Conventional Least Research Articles

Optimizing Digital Image Steganography through Hybridization of LSB and Zstandard Compression

In response to the growing need for secure digital communication globally, this research delves into an innovative strategy for enhancing data transmission security through steganography. This inventive approach involves the integration of the conventional Least Significant Bit (LSB) method with Zstandard (Zstd) compression to elevate the quality of stego images. The study carefully explores how the synergistic use of LSB and Zstd contributes to an improved equilibrium between embedding capacity and visual quality in stego images. This hybrid methodology capitalizes on the efficiency of Zstd in reducing file size, thereby facilitating more effective data concealment using LSB. The experimental outcomes showcase a notable 51.2% increase in embedding capacity, a 4.70% elevation in PSNR value, accompanied by a substantial 51.03% decrease in MSE value. Additionally, SSIM values hover around 0.007%, indicating a perceptually minimal difference between the original and steganographically modified images. These compelling results underscore the efficacy of the proposed method, highlighting its proficiency in preserving and enhancing the quality of stego images generated through the embedding process. This research signifies a significant stride in the realm of secure digital communication, demonstrating a promising fusion of traditional LSB with advanced Zstd compression for optimizing digital image steganographic.

Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm.

This study leverages advanced data mining and machine learning techniques to delve deeper into the impact of sports activities on physical health and provide a scientific foundation for informed sports selection and health promotion. Guided by the Elastic Net algorithm, a sports performance assessment model is meticulously constructed. In contrast to the conventional Least Absolute Shrinkage and Selection Operator (Lasso) algorithm, this model seeks to elucidate the factors influencing physical health indicators due to sports activities. Additionally, the incorporation of the Random Forest algorithm facilitates a comprehensive evaluation of sports performance across distinct dimensions: wrestling-type sports, soccer-type sports, skill-based sports, and school physical education. Employing the Top-K criterion for evaluation and juxtaposing it with the high-performance Support Vector Machine (SVM) algorithm, the accuracy is scrutinized under three distinct criteria: Top-3, Top-5, and Top-10. The pivotal innovation of this study resides in the amalgamation of the Elastic Net and Random Forest algorithms, permitting a holistic contemplation of the influencing factors of diverse sports activities on physical health indicators. Through this integrated methodology, the research achieves a more precise assessment of the effects of sports activities, unveiling a range of impacts various sports have on physical health. Consequently, a more refined assessment tool for sports performance detection and health development is established. Capitalizing on the Elastic Net algorithm, this research optimizes model construction during the pivotal feature selection phase, effectively capturing the crucial influencing factors associated with different sports activities. Concurrently, the integration of the Random Forest algorithm augments the predictive prowess of the model, enabling the sports performance assessment model to comprehensively unveil the extent of impact stemming from various sports activities. This study stands as a noteworthy contribution to the arena of sports performance assessment, offering substantial insights and advancements to both sports health and research methodologies.

Development of a Non-Iterative Macromodeling Technique by Data Integration and Least Square Method

In this paper, a new method is introduced to synthesize the original data obtained from simulation or measurement results in the form of a rational function. The integration of the available data is vital to the performance of the proposed method. The values of poles and residues of the rational model are determined by solving the system of linear equations using the conventional Least Square Method (LSM). To ensure the stability condition of the provided model, a controller coefficient is considered. Also, using this parameter, the designer can increase the stability margin of a system with poor stability conditions. The introduced method has the potential to be used for a wide range of practical applications since there is no specific restriction on the use of this method. The only requirement that should be considered is the Dirichlet condition for the original data, usually the case for physical systems. To verify the performances of the proposed method, several application test cases are investigated and the obtained results are compared with those gathered by the well-known vector fitting algorithm. Also, the examinations show that the method is efficient in the presence of noisy data.

Multilevel Inverter Based Power Quality Enhancement Using Improved Immune Control Algorithm

ABSTRACT Multilevel inverters are currently the subject of extensive investigation and are used primarily for medium and high voltage distribution systems. The work presented in this paper provides a cost-effective and realistic solution to mitigate power quality (PQ) problems. A Cascaded H-Bridge multilevel inverter (CHB-MLI) has been configured as a shunt active power filter (SAPF). It is well reported that large-scale use of power electronics-based devices leads to several PQ problems such as poor power factor, unregulated dc voltage, harmonics, voltage regulation etc. In this paper, an improved immune feedback control algorithm is proposed and developed to mitigate PQ issues by predicting the weighted active component of load current and to generate reference current for the grid. Extensive MATLAB simulation and experimental work have been reported for validation on a single-phase system. A low prototype model is developed in the laboratory to study and compare the steady-state and dynamic results of the proposed controller with conventional Least Mean Square (LMS) and Immune algorithm.

