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This paper describes a novel steganography algorithm that combines quantum computing with the least significant bit technique (LSB). Using the quantum properties, along with the Python programming language and the Qiskit framework for the circuit simulation, a sub-image can be hidden inside a usual image. For a gray image, this article presents how the LSB of each of the first 16 pixels on the edges can be used to hide two threshold values, that are then used to filter out the image and reveal the secret message. The speedup, compared to the classical version, is possible due to the quantum representation of an image (NEQR is used in this paper) and the efficiency of the novel multi-bit quantum comparators. [ABSTRACT FROM AUTHOR]

In this paper, the comprehensive loss of a drytype transformer under the influence of power quality is objectively and reasonably analyzed. A combined objective weighting method based on Adaptive Average Pooling Enhanced Whale Optimization Algorithm (AEWOA) is proposed. Four objective weighting methods were utilized to calculate the foundational weights, aiming to circumvent the potential impact of subjective factors on the weighting process. The AEWOA incorporates an enhanced pooling mechanism to accelerate convergence towards the optimal population, thereby improving the efficiency of the algorithm in locating the global optimum. Extreme difference maximization is utilized as the objective function to ensure that a significant degree of differentiation is maintained among the linear combination coefficients. As a result, a comprehensive loss calculation model is established under the influence of power quality. The contrast experiment showcases the model's proficiency in lightweighting, emphasizing its superiority over the superimposed loss calculation method. This underscores the model's rationality and efficacy compared to traditional approaches. The model is valuable for calculating and analyzing the thermal load of dry-type transformers. [ABSTRACT FROM AUTHOR]

Video streaming takes the largest share of internet traffic today, and MPEG dynamic adaptive streaming over HTTP (DASH) has become dominant among other video streaming standards and protocols. According to the DASH standard, multimedia content is encoded in different quality levels with different bitrates located on the server, and users can request multimedia content of any available bitrate. The user side determines the desired bitrate in the unit called adaptation bitrate (ABR) logic. Many ABR algorithms have been proposed to improve the quality of experience (QoE). The main criteria for determining QoE are average bitrate, number of switches between resolutions, and number of buffer underflows. This paper presents a hybrid DASH adaptation algorithm that uses the following input values: current buffer occupancy level, network throughput value calculated on the last downloaded DASH segment, and Takagi-Sugeno-Kang model output that represents expected throughput in the next segment download iteration. We compared the proposed algorithm with several other algorithms and the results show that it outperforms others in average bitrate and number of bitrate switches. Furthermore, our algorithm prevented all buffer underflows. [ABSTRACT FROM AUTHOR]

Message forwarding schemes allow vehicles to exchange messages for various purposes such as safety, entertainment and traffic efficiency in vehicular ad hoc networks (VANETs). This paper addresses the problem of message dissemination under emergency situations in the context of vehicle-to-vehicle communication along the roads. The paper proposes a new forwarding scheme for both hazardous and normal traffic messages. The proposed scheme improves the irresponsible forwarding probability in case of both emergency and non-emergency situations. Furthermore, the scheme uses adaptive broadcast range rather than using static one and thus more appropriate for practical applications. In our work Veins-SUMO-OMNeT simulation platform has been used. The simulation results show that the proposed forwarding algorithm outperforms the other dissemination techniques in terms of message losses and data dissemination success for normal messages and emergency messages. [ABSTRACT FROM AUTHOR]

Auctions are often recommended as effective cloud resource pricing and allocation mechanism. If adequately set, auctions provide incentives for cloud users' truthful bidding and support cloud provider's revenue maximization. In such a cloud system, resources are offered via an auction mechanism as Virtual Machines (VMs). Due to the virtualization of the cloud system, VMs' security becomes a critical factor. However, security requirements are often in contrast with performance requirements since the operation of security mechanism inevitably consumes a certain amount of Central Processing Time (CPU) and memory. Thus, delays and energy consumption increase. In this paper, we propose a novel simulation model based on a truthful auction mechanism to address revenues, security, and energy consumption in a cloud system. The VMs security modeling is introduced to assess the security level of VMs. A Vickrey-Clarke-Groves (VCG) driven algorithm is established for winner determination. The proposed simulation model is used to observe cloud provider's revenues, lost revenues, cloud users' task rejection rate and energy consumption depending on the offered security level. This model supports decision making in terms of investments in security and selection of security scenario that maximizes revenues and minimizes task rejection rate and energy consumption. [ABSTRACT FROM AUTHOR]

