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Considering the extensive applications of turning and facing operations in mechanical engineering manufacturing, the energy consumption of machining equipment has emerged as a significant concern in the manufacturing industries, such as aerospace and automotive. This article focuses on establishing a predictive model and optimizing energy consumption for facing and turning operations on two types of steel, namely, AISI 1038 and AISI 4142, using two different cutting tools. The experiments were planned using Taguchi's L16 orthogonal array, and the coefficients of the predictive model were determined through a linear regression approach. Additionally, a genetic algorithm (GA) with two distinct selection techniques was employed to optimize the three main variables: cutting depth, cutting speed, and feed rate, all aimed at reducing energy consumption. The results of this study show that the selection method used in GA significantly affects convergence toward the optimal solution, while the choice of cutting tool has a considerable impact on energy usage. Moreover, the variation in the effect of cutting parameters on machining energy consumption is analyzed to determine which parameters contribute most to energy savings. The results underscore the importance for manufacturers of using advanced predictive and optimization tools, offering them a competitive edge by achieving economic, environmental, and performance-related benefits. [ABSTRACT FROM AUTHOR]

IoT devices generate a massive amount of sensitive data, which is transferred tremendously to the cloud for processing and decision-making. The most significant issues that need to be solved for IoT devices are improving energy efficiency and guaranteeing data security while several devices are connected. In this paper, for edge computing, a hybrid algorithm is proposed that uses compression and encryption in the same manner to improve efficiency in terms of energy and data security in IoT devices. The authenticated encryption with associated data (AEAD) ChaCha12-Poly1305 algorithm and improved SZ 2.1 compression are used in this hybrid architecture. While sending the data to the edge, confidentiality, integrity, and authentication are maintained. Several experiments were conducted considering the driver stress detection dataset (Bernstein DJ. Lecture Notes in Computer Science including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 4986:84–97 (2008); Lakshminarasimhan S et al. LNCS 6852(1):366–379 (2011)) and gas-sensor dataset (Ibarria L et al. Comput Graph Forum 22(3):343–348 (2003)) for monitoring the home activity to validate this approach. The performance of the proposed improved SZ 2.1 compression algorithm is compared with the five baseline algorithms including SZ 1.4, original SZ 2.1, selective compression, algorithm-based fault tolerance (ABFT), and digit rounding algorithms. The key parameters used in the experiment to measure the performance of the proposed algorithm are data reduction, compression ratio, power consumption, encryption time, total processing time, and error. Using the improved SZ 2.1 compression algorithm in conjunction with the ChaCha12-Poly1305 AEAD algorithm, the device's battery life is also enhanced by about 10% while ensuring the security of data disseminated to the Edge. The use of the proposed secure hybrid model reduces both encryption and overall processing time by 95% and 98% respectively for both datasets. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research paper presents a detailed analysis of a lightweight block cipher's (LWBC) power consumption and security features, specifically designed for IoT applications. To accurately measure energy consumption during the execution of the LWBC algorithm, we utilised the Qoitech Otii Arc, a specialised tool for optimising energy usage. Our experimental setup involved using the Otii Arc as a power source for an Arduino NodeMCU V3, running the LWBC security algorithm. Our methodology focused on energy consumption analysis using the shunt resistor technique. Our findings reveal that the LWBC is highly efficient and provides an effective solution for energy-limited IoT devices. We also conducted a comparative analysis of the proposed cipher against established LWBCs, which demonstrated its superior performance in terms of energy consumption per bit. The proposed LWBC was evaluated based on various key dimensions such as power efficiency, key and block size, rounds, cipher architecture, gate area, ROM, latency, and throughput. The results of our analysis indicate that the proposed LWBC is a promising cryptographic solution for energy-conscious and resource-limited IoT applications. [ABSTRACT FROM AUTHOR]

Probabilistic forecasting of power consumption in a middle-term horizon (few months to a year) is a main challenge in the energy sector. It plays a key role in planning future generation plants and transmission grid. This paper proposes a novel model that (i) incorporates seasonality and autoregressive features in a traditional time-series analysis and (ii) includes weather conditions in a parsimonious machine learning approach, known as Gaussian Process. Applying to a daily power consumption dataset in North East England, provided by one of the largest energy suppliers, we obtain promising results in Out-of-Sample density forecasts up to one year, even using a small dataset, with only a two-year calibration set. For the evaluation of the achieved probabilistic forecasts, we consider the pinball loss—a metric common in the energy sector—and we assess the coverage—a procedure standard in the banking sector after the introduction of Basel II Accords—also running the conditional and unconditional tests for probability intervals. Results show that the proposed model outperforms benchmarks in terms of both accuracy and reliability. [ABSTRACT FROM AUTHOR]

The accurate estimation of the power number for closed clearance impellers holds significant importance in industries such as chemical, biochemical, paper and pulp, as well as paints, pigments, and polymers. Existing state‐of‐the‐art correlations for predicting power numbers, however, are inaccurate for impeller Reynolds number ReI>100. In this study, we compiled a dataset of 1470 data points from 15 research articles in the open literature, covering five types of impellers: (i) anchor; (ii) gate; (iii) single helical ribbon; (iv) double helical ribbon; and (v) helical ribbon with screw. Six machine learning models, namely artificial neural networks (ANN), CatBoost regressor, extra tree regressor, support vector regressor, random forest, and XGBoost regressor, were developed and compared. The results revealed that ANN emerged as the most efficient model, demonstrating the highest testing R2 value of 0.99 and the lowest testing MAPE of 7.3%. Further, we used the ANN model to develop a novel set of process correlations to estimate impeller power numbers for the industrially important anchor and double helical ribbon impellers: which significantly outperform the existing state‐of‐the‐art correlations available in literature. [ABSTRACT FROM AUTHOR]

