Execution Of Algorithm Research Articles

Алгоритм вычисления температуры поверхности для восстановления потери данных Landsat 8–9 Collection 2 Level 2

The use of thermal images for the analysis of urban areas is presented in Proceedings of InterCarto. InterGIS, on the example of the city of Krasnoyarsk according to Landsat 8 data. New Landsat 8–9 collection 2 level 2 data became available in March 2021. The data has been supplemented with valuable scientific products such as atmospheric parameters (atmospheric spectral transmittance, up-going radiation and down-going radiation), earth surface temperatures, thermal radiation, quality assessment mask. A detailed study of the data showed that for many territories previously studied by the authors, data were not provided in full. There is partial or complete loss of data in the emissivity layer, and as a consequence the loss is repeated in the Earth’s surface temperature layers. The same situation with data loss is observed in a number of other large Russian cities. The current situation required a detailed study of the documentation describing the Landsat 8–9 algorithms. Having studied all the stages of the algorithm execution, the authors managed to restore the sequence of calculations, while retaining additional atmospheric parameters. The lost data in the emissivity channel has been replaced with a new layer computed via NDVI. As a result, the authors tested the operation of the proposed algorithm on the territory of the city of Krasnoyarsk. The result of the algorithm execution is layers with surface temperature values without data loss, except for scenes with high cloudiness. There is a slight discrepancy between the surface temperature values when using the described algorithm and initial data, due to the use of different sources to determine the emissivity.

A blockchain-based IIoT traceability system: ERC-721 tokens for Industry 4.0

As manufacturing firms increasingly utilize IIoT technologies and sensor data is integrated into their processes, centralized architectures and the expansion of value creation networks pose challenges regarding data integrity and transparency. This offers an application context for the blockchain and smart contract technologies. Through a design science research approach a prototypical instantiation in form of a private Ethereum blockchain with RFID sensors as data inputs and ERC-721 tokens is created. The artifact is implemented into an industrial demonstrator, where the immutable tracking of workpieces during their production processes and the algorithm execution time are evaluated in trial runs.

МЕТОДЫ МАСШТАБИРОВАНИЯ ЧИСЕЛ В МОДУЛЯРНОЙ АРИФМЕТИКЕ: ОБЗОР, РАЗРАБОТКА И ОЦЕНКА СЛОЖНОСТИ АЛГОРИТМОВ

The study describes two methods of numeral scaling in a modular number system: one which is based on the interval estimation and the other one which uses iterative algorithm of scaling number X by the coefficient K and includes both the stage of base system expansion and the scaling stage itself. The authors demonstrate the examples and results of algorithms operation provided by the programs developed via Python that simulate algorithms execution on a computer. Estimates of the algorithms complexity were defined in order to compare them and to detect the most appropriate ones.

A Hybrid Accuracy- and Energy-Aware Human Activity Recognition Model in IoT Environment

Personalised health and fitness provide users with information regarding their wellbeing and an opportunity to inform healthcare services for better patient outcomes. Underpinning this industry sector is the need to establish human activity recognition (HAR) in a ubiquitous manner. For example, through the use of smartwatches and/or mobile phones gathering information such as heart rates, movement, and steps of a user. The engineering challenge is providing accurate, informative, and timely data without rapidly depleting the mobile device's battery life. This problem is compounded as a number of algorithms used to process such data require substantial, cloud-based resources, to achieve higher accuracy. Therefore, a balance is required between battery depletion, accuracy of data, and timely delivery of results through a mixture of cloud and local algorithmic execution. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AE-HAR (Accuracy and Energy Aware-HAR)</i> model that delivers engineered solutions which approach optimal combinations in the consideration of energy consumption, accuracy, and timeliness of results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AE-HAR</i> introduces a “light-weight” <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">machine learning</i> on-device component identifying the probabilistic accuracy of data together with energy consumption identification requirements. A heuristic is then adopted to determine if cloud-enabled calculations are required while including possible performance costs related to the analysis of networking infrastructures. Our model is validated in a real-world environment through experimentation that demonstrates accuracy in excess of 93% and energy consumption savings in excess of 94%.