This paper presents studies of the four maximum power point tracking (MPPT) algorithms of a single-phase grid-connected photovoltaic (PV) inverter based on single loop voltage control (VC) and cascaded voltage and current control (CVCC). The purpose of this research was to evaluate four techniques for maximum power tracking control: (1) the Perturbation and Observation (P&O) MPPT&VC, (2) the Incremental Conductance (InC) MPPT&VC, (3) the P&O MPPT&CVCC and (4) the InC MPPT&CVCC. The MPPT efficiency values of each technique were affected by sudden changes in various irradiances. The CVCC was found to reduce the oscillating PV power and have a faster response time of MPPT from the PV array than the VC configuration. The InC MPPT&CVCC technique can track maximum power when irradiance changes rapidly. The simulation results showed that the InC MPPT&CVCC technique can decrease oscillation at a steady state around the set point and has a fast response time of MPPT, reduced electrical power ripple, and less power loss. The average efficiency of MPPT was 99.98%. [ABSTRACT FROM AUTHOR]

In order to improve the performance of traditional particle swarm optimization, this paper introduces the principle of Levy flight and cross-border reset mechanism. In the proposed particle swarm optimization, the dynamic variation of parameters meets the power-law distribution and the pattern of particles transition conforms to the Lévy flight in the process of algorithm optimization. It means the particles make long distance movements in the search space with a small probability and make short distance movements with a large probability. Therefore, the particles can jump out of local optimum more easily and coordinate the global search and local search of particle swarm optimization. This paper also designs the cross-border reset mechanism to make particles regain optimization ability when stranding on the border of search space after a long distance movement. The simulation results demonstrate the proposed algorithms are easier to jump out of local optimum and have higher accuracy when compared with the existing similar algorithms based on benchmark test functions and handwriting character recognition system. [ABSTRACT FROM AUTHOR]

The potential for the usage of energy exponents in motion detection from video sequences is explored. The wavelet domain was chosen for the research due to the optimality of Hilbert's space for energy calculations and Parseval's equation for energy equivalence between domains. Five algorithms were considered: wavelet energy motion detection algorithm based on wavelet pairs and buffer, listed in the references, and four which are the contributions of this paper: modification by the application of different wavelet pairs, a modified algorithm without buffer, a modified algorithm without buffer and pairs, newly developed algorithms for energy exponents with and without buffer, but with wavelet pairs. The considered algorithms are background subtraction algorithms modified not to use pixels values, but rather energy/energy exponent backgrounds and the current situation models. These models are described by wavelet descriptors, the introduction of which is the contribution of this paper. They are compared by standard statistical criteria and execution time. The results suggest that an increase in the energy exponent decreases precision, recall and F-measure. However, the percentage of correct classifications remains almost constant. Higher exponentials reduce noise, but are more susceptible to shadows, the waving tree effect and similar abnormalities. Algorithms without buffers are less robust to illumination changes. [ABSTRACT FROM AUTHOR]

The goal of this paper is to present an implementation method for a Maximum Power Point Tracking (MPPT) algorithm using an electronic load and custom designed LabView software. The aim is to facilitate the testing of the algorithm in laboratory conditions, before it can be used in the real world, improving development time, facilitating cost reduction and offering confidence in the design. This paper analyses the most suitable MPPT algorithms for testing purposes and suggests a complete software and hardware implementation for hardware in the loop testing which can facilitate in-depth evaluation of different algorithms. In order to replicate realistic stimuli for the MPPT algorithm, a solar array simulator has been designed. Using the proposed method, the performance of various MPPT algorithms for different atmospheric conditions can be evaluated. The hardware and software setup have been tested and validated in laboratory conditions. The experimental results have validated the proposed evaluation method and the good dynamic response of the MPPT algorithm. [ABSTRACT FROM AUTHOR]

An adaptive three-level delta modulation with a switched second-order linear prediction is proposed in this paper, intended for encoding the time-varying signals modeled by Gaussian distribution. The input signal is processed frame-by-frame, and the adaptation of the quantizer is performed at the frame level. The signal at the output of quantizer is further processed using variable length encoder to decrease the bit rate. The performance is tested in speech coding, showing that the proposed algorithm provides much wider dynamic range and attains higher Signal to Noise Ratio with respect to the baselines, including CFDM, CVSDM and 2-bit Adaptive Delta Modulation. [ABSTRACT FROM AUTHOR]