In large-format pouch cells for electric vehicles, the issues of elevated and non-uniform temperatures resulting from heat generation are intensified, necessitating the use of liquid cooling systems. The design factors of the liquid cooling system influence the maximum temperature and temperature differences in the module as well as the pumping power of the cooling system. Although it is known that these design factors interact, research on these interactions and their effects is currently lacking. In this study, the individual as well as interaction effects of design factors on the performance of the liquid cooling system for a large-format pouch cell module were investigated using design of experiment and analyzed through statistical methods. Electrochemical–thermal fluid coupled analysis was used to calculate the performance according to the design factors of the liquid cooling system. The wall and channel widths are factors that directly determine the coolant flow velocity and cooling plate heat capacity, and they exhibited major effects on all three responses. Moreover, the influence of each design factor tended to change in response to variations in the other design factors. Thus, the effects of each factor individually and of interactions between factors were quantitatively compared and evaluated for significance. The width of the walls was found to contribute the most to the maximum temperature (36.00%) and pumping power (57.56%), while the width of the channels contributed the most to the temperature difference (38.24%), indicating that they are the main influencing factors. [ABSTRACT FROM AUTHOR]

Summary: Multivalued logic (MVL) offers higher information density as compared with binary logic systems for an equal number of digits. In a ternary microprocessor, memory access is the most time‐ and power‐consuming action. In order to design a ternary computer, the possibilities for designing ternary encoders and decoders must be examined. This paper presents the designs of 9:2 unbalanced ternary encoder (TENC) and 2:9 unbalanced ternary line address decoder (TDEC) based on novel designs of standard ternary inverter (STI), ternary NAND (TNAND), and ternary NOR (TNOR) logic gates using resistive random‐access memory (RRAM) and carbon nanotube field effect transistor (CNTFET) technology. The simulation results indicate an improvement in performance parameters such as power consumption, delay, power–delay product (PDP), and component count of the proposed circuits in comparison with the different existing counterparts. Moreover, a detailed analysis is carried out on the impact of different process, voltage, and temperature (PVT) variations on the performance metrics, including propagation delay, power consumption, and PDP of the 9:2 unbalanced ternary encoder and 2:9 unbalanced ternary line address decoder. The proposed ternary encoder circuit shows an improvement of 62% in delay, 71% in power consumption, 88.6% improvement in PDP, and 53% reduction in CNTFET count, whereas the ternary decoder circuit exhibits an improvement of 6.6% and 94% in delay and 43% and 94% improvement in power consumption, respectively. Moreover, the CNTFET count is reduced by 16.07% and 46%. [ABSTRACT FROM AUTHOR]

Detection of abnormal power consumption is very important to reduce irregular power consumption and economic losses. To reduce power wastage, it is necessary to understand the power consumption habits of users and detect unusual usage behavior promptly. Researchers have developed various methods to detect abnormal electricity consumption behavior. However, it does not provide enough consideration to identify the relationships between different time stamps and multi-scale characteristics of the power utilization series. Therefore, a grey relation projection–based better clustering learning technique is proposed for assessing power consumption abnormalities. Power data from a power consumption information gathering system is analyzed with a diagnosis model for anomalous electricity use by customers constructed using big data techniques and machine learning-related algorithms. Density-based clustering is offered as a means of analyzing instances of excessive electricity use. First, consumers in the same area are grouped based on the similarities between their electricity usage patterns. Then, using density clustering, we identify the most extreme cases of electricity use amongst consumers who are otherwise comparable. Using a comparable user electricity consumption framework and the historical electricity usage approach, we can determine the degree to which these anomalies match the norm. Finally, the threshold value allows for discrimination of anomalous electricity usage with the level of comprehensive support. The proposed DBSCAN approach attains the PR-AUC values of 0.96, and AUC attains 0.911, results from simulations demonstrate that this approach can detect aberrant patterns of electricity consumption. [ABSTRACT FROM AUTHOR]

Currently, people spend a lot of time using their mobile devices. With such ubiquity of mobile devices in our daily life, battery capacity and quality are of utmost importance. Running software applications (called apps) are one of the major factors influencing the power consumption in mobile devices. In order to meet user needs, mobile apps are becoming inherently complex and resource greedy. Therefore, fulfilling these requirements at the pace imposed by the market may degrade software construction quality and insert so-called energy code smells: bad patterns in the source code of an app that indicate a deeper problem and adversely affect power consumption. This work proposes a developer-oriented framework for identifying and fixing patterns via analyzing different application code flavors in a user-driven test scenario. A special app was designed in order to validate the Android implementation of the proposed methodology. The study results have shown significant improvement regarding energy efficiency after correcting one or more energy code smells, with a 4 to 30 percent decrease in battery drain. Additionally, the power consumption signature term is defined in the context of mobile applications. This paper presents a developer-oriented framework for assessing power consumption in mobile applications. Our key contributions include identifying significant energy code smells, demonstrating their impact on power consumption, and providing a toolset for developers to improve energy efficiency. [ABSTRACT FROM AUTHOR]

• Semi-empirical models for suction flow rate, injection flow rate, input power consumption, and discharge temperature are proposed for scroll compressors with vapor injection. • The modeling approach can be consistently applied to fixed-speed and variable-speed compressors. • The models were fit and evaluated using a data set containing four compressors. The prediction accuracy is similar to other proposed models and the model requires relatively low testing effort to fit the empirical parameters. • The models were applied to one compressor for operation with and without vapor injection. The prediction errors for both modes of operation are similar, while only using one set of model parameters. Semi-empirical models for predicting the suction flow rate, injection flow rate, input power consumption, and discharge temperature are proposed for scroll compressors with vapor injection. A data set containing four compressors wherein each compressor was tested over a range of operating conditions is used to evaluate the models. The fitted parameters of the models and the prediction errors of each data set are discussed. The accuracy and generalizability of applying the models to the same compressor with and without vapor injection are also discussed. [ABSTRACT FROM AUTHOR]

Copyright of Misr Journal of Agricultural Engineering is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Industrial development has implicitly led to the development of new systems that increase the ability to provide services and products in real time. Autonomous mobile robots are considered some of the most important tools that can help both industry and society. These robots offer a certain autonomy that makes them indispensable in industrial activities. However, some elements of these robots are not yet very well outlined, such as their construction, their lifetime and energy consumption, and the environmental impact of their activity. Within the context of European regulations (here, we focus on the Green Deal and the growth in greenhouse gas emissions), any industrial activity must be analyzed and optimized so that it is efficient and does not significantly impact the environment. The added value of this paper is its examination of the activities carried out by mobile robots and the impact of their electronic components on the environment. The proposed analysis employs, as a central point, an analysis of mobile robots from the point of view of their electronic components and the impact of their activity on the environment in terms of energy consumption, as evaluated by calculating the emission of greenhouse gases (GHGs). The way in which the activity of a robot impacts the environment was established throughout the economic flow, as well as by providing possible methods of reducing this impact by optimizing the robot's activity. The environmental impact of a mobile robot, in regard to its electronic components, will also be analyzed when the period of operation is completed. [ABSTRACT FROM AUTHOR]