Metaheuristic Optimization Techniques Used in Controlling of an Active Magnetic Bearing System for High-Speed Machining Application

Smart control tactics, wider stability region, rapid reaction time, and high-speed performance are essential requirements for any controller to provide a smooth, vibrationless, and efficient performance of an in-house fabricated active magnetic bearing (AMB) system. In this manuscript, three pre-eminent population-based metaheuristic optimization techniques: Genetic algorithm (GA), Particle swarm optimization (PSO), and Cuckoo search algorithm (CSA) are implemented one by one, to calculate optimized gain parameters of PID controller for the proposed closed-loop active magnetic bearing (AMB) system. Performance indices or, objective functions on which these optimization techniques are executed are integral absolute error (IAE), integral square error (ISE), integral time multiplied absolute error (ITAE), and integral time multiplied square error (ITSE). The significance of an optimization technique and objective function can obtain only by implementing it. As a result, several comparisons are made based on statistical performance, time domain, frequency response behavior, and algorithm execution time. Finally, the applicability of optimization strategies in addition to the performance indices is determined with the aid of the comparative analysis. That could assist in choosing a suitable optimization technique along with a performance index for a high-speed application of an active magnetic bearing system.

АРХІТЕКТУРА РОЗПОДІЛЕНОГО ПРОГРАМНОГО ЗАСТОСУНКУ ДЛЯ ГЕНЕРУВАННЯ ЛАНДШАФТІВ ПЛАНЕТОЇДІВ

The procedural generation of planetoids finds its place in the field of visualization of virtual worlds in video games, movies, and simulation tools. Due to the growing popularity of the application, the requirements for the quality, uniqueness, and scalability of visualization results are increasing, which, in turn, leads to higher requirements for hardware computing resources. This paper proposes a solution for the architecture of a software system for generating planetoid landscapes, based on a combination of a distributed computing system and the use of parallelism based on the Morsel-Driven Query Execution algorithm to overcome hardware limitations. The computing model includes the following components: a main server that supports gRPC connections; worker servers that perform the task of generating planetoid landscapes in parallel; a geospatial database containing vector data of generated planetoids (rivers and reservoirs; geographic regions – biomes, forests, and other road segments) binary storage of three-dimensional models that are superimposed on the generated planetoid landscape; tileset-storage for storing raster data required for a generation; users who use the software system for generating planetoid landscapes to realize their own goals. The use of software agents in the built system allows unifying a set of algorithms as a single entity used for a particular stage of landscape generation and solving the problem of software system extensibility. A distributed messaging system, a broker, is used to send and process requests using a topic per software agent sequence position. The broker utilizes load balancing to deliver landscape generation requests to background workers. To analyze the system’s performance, experiments were conducted with different numbers of background workers (1, 2: 4, 8; 16) and segment sizes of 512, and 2048 pixels. The lowest average time for generating one segment was obtained when the segment size was 512 pixels and the number of segments was 64. The average segment generation time for the above experiments ranged from 0.33 to 9.32 seconds. The integrated architectural solution allowed to reduce the CPU time by 2 to 5 times compared to a system that uses the approach of storing queries in the database. The solution’s efficiency is especially noticeable with large amounts of data, which is determined by the number of segments and their size in pixels.

Impacts of Electric Vehicle Connected with Charging Station Using Student Psychology Optimization Algorithm (SPOA) and AdaBoost Algorithm

This paper presents an electric vehicle connected to a charging station based on the proposed method. The proposed technique is the joint execution of the Student Psychology Optimization Algorithm (SPOA) and the AdaBoost algorithm and is therefore called the SPOA-AdaBoost algorithm. In particular, the annualized social cost depends on CS and EVCS set from the objective function of the allocation model. The EVCS is linked with the CS and allows the charging service for electric vehicles. The vehicle-to-grid functions of electric vehicles are properly considered under the present optimization model. The EV load demands are considered as controllable resources, and EV optimal optimization problems are connected with allocation problems. When EV arrives at the charging station, it reports its own energy demand and expected departure time using the EVCS operator. Every EVCS could attack the details of electric vehicles via the proposed method. With this proper action, this method manages the energy demand and the total supply. The constraints are the power flow equations, equivalent load demands on the buses, branch current constraints, discrete size restrictions for CS, constraints on CS outputs, EV participation on V2G activities, mutual exclusivity of the EV charge and discharge statuses, EV Owner charge satisfaction, EV Owner satisfaction charge, EV SOC restrictions, Occupied CF quantities, and EVCS CF sufficiency. Among these, the execution of the present model done by the MATLAB/Simulink platform and the performance of the proposed model is likened with other systems.

