommodate growing book catalogs and changing customer preferences. All things considered, this work advances book recommendation systems by introducing a predictive model that uses user ratings to produce tailored recommendations. The approach improves reading enjoyment and encourages readers to become more deeply involved with books by making it easier for them to find interesting and engaging literary content..

Crystallization Control Assists Annealing‐Free Fabrication of Printable Mesoscopic Perovskite Solar Cells with Power Conversion Efficiency over 17%

The one‐step drop‐casting for preparing perovskite films in three‐layer mesoporous for printable mesoscopic perovskite solar cells always results in insufficient filling of mesopores. A binary solvent based on 2‐methoxyethanol (2‐ME) and 1‐methyl‐2‐pyrrolidone (NMP) offers a method suitable for annealing‐free to prepare dense perovskite films in mesopores. The crystallization process of perovskite films in mesopores can be adjusted by the content of NMP in the solvent. The introduction of NMP enhances the density of the perovskite film based on the binary solvent system, optimizing the contact between the perovskite film and the mesoporous scaffold. When the ratio of 2‐ME to NMP is 9:1, the optimal device achieves impressive performance metrics, including a power conversion efficiency (PCE) of 17.28%, an open‐circuit voltage of 0.98 V, a short‐circuit current of 23.80 mA cm−2, and a fill factor of 74.08%. This represents the highest PCE achieved to date in the annealing‐free preparation of perovskite films for printable mesoscopic perovskite solar cells. Furthermore, devices based on this binary solvent system exhibit remarkable stability, with almost no loss in efficiency even after 120 days of storage in an air environment without encapsulation.

AN EFFICIENT VLSI IMPLEMENTATION OF EDGE DETECTION OF IMAGES

Edge detection is a fundamental process in image processing with significant implications across multiple domains. Within the realm of object detection, the accurate delineation of image perimeters forms the bedrock for subsequent analysis. In this study, we present an optimized Laplacian Edge Detection algorithm tailored for efficient implementation in hardware.Leveraging approximation methodologies and strategic pipelining, we streamline complex operations, thereby reducing latency and resource consumption. Our implementation, realized through the Verilog Hardware Description Language (VHDL), stands as a testament to the efficacy of our approach. Through rigorous optimization and meticulous comparison with conventional Sobel edge detection, we demonstrate superior performance metrics, including enhanced Area utilization, reduced Time Delay, and improved Power Efficiency. This work underscores the importance of methodological refinement and technological innovation in advancing edge detection techniques for practical applications. Keywords: Laplacian, Sobel, Kernel, Pixel range, Area, Power

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.

FireXplainNet: Optimizing Convolution Block Architecture for Enhanced Wildfire Detection and Interpretability

The early detection of wildfires is a crucial challenge in environmental monitoring, pivotal for effective disaster management and ecological conservation. Traditional detection methods often fail to detect fires accurately and in a timely manner, resulting in significant adverse consequences. This paper presents FireXplainNet, a Convolutional Neural Network (CNN) base model, designed specifically to address these limitations through enhanced efficiency and precision in wildfire detection. We optimized data input via specialized preprocessing techniques, significantly improving detection accuracy on both the Wildfire Image and FLAME datasets. A distinctive feature of our approach is the integration of Local Interpretable Model-agnostic Explanations (LIME), which facilitates a deeper understanding of and trust in the model’s predictive capabilities. Additionally, we have delved into optimizing pretrained models through transfer learning, enriching our analysis and offering insights into the comparative effectiveness of FireXplainNet. The model achieved an accuracy of 87.32% on the FLAME dataset and 98.70% on the Wildfire Image dataset, with inference times of 0.221 and 0.168 milliseconds, respectively. These performance metrics are critical for the application of real-time fire detection systems, underscoring the potential of FireXplainNet in environmental monitoring and disaster management strategies.