A continuous development of technology involves a permanent improvement of appliances towards the increase in energy effectiveness, efficiency and precision of operation. This applies also to induction heating equipment. In order to increase the energy effectiveness of the inverter presented in this paper and limit its switching losses, its operating point has been optimized, which required solving a system of two nonlinear equations. Due to the complex surface optimization to determine the optimal operating point, a modified firefly algorithm was used, which belongs to the family of intelligent optimization methods. An analysis of the effectiveness of optimization process was carried out, due to the firefly algorithm parameters. A modification of the firefly algorithm was proposed to speed up the optimization and get certainty of finding the global optimum. Theoretical outcomes were compared with the measured experimental results obtained in a real inverter system. [ABSTRACT FROM AUTHOR]

This paper introduces an algorithm based on switched scalar quantization utilizing a novel µ-law quantization model (optimized in terms of minimal distortion) and variable length codewords, for high-quality encoding of the signals modeled by Gaussian distribution. The implemented µ-law quantizer represents an improvement of the standard µ-law quantizer in terms of bit rate, at the same time providing the equal signal quality. The main concept of the algorithm is to divide the range of the input signal variances into a certain number of sub-ranges, and to design the optimal quantizer for each sub-range. The signal is processed frame-by-frame, and for each frame the best performing quantizer is chosen, where the estimated frame variance is used as the switching criterion. Theoretical results indicate that the proposed algorithm achieves performance comparable to the standard µ-law quantizer, enabling the compression of about 0.5 bit/sample. The simulation results are provided to confirm the correctness of the proposed model. [ABSTRACT FROM AUTHOR]

Shannon energy-based algorithm has been implemented in peak detection method of various physiological signals including electrocardiogram, which is used to enhance significant peaks for accurate peak detection. Two significant methods of R-peak detection that apply Shannon energy are identified. However, direct comparison cannot be made due to the differences in database used, number of beat analysed, frequency range selected, and signal processing technique applied. This paper aimed to properly evaluate the performance of Shannon energy-based algorithms for R-peak detection on two methods of bandpass filter and Stockwell transform. Simple enveloping technique using moving average filter is proposed, and a threshold is set to localize R-peak at a selected frequency range of 7-15 Hz. Performance of both methods were then evaluated using all 48 data from MIT-BIH Arrhythmia database. Result showed that both methods are equivalently useful in reducing P and T waves interference and produced similar output of Shannon energy envelope. However, Shannon energy application on bandpass filter offered 99.71% sensitivity, 99.80% positive predictivity and 99.52% accuracy, slightly better than that of the Stockwell transform method that only produced 99.65% sensitivity, 99.68% positive predictivity and 99.33% accuracy. [ABSTRACT FROM AUTHOR]

Phasor measurement units (PMUs) play an important role in many applications of power systems. In order to ensure a reliable performance, the phasor estimation algorithm has to satisfy a set of requirements stated in the IEEE Standard C37.118.1, which establishes the test conditions and requirements for steady-state and dynamic conditions. There, two classes of performance, P and M, can be found. In general, P-class is intended for applications that require fast response and M-class is used when greater precision is necessary. In this paper, a novel algorithm based on Hilbert transform for phasor estimation in compliance with the IEEE standard C37.118.1 for P-class is proposed. Advantages of the proposal are a fast response and a low computational burden due to the HT implementation as a low-order filter of one cycle. Further, two low-complex strategies of compensation are proposed. The proposal is validated using all the test conditions specified in the IEEE Standard C37.118.1. Besides that, real voltage and current signals of an electrical system are analyzed. The obtained results demonstrate that the new proposal can meet all the requirements for P-class performance. [ABSTRACT FROM AUTHOR]

Many imaging systems are faced with the problem of estimating a true image from a degraded dataset. In such systems, the image degradation is translated into a convolution with a Point Spread Function (PSF) and addition of noise. Often, the image recovery by inverse filtering is not possible because the PSF matrix is ill-conditioned. Maximum Entropy (MaxEnt) is an alternative method, which uses the entropy concept for estimating the true image. This paper presents MaxEnt method, starting with the historical references of the entropy concept and finalizing with its application in image restoration and reconstruction. The statistical model of MaxEnt for images is discussed and the connection of MaxEnt with the Bayesian inference is explained. MaxEnt is evaluated by using a modified version of Cornwell algorithm. Two cases are considered: images degraded by various PSF kernels in presence of additive noise and images resulted from incomplete datasets. The tests show PSNR gains ranging from 1 to 7dB for the degraded images and images reconstructed at 25dB from datasets with up to 80% missing pixels. [ABSTRACT FROM AUTHOR]