The complex fluid dynamics mechanism of multi‐shaft stirring systems has constrained their application in industrial production. In this work, we have extracted torque signals to correlate power consumption with rotational speeds across single, dual, and triple shaft stirred reactors. Remarkably, the reasons behind the unique linear power consumption‐rotational speed relationship exhibited in triple‐shaft reactor, through the application of Fractional Fourier Transform. Our comparative analysis, through mixing energy, reveals multi‐shaft systems' superiority in energy‐efficient mixing. More importantly, the interaction between impellers and fluid within a reactor is reflected in the evolving of torque chaotic attractors. We have established a novel characterization strategy by fractal dimension of attractors which reflect the reactors' mixing efficiency. By mathematically modeling these attractors, we have demonstrated their potential as a tool for establishing the internal dynamics equations of stirred reactors. This approach offers a new method for evaluating mixing effectiveness and automated control of stirring machinery. [ABSTRACT FROM AUTHOR]

Summary Even with existing methodologies, dynamic resource allocation, service inconsistency, operational costs, and excess energy/power consumption in optical access networks remain the key challenges for the next‐generation passive optical network (NG‐PON) design and development. This paper introduces a novel method for analyzing energy/power consumption in time and wavelength division multiplexed (TWDM) passive optical networks (PONs) with a ring‐based topology. We propose subscriber‐based channel allocation models (CAMs) for on‐demand services, allocate bandwidth dynamically, and evaluate their downstream power consumption. To determine the necessary optical devices for the various CAMs, we investigate subscriber take‐up rates by optimizing the utilization of optical components based on active subscribers. Implementing a novel Poisson distribution‐based network traffic model addresses energy‐related concerns and explores optimization techniques to minimize power and improve energy efficiency. With a take‐up rate of 64 active subscribers, simulation results show significant power consumption reductions ranging from 62.2 to 67.7%, achieved by the proposed subscriber‐based architecture models with varying subscriber counts, when compared with the conventional model. The model with the most subscribers (2048 subscribers) saves the most energy by reducing power consumption to 292 W from the conventional model's (2048 subscribers) 440 W. Furthermore, the model with the most subscribers saves 33.4% of energy, outperforming other subscriber‐based architecture models without affecting QoS even during peak hours of the day. These numerical results show that TWDM‐PONs with the most subscribers exhibit improved energy efficiency than the conventional and other proposed subscriber‐based architecture models. [ABSTRACT FROM AUTHOR]

The ternary logic has a benefit over the binary logic which provides a secured solution to achieve a trade-off between the area and power of the design. However, from the structure of the ternary Aritmetic Logic Unit (ALU), it is clear that its architecture increases the area, propagation delay, and power consumption. To overcome this drawback, a loopback algorithm is proposed to achieve low power and high throughput Internet of Things (IoT) processors. The loopback algorithm reduces the number of processing stages in multipliers and adders which can significantly reduce area and power dissipation. The proposed 2:1 multiplexer-based approach reduces the need for a decoder and results in low power consumption. The proposed design will be implemented in Xilinx ISE 13.0 and simulation will be done in Modelsim. The modified Ternary ALU (TALU) performs finer than the previous TALU method. The number of registers used in this architecture is reduced by up to 25% than the existing system therefore there is a reduction in power dissipation. [ABSTRACT FROM AUTHOR]

This study introduces a mixed integer secondorder cone programming (MISOCP) model called the green Ellipsoid model for power saving in communication networks. The goal of this research is to optimize power consumption in communication networks by applying the conic optimization technique which is an emerging branch of research. The consumed power in communication networks is minimized considering network data variation using robust optimization. We apply the ellipsoidal uncertainty set to grant a variation in the network data and presented the model in the form of MISOCP. To scale down the total energy utilization in the network, the study proposes shutting down some unnecessary links throughout the entire network. By identifying and deactivating these redundant or unused links, the overall power consumption is reduced. Based on the numerical results obtained, the proposed green Ellipsoid model demonstrates the ability to save consumed power in comparison to the existing green Hose approach. The results indicate that the green Ellipsoid model exceeds the green Hose approach in terms of power savings. This suggests that the introduced model is more adequate in optimizing power utilization and achieving energy efficiency in the communication networks. An additional advantage of the green Ellipsoid model is its ability to accommodate data variation across the network through a single parameter. Acknowledging that MISOCP is a known NP-hard problem, the study also proposes an improved method to minimize computation time. This is achieved by leveraging optimization software tools that make the model tractable. By utilizing these tools, the presented model can be efficiently solved within an acceptable time. [ABSTRACT FROM AUTHOR]

The rapid advancement of software-based deep learning has led to a surge in AI applications, yet hardware limitations in silicon CMOS technology hinder performance. Consequently, interest is growing in hardware technologies and new materials for artificial neural networks (ANNs) and neuromorphic systems. Exploiting nanomaterials nonlinearity caused by spontaneous physical phenomena holds promise of reducing power consumption in AI hardware. Reservoir computing devices, derived from recurrent neural networks, play a crucial role, with material reservoir devices showcasing "material intelligence." Tailored nanomaterials for reservoir devices hold potential for revolutionizing AI, especially in robotics. As research progresses, focusing on device functionalization and applications, recent findings underline the significance of integrating nanomaterials into AI hardware for enhanced computational capabilities and energy efficiency. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has been widely applied to numerous areas and aspects of human life and modern society. Many such applications can be represented as directed acyclic graphs (DAG). Device-edge-cloud fusion provides a new kind of heterogeneous, distributed, and collaborative computing environment to support various MEC applications. DAG scheduling is a procedure employed to effectively and efficiently manage and monitor the execution of tasks that have precedence constraints on each other. In this paper, we investigate the NP-hard problems of DAG scheduling and energy-constrained DAG scheduling on mobile devices, edge servers, and cloud servers by designing and evaluating new heuristic algorithms. Our contributions to DAG scheduling can be summarized as follows. First, our heuristic algorithms guarantee that all task dependencies are correctly followed by keeping track of the number of remaining predecessors that are still not completed. Second, our heuristic algorithms ensure that all wireless transmissions between a mobile device and edge/cloud servers are performed one after another. Third, our heuristic algorithms allow an edge/cloud server to start the execution of a task as soon as the transmission of the task is finished. Fourth, we derive a lower bound for the optimal makespan such that the solutions of our heuristic algorithms can be compared with optimal solutions. Our contributions to energy-constrained DAG scheduling can be summarized as follows. First, our heuristic algorithms ensure that the overall computation energy consumption and communication energy consumption does not exceed the given energy constraint. Second, our algorithms adopt an iterative and progressive procedure to determine appropriate computation speed and wireless communication speeds while generating a DAG schedule and satisfying the energy constraint. Third, we derive a lower bound for the optimal makespan and evaluate the performance of our heuristic algorithms in such a way that their heuristic solutions are compared with optimal solutions. To the author’s knowledge, this is the first paper that considers DAG scheduling and energy-constrained DAG scheduling on edge and cloud servers with sequential wireless communications and overlapped communication and computation to minimize makespan. [ABSTRACT FROM AUTHOR]