HYBRID LOAD BALANCING POLICY TO OPTIMIZE RESOURCE DISTRIBUTION AND RESPONSE TIME IN CLOUD ENVIRONMENT

Load balancing and task scheduling are the main challenges in Cloud Computing. Existing load balancing algorithms have a drawback in considering the capacity of virtual machines while distributing loads among them. The proposed algorithm works toward solving existing issues, such as fair load distribution, avoiding underloading and overloading, and improving response time. It implements best practices of Throttled load balancing algorithm and Equally Shared Current Execution algorithm. Virtual machines are selected based on the ratio of their bandwidth and load allocation count. Requests are sent to a Virtual Machine with higher bandwidth and lower load allocation count. Proposed algorithm checks for the availability of VM based on their capacity. This process is performed by selecting two VMs and comparing their vmWeight capacity. The one with the least vmWeight is selected. CloudAnalyst is used for simulation, response time evaluation, and resource utilization evaluation. The simulation result of the proposed algorithm is compared with three well-known load-balancing algorithms. These are Round Robin, Throttled Load balancing algorithm, and Enhanced Active Monitoring. Load-balancing Proposed Algorithm selects VMs based on their Algorithm. The proposed algorithm has improved over other algorithms in load distribution, response time, and resource utilization. All virtual machines in the data centers are loaded with a relatively equal number of tasks according to their capacity. This resulted in fair resource sharing and load distribution.

Using Adaptive Zero-Knowledge Authentication Protocol in VANET Automotive Network

One of the most important components of intelligent transportation systems (ITS) is the automotive self-organizing VANET network (vehicular ad hoc network). Its nodes are vehicles with specialized onboard units (OBU) installed on them. Such a network can be subject to various attacks. To reduce the effectiveness of a number of attacks on the VANET, it is advisable to use authentication protocols. Well-known authentication protocols support a security policy with full trust in roadside unit (RSU) base stations. The disadvantage of these authentication protocols is the ability of the RSU to track the route of the vehicle. This leads to a violation of the privacy and anonymity of the vehicle’s owner. To eliminate this drawback, the article proposes an adaptive authentication protocol. An advantage of this protocol is the provision of high imitation resistance without using symmetric and asymmetric ciphers. This result has been achieved by using a zero-knowledge authentication protocol. A scheme for adapting the protocol parameters depending on the intensity of the user’s traffic has been developed for the proposed protocol. The scientific novelty of this solution is to reduce time spent on authentication without changing the protocol execution algorithm by reducing the number of modular exponentiation operations when calculating true and “distorted” digests of the prover and verifying the correctness of responses, as well as by reducing the number of responses. Authentication, as before, takes place in one round without changing the bit depth of the modulus used in the protocol. To evaluate the effectiveness of the adaptive authentication protocol, the VANET model was implemented using NS-2. The obtained research results have shown that the adaptation of the authentication protocol in conditions of increased density of vehicles on the road makes it possible to increase the volume of data exchange between OBU and RSU by reducing the level of confidentiality. In addition, a mechanism for verifying the authority of the vehicle’s owner for provided services has been developed. As a result of the implementation of this mechanism, vehicle registration sites (VRS) calculate the public key of the vehicle without using encryption and provide necessary services to the owner.

Avoiding the bit-reversed ordering in parallel multi-digit multiplication based on FFT

The paper for the parallel model of computation, a modification of the method of implementing the multiplication of multi-digit integers based on the fast Fourier transform (FFT) avoiding the bit-reversed ordering is proposed. The paper researches the calculation of FFT according to the “butterfly” scheme based on decimation-in- frequency and decimation-in-time methods, an input signal with elements in direct and bit-reversed order, with an increase and decrease in the number Fourier series coefficients at each step of the "butterfly", the use of a list of Fourier series coefficients in direct and bit-reversed order. The standard FFT-based multiplication algorithm uses the same “butterfly” operation to compute the forward and inverse Fourier transforms. The paper analyzes two combinations of the FFTFDN–FFTTBN and FFTFBN–FFTTDN “butterfly” calculation schemes for calculating forward and inverse discrete Fourier transforms (DFT) in the case of implementing the multi-digit operation in parallel computational model to exclude bit-reversed permutation. A scheme for distributing calculations among four processors is proposed, in which forward and inverse Fourier transform calculations are localized within one parallel processor. The proposed modification does not reduce the computational complexity in terms of the number of complex operations, but due to the exclusion of bit-reversed permutation, the number of synchronization commands between processors and data is reduced, which reduces the algorithm execution time. The scheme can be adapted to distribute the computations among a larger number of processors. Four algorithms for implementing FFT based on decimation-in-frequency and decimation-in-time methods, an input vector with elements in direct and bit-reversed orders are presented. To check the result of the calculation, the algorithm of multiplication avoiding the steps of bit-reversed ordering was implemented in the APL programming language. An example of calculation is given in the form of a table.