Automatic 3D reconstruction of indoor scenes remains a challenging task due to the incomplete and noisy nature of scanned data. We propose a semantic-guided method for reconstructing indoor scene based on semantic segmentation of point clouds. Firstly, a Multi-Feature Adaptive Aggregation Network is designed for semantic segmentation, assigning the semantic label for each point. Then, a novel slicing-projection method is proposed to segment and reconstruct the walls. Next, a hierarchical Euclidean Clustering is proposed to separate objects into individual ones. Finally, each object is replaced with the most similar CAD model from the database, utilizing the Rotational Projection Statistics (RoPS) descriptor and the iterative closest point (ICP) algorithm. The selected template models are then deformed and transformed to fit the objects in the scene. Experimental results demonstrate that the proposed method achieves highquality reconstruction even when faced with defective scanned point clouds. [ABSTRACT FROM AUTHOR]

This paper presents the analysis and multiobjective optimization of a magnetic actuator. By varying just 8 parameters of the magnetic actuator';s model the design space grows to more than 6 million configurations. Much more, the 8 objectives that must be optimized are conflicting and generate a huge objectives space, too. To cope with this complexity, we use advanced heuristic methods for Automatic Design Space Exploration. FADSE tool is one Automatic Design Space Exploration framework including different state of the art multi-objective meta-heuristics for solving NP-hard problems, which we used for the analysis and optimization of the COMSOL and MATLAB model of the magnetic actuator. We show that using a state of the art genetic multi-objective algorithm, response surface modelling methods and some machine learning techniques, the timing complexity of the design space exploration can be reduced, while still taking into consideration objective constraints so that various Pareto optimal configurations can be found. Using our developed approach, we were able to decrease the simulation time by at least a factor of 10, compared to a run that does all the simulations, while keeping prediction errors to around 1%. [ABSTRACT FROM AUTHOR]

The volume and complexity of the data that is generated every day increased in the last years in an exponential manner. For processing the generated data in a quicker way the hardware capabilities evolved and new versions of algorithms were created recently, but the existing algorithms were improved and even optimized as well. This paper presents an improved clustering approach, based on the classical k-means algorithm, and referred to as the centroid update approach. The new centroid update approach formulated as an algorithm and included in the k-means algorithm reduces the number of iterations that are needed to perform a clustering process, leading to an alleviation of the time needed for processing a dataset. [ABSTRACT FROM AUTHOR]

Real-time systems (RTS) are characterized by tasks executing in a timely manner to meet its deadlines as a real-time constraint. Most studies of RTS have focused on these criteria as primary design points. However, recent increases in security threats to various real-time systems have shown that enhanced security support must be included as an important design point, retro-fitting such support to existing systems as necessary. In this paper, we propose a new pre-flush technique referred to as flush task reservation for FP scheduling (FTRFP) to conditionally sanitize the state of resources shared by real-time tasks by invoking a flush task (FT) in order to mitigate information leakage/corruption of real-time systems. FTR-FP extends existing works exploiting FTs to be applicable more general scheduling algorithms and security model. We also propose modifications to existing real-time scheduling algorithms to implement a pre-flush technique as a security constraint, and analysis technique to verify schedulability of the real-time scheduling. For better analytic capability, our analysis technique provides a count of the precise number of preemptions that a task experiences offline. Our evaluation results demonstrate that our proposed schedulability analysis improves the performance of existing scheduling algorithms in terms of schedulability and preemption cost. [ABSTRACT FROM AUTHOR]

Block matching methods are one of the most widely used methods in motion estimation and compensation. In this work, we propose a new hybrid block matching motion estimation algorithm based on the Lucas and Kanade method as a distortion criterion to improve the accuracy of estimated motion. The proposed algorithm proceeds in three steps. In the first step, a small hexagonal pattern is used, in order to find the smaller motion vectors and thus fewer searching points. In the second step, the modified large hexagonal pattern is used to identify the direction of motion vectors. In the third step, the small hexagonal search pattern is used to refine the solution search. The proposed algorithm is tested on several both synthetic and real images sequences. The experimental results show that our proposal could achieve good performances in terms of amplitude and angular errors, prediction quality, and computational complexity, compared to some related works. [ABSTRACT FROM AUTHOR]