This research explores the intersection of sustainability, load balancing, request management, resource diversity, system capability, and cost-effectiveness in the design of resilient and environmentally responsible engineering systems. Sustainability, a cornerstone of societal progress, encompasses the pursuit of enduring harmony on Earth, encompassing environmental preservation, economic prosperity, and social welfare. Technological advancement is intricately tied to the efficient and responsible utilization of resources. Load balancing, a critical component of computing communication, seeks to optimize workload distribution across computing resources to mitigate redundancies and bottlenecks, ensuring smooth network operation. In this paradigm, the amount and type of requests becomes a critical component, which determines the extent to which resources can be adequately distributed to process the incoming tasks. Every system has different system parameters, such as CPU counts and memory, which determine the capability of a system to process different types of tasks. Load balancing, which balances the variables, including requests, ensures that imbalances do not occur and that this approach strengthens system performance as a demonstration of a close connection between sustainability and load balancing. However, the proposed algorithm makes a futuristic approach by suggesting that a resource allocation matrix, considering the number of requests, types of resources, CPU counts, cost, and memory. Moreover, the algorithm also enhances durability goals as it helps in the improvement of resource utilization and thus helping in load distribution. Lastly, the dynamic resource allocation is made achievable by the algorithm can also enhance higher resource efficiency, and reduced system power consumption. In conclusion, some of the traditional algorithms was compared with the proposed algorithm which is reflect better or high performance noted on the proposed algorithm. [ABSTRACT FROM AUTHOR]

The conventional planar Metal-Oxide-Semiconductor FETs (MOSFETs) are being replaced with Fin Field-Effect Transistors (FinFETs) due to their improved ability to manage power dissipation, propagation delay, leakage current, and short channel effects. Process variability is an issue for planar MOSFETs, however the amount of dopant ions in FinFETs reduces device performance variability. In this study, a Static-Random Access Memory (SRAM) cell employing FinFET technology is designed using three MOS transistors. It is composed of two NMOS plus a single PMOS transistor. One NMOS acts as access transistor while the pull-up and pull-down functions are performed by remaining NMOS and PMOS transistors, respectively. The proposed SRAM cell is simulated using H-SPICE simulator and is compared with existing SRAM cell designs in terms delay, power consumption and transistor count. Performance analysis shows that the proposed SRAM Cell overcomes the constraint and achieves full swing storage of logic values between 0 and 1. This justifies the definition of static. Implementing a single-ended SRAM cell also has the benefit of simplifying the SRAM cell architecture, which also results in a reduction in area. [ABSTRACT FROM AUTHOR]

Power consumption in the home has grown in recent years as a consequence of the use of varied residential applications. On the other hand, many families are beginning to use renewable energy, such as energy production, energy storage devices, and electric vehicles. As a result, estimating household power demand is necessary for energy consumption monitoring and planning. Power consumption forecasting is a challenging time series prediction topic. Furthermore, conventional forecasting approaches make it difficult to anticipate electric power consumption since it comprises irregular trend components, such as regular seasonal fluctuations. To address this issue, algorithms combining stationary wavelet transform (SWT) with deep learning models have been proposed. The denoised series is fitted with various benchmark models, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Bidirectional Gated Recurrent Units (Bi-GRUs), Bidirectional Long Short-Term Memory (Bi-LSTM), and Bidirectional Gated Recurrent Units Long Short-Term Memory (Bi-GRU LSTM) models. The performance of the SWT approach is evaluated using power consumption data at three different time intervals (1 min, 15 min, and 1 h). The performance of these models is evaluated using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The SWT/GRU model, utilizing the bior2.4 filter at level 1, has emerged as a highly reliable option for precise power consumption forecasting across various time intervals. It is observed that the bior2.4/GRU model has enhanced accuracy by over 60% compared to the deep learning model alone across all accuracy measures. The findings clearly highlight the success of the SWT denoising technique with the bior2.4 filter in improving the power consumption prediction accuracy. [ABSTRACT FROM AUTHOR]

Background: The fast growth of the Internet of Things (IoT) in Fifth-Generation (5G) networks has emphasised the need to examine energy efficiency thoroughly. Ensuring the sustainable implementation of IoT devices in these sophisticated networks is paramount. Objective: The article seeks to experimentally examine the disparities in energy use between two commonly utilised microcontroller platforms, namely the Arduino Nano and Arduino Micro when included in 5G network settings. Methodology: The power consumption of the Arduino Nano and Arduino Micro was evaluated by connecting them to 5G connectivity modules. The assessment centered on three operating states: data acquisition, transfer, and idle. Power measurements were performed using calibrated instruments in a controlled experimental environment to achieve precise and consistent results. Results: The findings indicated that the Arduino Micro, while carrying out comparable computing and data transmission operations in a 5G setting, spent around 8% less power than the Arduino Nano. The difference in energy consumption may be primarily due to the Arduino Micro's superior voltage control technologies and optimized low-power components. In contrast, the Arduino Nano demonstrated reduced power consumption in standby mode, indicating an intricate and non-linear correlation between operational states and power utilisation. Conclusion: The results of this study provide vital information for choosing microcontroller platforms for 5G IoT applications. The Arduino Micro is a more energy-efficient option for jobs requiring constant data processing and transmission. On the other hand, the Arduino Nano is better suited for situations where there is intermittent network access. The article contributes substantially to the current discussion on improving energy efficiency in Internet of Things (IoT) devices for 5G networks.