Selection of regional determinants of economic security

Subject. The article addresses the selection of informative and significant regional determinants of economic security. Objectives. The aim is to explore statistical and econometric tools to select regional determinants, to build economic security classifiers, using the machine learning algorithms. Methods. The determinants are selected, using the filter-based methods (on the basis of Chi-square, F-criterion, information content criterion), sequential and recursive selection in the opposite direction, exhaustive sampling and selection based on the "random forest" classifier. The above algorithms are implemented in the Scikit-learn and MLxtend libraries. The analytical environment Jupyter Notebook is used as a system providing the execution of feature selection algorithms. Results. I formed a training sample of 90% of the total set of regional economic determinants for 82 Russian regions. The determinants were ranked by significance. Logistic loss and model accuracy were set as a model evaluation criterion. The accuracy of created classifiers was between 78% and 93%. The paper establishes the significance of categorical variables that determine whether a particular region belongs to the resource, investment, or regional profitability cluster. The constructed heat map enabled to establish the degree of linear relationship between the level of economic security of the region and regional determinants. Conclusions. The most significant group of panel indicators to determine the class of economic security is the resource security of regions. The level of resource endowment depends on the volume of consumption and the share of manufactured goods in the GRP. To build a model to classify regions by the economic security level, five features are sufficient; this circumstance was established by automatic set of determinants, using the recursive feature elimination algorithm with cross-validation (RFECV). The implemented wrapping methods of feature selection demonstrated that significant indicators in the model are budget expenditures and the amount of debt per capita, investment risk and net financial result. The best quality of the classifier is provided in the course of an exhaustive set of features, using the K-Nearest Neighbors algorithm.

Large scale medical image online three-dimensional reconstruction based on WebGL using four tier client server architecture

The development of medical device technology has led to the rapid growth of medical imaging data. The reconstruction from two-dimensional images to three-dimensional volume visualization not only shows the location and shape of lesions from multiple views but also provides intuitive simulation for surgical treatment. However, the three-dimensional reconstruction process requires the high performance execution of image data acquisition and reconstruction algorithms, which limits the application to equipments with limited resources. Therefore, it is difficult to apply on many online scenarios, and mobile devices cannot meet high-performance hardware and software requirements. This paper proposes an online medical image rendering and real-time three-dimensional (3D) visualization method based on Web Graphics Library (WebGL). This method is based on a four-tier client-server architecture and uses the method of medical image data synchronization to reconstruct at both sides of the client and the server. The reconstruction method is designed to achieve the dual requirements of reconstruction speed and quality. The real-time 3D reconstruction visualization of large-scale medical images is tested in real environments. During the interaction with the reconstruction model, users can obtain the reconstructed results in real-time and observe and analyze it from all angles. The proposed four-tier client-server architecture will provide instant visual feedback and interactive information for many medical practitioners in collaborative therapy and tele-medicine applications. The experiments also show that the method of online 3D image reconstruction is applied in clinical practice on large scale image data while maintaining high reconstruction speed and quality.

Operational State Recognition of a DC Motor Using Edge Artificial Intelligence

Edge artificial intelligence (EDGE-AI) refers to the execution of artificial intelligence algorithms on hardware devices while processing sensor data/signals in order to extract information and identify patterns, without utilizing the cloud. In the field of predictive maintenance for industrial applications, EDGE-AI systems can provide operational state recognition for machines and production chains, almost in real time. This work presents two methodological approaches for the detection of the operational states of a DC motor, based on sound data. Initially, features were extracted using an audio dataset. Two different Convolutional Neural Network (CNN) models were trained for the particular classification problem. These two models are subject to post-training quantization and an appropriate conversion/compression in order to be deployed to microcontroller units (MCUs) through utilizing appropriate software tools. A real-time validation experiment was conducted, including the simulation of a custom stress test environment, to check the deployed models' performance on the recognition of the engine's operational states and the response time for the transition between the engine's states. Finally, the two implementations were compared in terms of classification accuracy, latency, and resource utilization, leading to promising results.