In recent decades, signal reconstruction schemes play an important role in rebuilding the signal again from incomplete information about the original one. In this article, we consider a novel technique for transmitting a signal from its bandpass spectral information. A new recovery algorithm is also used to reconstruct most of baseband signal's energy despite receiving a window of its spectrum. This algorithm is tested for many types of signals with different data rates. It is also applied to a human speech signal and showed a good reconstruction status. The performance of the algorithm is evaluated in terms of the normalized mean square error (NMSE) in noiseless and noisy channels. The proposed technique shows its capability to send any signal over a bandlimited channel and reconstruct it again without any need to send most of its spectral power compared to the conventional schemes, thus more bandwidths can be saved and the bandwidth usage can be optimized. However, many problems have been introduced and resolved during the recovery process. The algorithm showed an acceptable recovery status when tested in noisy channels. The proposed scheme may find many applications in high data-rate communications, pulsed radar systems, next generation networking, and OFDM-based wireless communication. [ABSTRACT FROM AUTHOR]

In this article, a droop control algorithm is proposed for power distribution between sources in an island inverter-based microgrid without common central control. The analyses of the line impedance influence (resistive, inductive, and combined) are provided to show the impact on the power flow between a grid and a converter. Line impedance character influences the droop control algorithm performance. The impact of the accuracy of the setting, concerning line impedance, of the proposed droop control algorithm is then tested in a simulation. For the simulation, a microgrid model was built in the Simulink environment (MATLAB) with the PLECS toolbox. In the conclusion, the potential benefits and applications of the proposed droop control are discussed. [ABSTRACT FROM AUTHOR]

Cardiac anomalies are usually marked through irregular cardiac cycles. Atrial fibrillation is given through a rapid beating of the atria, announcing a possible heart failure or stroke. Electrocardiograms are an efficient way of supervising the electric activity of the heart. We have developed an effective, simple to implement automatic detection algorithm for identifying changes of the cardiac rhythm. The algorithm is based on wavelets and an enhanced time domain thresholding procedure. We take into account a variation of the electrocardiogram's amplitudes, to avoid loss of clinical features. The interval between beats is computed and provided for a reliable diagnosis. The results are validated both with objective evaluation criteria and displayed graphically, assisting the medical diagnosis procedure. [ABSTRACT FROM AUTHOR]

This work proposes and evaluates a methodology for monitoring and diagnosis of polymeric insulators in operation based on the parameterization of acoustic emissions (AE) created by corona and electrical surface discharges. The parameterization was performed with the use of the spectral subband centroid energy vectors (SSCEV) algorithm, which compresses the frequency spectrum and presents the results of the AE energies in several frequency bands. Thus, it was possible to calculate the dominant acoustic emission frequencies. This parameter was used as reference for an operating point of the insulators and, therefore, it was used to classify them. This classification was correlated to the classification obtained by visual inspection in the laboratory, where the insulators were divided into three distinct classes: clean, polluted and damaged. Aiming to insert an aid to the decision-making, this work still proposes the use of artificial neural networks (ANN) for pattern recognition. In this way, we performed a sensitivity analysis of the parameters that influence the SSCEV and ANN, in order to obtain the values and configurations with higher performance. The use of Levenberg-Marquardt training algorithm has proved to be more suitable, since it showed hit rates and convergence up to 97.66 percent and 70 epochs, respectively. [ABSTRACT FROM AUTHOR]

Image steganography is a technique to hide secret information in an image without leaving any apparent evidence of image alteration. Hiding capacity, perceptual transparency, robustness, and resistance against attack must be considered as characteristics of the image steganography algorithms. In this study, Improved Chaos Based Bit Embedding has been proposed as a new steganography algorithm. It is based on two basic principles. One of them is determining the bits in which the secret data will be embedded by logistic map and the other one is embedding the secret data into only one of the three color channels that is chosen randomly. It distorts the other remaining channels so that it is harder to obtain the text within the image by an unwanted person. The proposed algorithm has been tested on 10 sample images along with the four basic steganography algorithms: Least Significant Bit Embedding, Pseudo Random Least Significant Bit Embedding, EzStego, and F5. It has been seen that generating unpredictable indexes by the chaotic random number generators, and embedding the secret data into only one of the three channels (distorting remaining channels) increased resistance against attacks. Perceptual transparencies and capacity ratio of the proposed algorithm are compatible with the other four algorithms. [ABSTRACT FROM AUTHOR]

Performance measurement and evaluation study of calculations based on load flow analysis in power distribution network is presented. The focus is on the choice of load index as it is the basic input for efficient dynamic load balancing. The basic description of problem along with the proposed architecture is given. Different server resources are inspected and analyzed while running calculations, and based on this investigation, recommendations regarding the choice of load index are made. Short description of used static and dynamic load balancing algorithms is given and the proposition of load index choice is supported by tests run on large realworld power distribution network models. [ABSTRACT FROM AUTHOR]