Abstract The development of smart grids, traditional power grids, and the integration of internet of things devices have resulted in a wealth of data crucial to advancing energy management and efficiency. Nevertheless, public datasets remain limited due to grid operators' and companies' reluctance to disclose proprietary information. The authors present a comprehensive analysis of more than 50 publicly available datasets, organised into three main categories: micro‐ and macro‐consumption data, detailed in‐home consumption data (often referred to as non‐intrusive load monitoring datasets or building data) and grid data. Furthermore, the study underscores future research priorities, such as advancing synthetic data generation, improving data quality and standardisation, and enhancing big data management in smart grids. The aim of the authors is to enable researchers in the smart and power grid a comprehensive reference point to pick suitable and relevant public datasets to evaluate their proposed methods. The provided analysis highlights the importance of following a systematic and standardised approach in evaluating future methods and directs readers to future potential venues of research in the area of smart grid analytics.

Power consumption in the home has grown in recent years as a consequence of the use of varied residential applications. On the other hand, many families are beginning to use renewable energy, such as energy production, energy storage devices, and electric vehicles. As a result, estimating household power demand is necessary for energy consumption monitoring and planning. Power consumption forecasting is a challenging time series prediction topic. Furthermore, conventional forecasting approaches make it difficult to anticipate electric power consumption since it comprises irregular trend components, such as regular seasonal fluctuations. To address this issue, algorithms combining stationary wavelet transform (SWT) with deep learning models have been proposed. The denoised series is fitted with various benchmark models, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Bidirectional Gated Recurrent Units (Bi-GRUs), Bidirectional Long Short-Term Memory (Bi-LSTM), and Bidirectional Gated Recurrent Units Long Short-Term Memory (Bi-GRU LSTM) models. The performance of the SWT approach is evaluated using power consumption data at three different time intervals (1 min, 15 min, and 1 h). The performance of these models is evaluated using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The SWT/GRU model, utilizing the bior2.4 filter at level 1, has emerged as a highly reliable option for precise power consumption forecasting across various time intervals. It is observed that the bior2.4/GRU model has enhanced accuracy by over 60% compared to the deep learning model alone across all accuracy measures. The findings clearly highlight the success of the SWT denoising technique with the bior2.4 filter in improving the power consumption prediction accuracy.

Abstract Fast Fourier transform (FFT) is a fundamental building block for digital signal processing applications where high processing speed is crucial. Resource utilization in implementing FFT structures can be minimized by optimizing the performance of multipliers and adders used within the design. FFTs are also widely used in various machine learning algorithms. To achieve increased processor efficiency and reduced resource utilization, we propose a hardware design for Radix-2, Radix-4, and Split-Radix FFT architectures that utilizes a novel parallel prefix adder. This design offers lower power consumption, smaller chip area, and faster operation compared to existing architectures. Our performance analysis focuses on metrics such as power consumption, clock speed, and hardware complexity for Radix-2, Radix-4, and Split-Radix FFT algorithms implemented with the proposed adder. We compare these metrics using our proposed arithmetic structure against existing adder designs. The results indicate that the Split-Radix FFT architecture achieves lower power consumption and smaller chip area compared to Radix-4 and Radix-2 methods. Additionally, the Split-Radix FFT exhibits a higher clock speed. Therefore, based on these findings, the Split-Radix algorithm appears to be a compelling choice for implementation on field-programmable gate arrays due to its high speed and lower power consumption.

The research of energy consumption in an Internet of Things network and its analytical evaluation is the goal of this work. The authors of this work concentrate on developing a model for calculating the actual gain in power consumption in order to estimate the actual energy required. The method suggests measuring the difference in energy usage under three primary battery-powered working modes to maximize a device's lifetime. Due to the fact that each CPS device state has its own energy metrics, it is feasible to choose the best operation course for entire network. The presented technique is certainly viable, as demonstrated by the experimental examination of Zigbee and BLE devices. The comparison of power levels using a temperature sensor in three basic scenarios (power modes) dictates how the CPS device lifetime can be optimized. Multi-regime consumption models, in which the rates of charging and discharging are dependent upon the energy level, are analyzed in this paper. This work aimed to state an optimal energy consumption by finding the right balance between operational power and battery lifetime through mathematical modeling. Therefore, it is easy to determine the energy cost of power stage, for instance, to send data by setting the minimal duration of each working condition in terms of power consumption. Moreover, a reasonable balance of power consumption and battery lifetime which impacts the data collection from sensors is vital to the development of data extraction algorithms. The practical results depict how device should be accessible to be able to lose less power even during switching on/off or how operate more effective if it used for a short period of time. A long-term network could become a reality once battery life is optimized enough to not disturb a user.

Comminution, particularly post-crushing, is a critical phase in mineral processing that reduces run-of-mine ore to an acceptable particle size before undergoing a downstream beneficiation process. Despite its importance, this stage has been under-investigated probably due to challenges of sampling and data collection and inadequate technical and financial support. This research presents a multi-method machine learning framework designed to simulate the power dynamics within an industrial-scale iron ore cone crusher circuit. The framework integrates advanced artificial intelligence techniques to evaluate energy consumption and estimate cone crushers output K80 accurately. The developed framework spans from different algorithms by data preprocessing techniques such as normalization and Principal Component Analysis (PCA) to standardize and reduce the dataset’s dimensionality, coupled with multiple machine learning algorithms, including Gradient Boosting Machines (CatBoost, LightGBM, XGBoost), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks. The study utilized real-time data collected over six months by considering the key operational parameters such as total tonnage, hopper filling levels, power draw, cone position, and crusher discharge settings. The modeling approach was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Asymmetric Loss (AL), and the coefficient of determination (R2) as performance metrics. Results indicated that the LSTM model outperformed other methods by achieving the lowest MAE and RMSE and the highest R2 score of 0.92, thereby demonstrating superior precision in estimation. However, XGBoost showed optimal performance in managing asymmetric loss, highlighting its efficiency in error management across different scenarios. Model residual analysis, performance metrics box plots, and scatter plots comparing measured and estimated data across the models are also provided for further analysis. This study demonstrates the transformative potential of machine learning models in refining industrial process simulations, ultimately driving enhanced productivity and sustainability in the mining industry. [ABSTRACT FROM AUTHOR]