Improvement of the Secure Integration of IoT and Cloud Computing using Hybrid Encryption

Wireless Sensor Network (WSN) is an essential technology in many Internet of Things (IoT) applications, and since sensor nodes suffer from limited resources, it has become possible to overcome storage capacity problems using cloud computing, the integration of Internet of Things (IoT) with cloud computing (CC) seeks to achieve new levels of efficiency in service delivery. Security and privacy are key factors that slow down the rapid and widespread adoption and deployment of both IoT and cloud computing. In the proposed model, an integrated IoT system with cloud computing was developed starting from the analysis, and design, to the implementation to connect IoT devices with the cloud, the security is achieved by using a hybrid encryption mechanism which provides the performance advantages of symmetric and asymmetric encryption algorithms. Where the Elliptic Curve Cryptography (ECC) algorithm is used for key generation and AES (Advanced Encryption Standard) algorithm is used for encryption and decryption of the sensors' data to provide a reliable computing environment. We have implemented the proposed system and showed the results of using CONTIKI COOJA 3.0 that connected with the cloud service provider, Evaluate a set of performance metrics such as power consumption, packet delivery ratio, and the algorithm execution time, in addition to verifying network immunity against the black hole attack.

METHODS FOR TABULAR IMPLEMENTATION OF ARITHMETIC OPERATIONS OF THE RESIDUES OF TWO NUMBERS REPRESENTED IN THE SYSTEM OF RESIDUAL CLASSES

Context. Implementation of modular arithmetic operations of addition, subtraction and multiplication by a tabular method based on the use of the tabular multiplication code. The object of the study is the process of tabular implementation of basic arithmetic operations on the residues of numbers represented in the system of residual classes.&#x0D; Objective. The goal of the work is to develop methods for the tabular implementation of the arithmetic operations of multiplication, addition and subtraction of the residues of two numbers based on the use of the tabular multiplication code.&#x0D; Method. Tabular methods for implementing integer arithmetic modular operations of addition, subtraction and multiplication are proposed for consideration. In order to reduce the amount of equipment for a tabular operating unit of computer systems that implements modular operations of addition, subtraction and multiplication by reducing the coincidence circuits AND in the nodes of the tables for implementing arithmetic operations based on the code of table multiplication, two methods for performing arithmetic modular operations of addition and subtraction have been developed. These methods are based on the code of tabular multiplication, the use of which will reduce the amount of equipment of the tabular operating unit. Thus, despite the difference in the digital structure of the tables of modular operations of addition, subtraction and multiplication based on the use of the tabular multiplication code, two new tabular methods for implementing arithmetic modular operations of addition and subtraction have been created. Based on them, algorithms for tabular execution of modular arithmetic operations of addition and subtraction have been developed. Using these algorithms, it is possible to synthesize a structurally simple, highly reliable and fast table operating unit that operates in a system of residual classes, which is based on three separate permanent storage devices (read-only memory), each of which implements only one fourth of the corresponding complete table of values of the modular operation, what is earlier in the theory tabular arithmetic was supposed to be impossible.&#x0D; Results. The developed methods are justified theoretically and studied when performing arithmetic modular operations of addition, subtraction and multiplication using tabular procedures.&#x0D; Conclusions. The conducted examples of the implementation of integer arithmetic modular operations of addition and subtraction can be considered as presented experiments. The results obtained make it possible to recommend them for use in practice in the design of computer systems operating in a non-positional number system in residual classes. Prospects for further research may be to create a tabular method for implementing integer arithmetic modular division operations based on the use of the tabular multiplication code.

Variability testing of software product line: A preference-based dimensionality reduction approach

Multi- and many-evolutionary algorithms have been applied to derive products for the variability testing of Software Product Lines (SPLs). This problem refers to the selection of an adequate product set to test a SPL by optimizing some objectives related to the number of products to be tested, testing criteria to be satisfied, and revealed faults. However, some problems emerge when the number of objectives to be optimized increases, for example: the solutions generated by the optimization algorithms become incomparable, designing a Pareto-front in this context requires a large number of solutions, and the visualization of such solutions requires special techniques. Several techniques are proposed to tackle this problem, such as decomposition and algorithms based on indicators. Among them, algorithms based on dimensionality reduction and user preferences are widely used, but there are no studies in the literature investigating the usage of both in a combined way. In light of this, we introduce COR-NSGA-II (Confidence-based Objective Reduction NSGA-II). COR-NSGA-II defines for each objective a confidence-level calculated with the user preferences provided interactively. The objectives with higher values of confidence are removed from the next algorithm execution. For assessing the feasibility of COR-NSGA-II, experiments were conducted by using six different SPLs, seven objectives, two types of reference points representing the user preferences, and two scenarios to simulate different user profiles. COR-NSGA-II is evaluated against four algorithms explored in the literature for the problem, and outperforms most of them according to R-HV and R-IGD. It takes less time to execute and generates a reduced number of solutions, all of them satisfying the user preferences. A qualitative analysis performed with 12 potential users shows that the task of selecting a solution generated by COR-NSGA-II is easier than selecting a solution generated by the other algorithms.