Fast Fourier transform (FFT) is a fundamental building block for digital signal processing applications where high processing speed is crucial. Resource utilization in implementing FFT structures can be minimized by optimizing the performance of multipliers and adders used within the design. FFTs are also widely used in various machine learning algorithms. To achieve increased processor efficiency and reduced resource utilization, we propose a hardware design for Radix-2, Radix-4, and Split-Radix FFT architectures that utilizes a novel parallel prefix adder. This design offers lower power consumption, smaller chip area, and faster operation compared to existing architectures. Our performance analysis focuses on metrics such as power consumption, clock speed, and hardware complexity for Radix-2, Radix-4, and Split-Radix FFT algorithms implemented with the proposed adder. We compare these metrics using our proposed arithmetic structure against existing adder designs. The results indicate that the Split-Radix FFT architecture achieves lower power consumption and smaller chip area compared to Radix-4 and Radix-2 methods. Additionally, the Split-Radix FFT exhibits a higher clock speed. Therefore, based on these findings, the Split-Radix algorithm appears to be a compelling choice for implementation on field-programmable gate arrays due to its high speed and lower power consumption. [ABSTRACT FROM AUTHOR]

The proton exchange membrane fuel cell (PEMFC) in a fuel cell electric bus (FCEB) converts hydrogen's chemical energy into electrical energy. The fuel cell system comprises a fuel cell stack and a balance of plant (BOP) system, which efficiently controls the stack. Fuel cell and battery are sensitive to operational temperature, which directly impacts performance, lifespan, and safety. Therefore, a thermal management system (TMS) is necessary to maintain an appropriate temperature by dissipating the heat generated by the fuel cell and battery. In this study, the exponential or quadratic relationships between the power consumption of the major components of an FCEB and various factors, such as temperature and flow rate influencing the operational behavior and control of the components, were analyzed based on the results of a dynamometer vehicle test. Additionally, the vehicle's energy flow was calculated under different fuel cell load conditions. When the fuel cell operated at 56.3 kW, TMS power was 6.6 times higher than at 20 kW. At full load, under 90 kW, it increased to 17.4 times higher. The rise in fuel cell load correlated with higher heat generation, resulting in a significant increase in power consumption for both the radiator fan and coolant pump. [ABSTRACT FROM AUTHOR]

Electromagnet coil is mainly composed of enameled wire which wound on the coil skeleton, and the function is to convert the electrical signal into a magnetic signal, which is a very important electric‐magnetic conversion element. When designing the electromagnet coil, usually only the total winding turns and winding inner diameter can be calculated, and the specific design parameters such as the diameter of copper wire, paint layer thickness, and winding turns of each layer can only be selected through experience, indicating that the design method should be improved. In addition, the unreasonable design parameters will seriously affect the power consumption and cost of coil, so it is highly necessary to improve the optimization design method. In order to solve the above problems, an optimization design method for DC electromagnet coil is proposed in this paper. This method simultaneously takes into account both power consumption and manufacturing cost, and can achieve the optimal matching of design parameters. In this paper, the structure of electromagnet coil is introduced, and the theoretical calculation model of resistance is improved first. Then, according to the expected requirements of electromagnet coil for steady current, the optimization design method is proposed and the optimal design parameters are obtained. Finally, the effectiveness of the proposed optimization design method is verified by the test platform of electromagnet coil. The results show that the electromagnet coil designed by the optimization design method fully meets the expected requirements. The relevant research results can provide reference for the optimization design of electromagnet coil, which has a certain engineering practical value. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With the explosion of data in the Internet of vehicles, large power consumption, low quality of service, and content delivery security issues have been brought. To solve these problems, a secure caching strategy based on content and resource allocation is proposed. Firstly, the caching optimization problem is formulated as a power consumption minimization problem with vehicle rate and resource constraints. Then, considering the characteristics of the content placement and content delivery, the power consumption optimization problem is decomposed into two sub-problems. The first sub-problem researches the impacts of vehicle preferences and content sizes on cache decisions, and a content popularity and size-aware caching strategy is proposed. The second sub-problem researches the effects of wireless resources on transmission power consumption, and an efficient content delivery strategy by iterative bandwidth and power allocation is proposed. After obtaining the optimal caching strategy, this paper combines the use of improved elliptic curve cryptography and advanced encryption standard algorithms to perform content secure placement. Simulation results demonstrate that the proposed strategy can significantly improve the power consumption and caching hit rate compared with benchmark schemes. [ABSTRACT FROM AUTHOR]

The interest in alternative logic technologies is continuously increasing for short nanometer designs. From this viewpoint, logic gates, full adder and D-latch designs based on graphene nanoribbon field effect transistors (GNRFETs) at 7 nm technology nodes were presented, considering that these structures are core elements for digital integrated circuits. Firstly, NOT, NOR and NAND gates were implemented using GNRFETs. Then, 28T full adder and 18T D-latch circuits based on CMOS logic were designed using GNRFETs. As the first result of this work, it was shown through HSPICE simulations that the average power consumption of the considered logic circuits employing GNRFETs was 78.6% lower than those built using classical Si-based MOSFETs. Similarly, the delay advantage of the logic circuits employing GNRFETs was calculated to be 53.2% lower than those using Si-based MOSFET counterparts. In addition, a deep learning model was developed to model both the power consumption and the propagation delay of GNRFET-based logic inverters. As the second result, it was demonstrated that the developed deep learning model could accurately represent the power consumption and delay of GNRFET-based logic circuits with the coefficient of determination (R2) values in the range of 0.86 and 0.99. [ABSTRACT FROM AUTHOR]

Due to extensive distribution and huge demand of energy efficient processes, the energy-saving of machining processes draws more and more attention, and a significant variety of methods have evolved to prognosis or optimise the energy consumption in machining operations. Similarly, the estimation of power consumption-cutting conditions relationships is of great importance for optimizing processing costs and for cleaner machining. Compared to traditional methods, machine learning (ML) approach is one of the effective analysis options to model machinability indicators such as cutting force, tool wear, power consumption and surface quality. In this study, PH13-8Mo stainless steel was machined with coated carbide inserts using primarily Dry, MQL, nano-Graphene + MQL, nano-hBN + MQL, Cryo, Cryo + MQL cutting environments. Power consumption and its signals during milling were measured and different machine learning models were applied to estimate the Pc. To develop the Pc model based on the ML algorithm, 70% of the power consumption data is reserved for training and 30% for testing. In all cutting environments, power consumption increased by an average of 3.14% as feed speed increased. The reduction in Pc compared to the dry cutting was calculated as an average of 2.2%, 3.17%, 2.57%, 4.88% and 5.45% for MQL, nano-Graphen + MQL, nano-hBN + MQL, Cryo, Cryo + MQL, respectively. It is seen that the developed prediction model can reflect the power consumption-parameter relationships at high accuracy. [ABSTRACT FROM AUTHOR]

Despite numerous studies regarding tool life modeling, it is usually difficult to apply conventional models to industrial fields because of factors such as impractical data acquisition systems, the complexity of model construction, and low adaptability to varying cutting conditions and volumes. This paper introduces a novel tool life model based on the power consumption of a machine tool with respect to varying process parameters; it was hypothesized that the proportion of the power consumed in tool wear (i.e., dissipating the cutting edge) to total power remains constant, regardless of the cutting conditions. Cutting power consumption was measured and integrated with the cutting length in the milling of titanium alloy. The experimental results confirmed the validity of the hypothesis; tool life was successfully predicted based on the accumulated cutting power consumption values, and the effects of variations in cutting volumes were compensated for based on cutting power consumption. The developed model was validated in a case with various feeds and cutting volumes mimicking a field case. Furthermore, the model was able to cope with unexpected incidents, such as a non-continuous coolant supply. Thus, the results demonstrate the applicability and practicality of using the integrated cutting power consumption value to predict tool life. Even in new machining environments such as with new tools and work materials, the developed model can easily be constructed with a small number of experiments to justify the model coefficients. Power consumption is expected to be more widely utilized for process monitoring in industry and as a health indicator based on its excellent performance and practicality. [ABSTRACT FROM AUTHOR]

One of the main sources of information dissemination is social networks. Changes in social and Internet of Things (IoT) relationships can affect the quality of service (QoS) of device-to-device (D2D) communication in fifth-generation (5G) networks. Integration of social networks and IoT networks will allow users to improve their communication abilities with proximity users. The most challenging issues are interference and channel uncertainty due to massive connectivity and random movement of users in an urban dynamic environment. In this paper, D2D-based social Internet of Things (D2D–social IoT) in a 5G network is modeled for urban regions. To address these issues, we formulate a throughput maximization problem under the maximum power and QoS constraints. The problem is divided into multiple subproblems to reduce interference and enhance throughput. First, we leverage the interference channel link for intelligent resource sharing with better QoS by the spatiotemporal method. The second one involves bisection-based power search optimization by utilizing the Mellin transformation method for latency and power minimization. Simulation results demonstrate that the proposed method significantly improves the network performance in terms of throughput, sum rate, and reduction in network latency by up to 19.91%, 26.24%, and 31.57%, respectively. It is expected that an inclusive implementation of the said method can enable resource allocation in 5G-enabled social IoT networks for the development of smart city. [ABSTRACT FROM AUTHOR]

Campus buildings often face issues with high energy consumption, low efficiency, and significant carbon emissions, making the creation of a green, low-carbon campus urgent. Utilizing solar photovoltaics on rooftops can provide an effective power solution to address high energy consumption. This study focuses on a university campus, employing the DeST energy consumption simulation software to model the HVAC systems, electrical devices, and hot water loads of five typical buildings. It combines this with calculations of available rooftop areas to assess the potential for rooftop solar photovoltaics. The results confirm varying annual electricity consumption among the different buildings, which directly correlates with building size and operational schedules. Among the five building types, sports facilities and academic buildings have relatively high rooftop photovoltaic self-sufficiency rates, exceeding 60%, while the library has the lowest, under 20%. The entire university campus has an annual rooftop photovoltaic self-sufficiency rate of 35%, significantly addressing the issue of high energy consumption in university campuses. This research provides a theoretical basis for implementing rooftop photovoltaic systems to achieve campus energy savings. [ABSTRACT FROM AUTHOR]

The shield tunneling method is commonly used in the development and construction of underground spaces, and the adjustment of its parameters is a crucial part of shield construction. However, there are relatively few studies on optimizing tunneling parameters from a sustainable perspective, with a focus on energy saving and emission reduction. This study addresses this gap by combining engineering geological conditions with shield machine propulsion parameters in a specific section of metro construction in China. By aiming to reduce power consumption and improve efficiency, an improved particle swarm optimization algorithm based on the concept of Pareto optimal solutions was employed to optimize the tunneling parameters. The results demonstrated that the optimized parameters reduced power consumption and improved efficiency. This validates the feasibility of the optimization scheme and its potential for broader applications in sustainable underground construction. [ABSTRACT FROM AUTHOR]

To improve the phenomenon of serious power consumption of a ladle furnace, the impact of argon flow rate on power consumption in the refining process is investigated through theoretical calculations based on the principle of alternating current measurement using Rogowski coil and integrator circuit. The study divides the power supply phase into four distinct flow rate regions in a single furnace of DH36 steel, a low‐alloy, high‐strength ship plate steel commonly used in shipbuilding and offshore platforms. The analysis of active power, current, and voltage trends, along with average power consumption per second at each flow rate region, reveals a potential for efficient reduction in power consumption. Additionally, experiments with different argon flow rates (300, 400, and 500 NL min−1) during the power supply stages of multifurnace argon tests of AB/A steel show improvements in desulfurization rate, heating rate, processing time, and power consumption per ton of steel heated by 1 °C and power consumption per ton of steel per second. [ABSTRACT FROM AUTHOR]

Wearable photoplethysmography (PPG) sensors provide real‐time monitoring of essential human health parameters by integrating a light source with a photodetector. However, they face a common challenge: the presence of a faint pulsating (AC) signal amidst a highly noisy and drifting non‐pulsatile background. This necessitates fine‐tuning how the light is spread out to make sure that a significant number of emitted photons carry vital physiological information and trigger the detector's photocurrent. An innovative organic PPG sensor with a microcavity organic light‐emitting diode (OLED) that emits light in a controlled direction, coupled with an annular organic photodetector (OPD) is demonstrated here. By adjusting the OLED's cavity lengths, the microcavity resonance peak is shifted, achieving angled directional emission. Placing the microcavity OLEDs at the center of the annular OPD enhances the portion of light that reflects from arterial vessels in the skin and tissues. This increases the AC signal for recording arterial vessel pulsations by 47.66%. This advancement simplifies sensor recognition and achieves an unprecedented low power consumption of only 9.96 µW for heart rate sensing, meeting essential recognition criteria. [ABSTRACT FROM AUTHOR]