Secure Data Transmission and Trustworthiness Judgement Approaches Against Cyber-Physical Attacks in an Integrated Data-Driven Framework

Threats of cyberattacks have penetrated from disclosing critical user information to destroying/manipulating industrial control systems. Study on data security during network transmission has raised increasing attention in the systems and control community, which is found very necessary and timely in the context of Industry 4.0. In most existing approaches, the protection of the transmitted data from eavesdropping attacks and the detection of malicious integrity attacks are usually carried out separately. In this study, an integrated data-driven framework applicable at the control level is proposed to deal with secure transmission and attack detection simultaneously. In the framework, a secure correlation-based encryption/decryption approach and a trustworthiness judgement approach are proposed. Comprehensive discussions are made regarding the analysis of the sensitivity to attacks, the introduced time delay, and the design degree-of-free. Executable algorithms are presented, corresponding to which hardware is modularized and can work standalone independent from the configuration of the monitoring and control systems or any third-party authentication agencies. Evaluation results on a simulated two-area frequency-load control power grid system are provided to show the effectiveness and performance of the proposed approaches.

Energy management approach in electric vehicle with optimizing electricity consumption cost using hybrid method

This paper proposes a hybrid approach for the optimal design of electric vehicle (EV) home energy management. The proposed hybrid system combines the execution of the Lichtenberg optimization algorithm and the heap-based optimizer; hence, it is named as LAHBO method. The main purpose of the proposed system is the reduction of costs and improvement of the power factor. Thus, two phases of optimization, such as Lichtenberg optimization algorithm–based cost minimization and heap-based optimizer–based power factor improvement. At initial phase, power conversation, and operating time of the smart home components are decided using the Lichtenberg optimization algorithm method. It is categorized into four groups, such as interruptible, uninterruptible, thermostatically controlled, and non-programmable loads. In second phase, the residential power factor at grid connection point is improved using the heap-based optimizer approach. Finally, the proposed system is carried out on MATLAB platform related to several existing approaches. The proposed method enhances the power factor and diminishes the cost than the existing method. The cost of proposed method is 0.16$ and existing approaches such as CGO, SMO, and SOA cost become 0.2, 0.3, and 0.35$, respectively.

Hardware Solutions for Low-Power Smart Edge Computing

The edge computing paradigm for Internet-of-Things brings computing closer to data sources, such as environmental sensors and cameras, using connected smart devices. Over the last few years, research in this area has been both interesting and timely. Typical services like analysis, decision, and control, can be realized by edge computing nodes executing full-fledged algorithms. Traditionally, low-power smart edge devices have been realized using resource-constrained systems executing machine learning (ML) algorithms for identifying objects or features, making decisions, etc. Initially, this paper discusses recent advances in embedded systems that are devoted to energy-efficient ML algorithm execution. A survey of the mainstream embedded computing devices for low-power IoT and edge computing is then presented. Finally, CYSmart is introduced as an innovative smart edge computing system. Two operational use cases are presented to illustrate its power efficiency.

Demand side management as a mandatory inclusion for economic operation of rural and residential microgrid systems

The load requirement of a microgrid system fluctuates on an hourly manner. As the load demand rises and falls, utilities set various rates at different hours of the day, which is known as the time-of-usage (TOU) based electricity pricing. The two types of load that make up the microgrid system's hourly demand are elastic and inelastic. Demand-side management (DSM) restructures the entire demand model based on demand-price elasticity by moving elastic loads from peak load hours to those hours when the utility charges less. This research employs a hybrid intelligence method to reduce the total cost of three microgrid systems taking into account the implementation of DSM. Practical complexities like unit-commitment of the dispatchable fossil fuelled generators were considered. Numerical results project noticeable savings ranging from 8% to 18% for all the test systems when the DSM strategy was incorporated. A detailed comparative analysis with the results available in the literature corroborates the effectiveness of the proposed hybrid approach. Measures of central tendencies, non-parametric statistical analysis and algorithm execution time show better results of the hybrid optimization tool presented in this paper.