Copyright of Chemical Industry / Hemijska Industrija is the property of Association of Chemical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The process of air entering the vapor compression refrigeration system is very common, and it can happen during installation, maintenance, or charging. Since air is a non-condensable gas compared to the refrigerant gas inside the system, it causes deterioration in the system mainly due to high condensing pressure. This experimental study contributes to verifying the negative effects that can be left by non-condensable gases in the vapor-compression refrigeration system via analyzing the performance of a chest freezer charged with R134a refrigerant without an air fraction and with three air fractions 1.7%, 2.5% and 3.3% under steady-state conditions and an ambient temperature of 32°C. The results revealed that the performance of the chest freezer deteriorated clearly with the increase in air fraction; at an air fraction of 3.3% the power consumption increased by 37.1% and the coefficient of performance decreased by 42.8% compared to without air fraction. In addition to high noise and vibration. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are a promising solution for sensor data collection (DC) in large-scale area. The challenge is to minimize the DC route, which will reduce UAV energy consumption and data latency. The novelty of this paper lies in its innovative approach to optimizing sensor data collection by UAVs. It combines Ant Colony Optimization (ACO) and K-means algorithms to establish an initial shortest route and introduces a unique method for grouping sensor nodes (SNs) along the route based on the UAV's footprint, reducing data latency and energy consumption for both UAV and sensors. First, an initial shortest route that traverses all SNs is established based on the ACO and the K-means algorithms. Second, we group the sensor nodes (SNs) along the initial route using the footprint of the UAV, so that the latter can collect the data of the group in one stop, instead of stopping at each SN. By sequencing the hovering locations, we obtain a (shorter) intermediate route. Third, we shorten this route even further, by applying ACO to the set of hovering locations of the intermediate route. The solution has been implemented fully in Python. The results show that the route length gets shorter progressively with each phase. To evaluate the performance of the solution objectively, we have compared it with four states of the art solutions. The results show vividly that the proposed solution produces a DC route 19.28% shorter than the shortest route produced by the four competitive solutions. Moreover, it demonstrates a remarkable improvement by retaining 44% of energy in most SNs while over 99% energy depletion observed in the five state-of-the-art competitive solutions. [ABSTRACT FROM AUTHOR]

AISI 5140 steel is an alloy frequently used in the manufacturing and automotive industries. This steel alloy is shaped using different manufacturing methods and cooling is required during this process. This research study included the milling of AISI 5140 steel utilizing various cutting settings and cooling/lubrication procedures. For this purpose, two cutting speeds (75–100 m/min), two feed rates (0.075–0.100 mm/rev), and four cooling media (dry, MQL, flood, nanofluid) were used. Then, 5% Mo nanoparticles were added to the nanofluid cutting fluid. Machinability and power consumption analyses were carried out using the input parameters selected in light of the manufacturer's recommendations and studies in the literature. The effects of sustainable cutting fluids and their parameters on machinability and power consumption were investigated through experiments. This study concluded that the use of nanofluid led to improvements in surface roughness, flank wear, and power consumption characteristics. It was determined that the flood environment is the most effective in reducing the cutting temperature. As a result, it is predicted that nanofluid cutting fluids can be used during machining. [ABSTRACT FROM AUTHOR]

This article addresses the growing need in resource-constrained edge computing scenarios for energy-efficient convolutional neural network (CNN) accelerators on mobile Field-Programmable Gate Array (FPGA) systems. In particular, we concentrate on register transfer level (RTL) design flow optimization to improve programming speed and power efficiency. We present a re-configurable accelerator design optimized for CNN-based object-detection applications, especially suitable for mobile FPGA platforms like the Xilinx PYNQ-Z2. By not only optimizing the MAC module using Enhanced clock gating (ECG), the accelerator can also use low-power techniques such as Local explicit clock gating (LECG) and Local explicit clock enable (LECE) in memory modules to efficiently minimize data access and memory utilization. The evaluation using ResNet-20 trained on the CIFAR-10 dataset demonstrated significant improvements in power efficiency consumption (up to 22%) and performance. The findings highlight the importance of using different optimization techniques across multiple hardware modules to achieve better results in real-world applications. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The alarming mortality rates associated with cardiac abnormalities emphasize the critical need for early and accurate detection of heart disorders to mitigate severe health consequences for patients. Electrocardiograms (ECG) are commonly employed instruments for the examination of cardiac disorders, with a preference for noise-free ECG signals to ensure precise interpretation. However, ECG signal recordings are susceptible to environmental interferences, including patient movement and electrode positioning. This paper introduces a hardware implementation for denoising ECG signals, leveraging a novel method by integrating high-order Synchrosqueezing Transform, Detrended Fluctuation Analysis, and Non-Local-Mean filter optimized by Particle Swarm Optimization (HSST-DFA-PSO-NLM) techniques on Field-Programmable Gate Array (FPGA) platforms. FPGA-based processing units are chosen for their outstanding performance attributes, including high re-programmability, speed, architectural flexibility, and low power consumption, resulting in efficient signal processing. The effectiveness of the designed filtering algorithm is evaluated using key criteria, including Signal-to-Noise Ratio (SNR) and Root Mean Square Error (RMSE) for performance assessment. Additionally, resource utilization metrics such as Look-Up Tables (LUTs), Flip Flops, and DSP Blocks, as well as power consumption measures including dynamic power and static or leakage power, are analysed across various FPGA boards (Virtex and Zedboards) utilizing the VIVADO environment. Comparative analyses are conducted to identify the most suitable FPGA board for implementation, highlighting the superior performance of the proposed design. Remarkably, the proposed denoising solution gives excellent SNR of 29.56, 29.68, and 28.86 by denoising various ECG noises. The RMSE attained by the model is also less than 0.05. This research advances the field of cardiac disorder detection by providing a reliable and efficient FPGA-based solution for ECG signal denoising, thereby enhancing the accuracy of early diagnosis and treatment. [ABSTRACT FROM AUTHOR]