Performance Evaluation of OFDM System using Pseudo-Pilots with Particle Swarm & Moth Flame (Hybrid) Optimization

This paper is focused on the advanced signal processing techniques for the multi-carrier modulation especially on the orthogonal frequency division multiplexing (OFDM). OFDM has high data rate and robust against the frequency selective multi-path channels. Channel estimation is crucial for the receiver design in coherent detection. In this paper we are investigating and examining the pilot aided system for channel estimation and its special effects on the performance of the OFDM based system. In this paper new technique is proposed which make use of conventional Least Square (LS) and Recursive Least Square (RLS) hybridization techniques for the channel estimation afterwards we apply Particle Swarm Optimization (PSO) and Hybrid PSO (PSO+ Moth Flame Optimization(MFO)) optimization for obtaining more optimize techniques. In the proposed system, to estimate channel impulse response we are using pseudo-pilots instead to pilots as it is useful to overcome the pilot overhead and decrease the complexity by using pseudo random symbol (PRS). The performance of the proposed techniques and the weighted scheme are compared and verified using computerized simulation carried out using Matrix Laboratory (MATLAB) software. It is demonstrated on the basis of graph between BER and signal to Noise Ratio (Eb/NO), that the proposed technique reduces the execution time and increase the transmission rate with low or zero overhead.

Generalized predictive recursion maximum likelihood for robust mixture regression

In the application of econometric model, the error distribution is unknown and is not easily to specify in the likelihood function. In some situations, there might exist a mixture distribution in the errors and thus the traditional estimation method would probably yield a biased result. In this study, this mixture distribution of the error term is taken into account and the generalized semiparametric estimation is presented and applied in regression model. We also use an experiment study and the real application analysis to check the performance of this estimator in regression model. The performance of this estimation is then compared with that of conventional Least Squares method in the real data analysis.

ILMSAF based speech enhancement with DNN and noise classification

In order to improve the performance of speech enhancement algorithm in low Signal-to-Noise Ratio (SNR) complex noise environments, a novel Improved Least Mean Square Adaptive Filtering (ILMSAF) based speech enhancement algorithm with Deep Neural Network (DNN) and noise classification is proposed. An adaptive coefficient of filter's parameters is introduced into conventional Least Mean Square Adaptive Filtering (LMSAF). First, the adaptive coefficient of filter's parameters is estimated by Deep Belief Network (DBN). Then, the enhanced speech is obtained by ILMSAF. In addition, in order to make the presented approach suitable for various kinds of noise environments, a new noise classification method based on DNN is presented. According to the result of noise classification, the corresponding ILMSAF model is selected in the enhancement process. The performance test results under ITU-TG.160 show that, the performance of the proposed algorithm tends to achieve significant improvements in terms of various speech subjective and objective quality measures than the wiener filtering based speech enhancement approach with Weighted Denoising Auto-encoder and noise classification.

Comparative evaluation among various robust estimation methods in deformation analysis

There is different robust estimation methods used for detecting small gross errors which may be presence in geodetic observations. Some of these methods are robust M-estimators, Least Absolute Sum and Danish method. The conventional Least Squares Estimation method and these robust estimation methods have been tested and applied on a precise geodetic network designed for detecting earth’s crustal deformations using single-point and rigid body displacement models. The results show that, the method of least squares leads to biased, unfavorable solution and useless estimates if the single-point displacement does not taken into considerations in the solution’s mathematical model. In contrast, the results of robust estimation methods are attained unbiased in a single estimation step. A reliable separation of single-point displacement and the general deformation model is achieved without difficulties. The comparison of results proved that, the Danish method gave the most accurate results. It also was more sensitive to outliers which may be presence in surveying observations in case of applying single-point displacement model. The Least Absolute Sum method was very nicely reproduces the simulated deformations and does not show a contamination of the estimated positions of stable points in the case of rigid body displacement model. The results of adjustment using rigid body displacement model were not identical with the nearly correct results of single-point displacement model. So, the rigid body displacement model may be not suitable for geodetic networks.

A Pioneering Perusal of Mimo- OFDM Realy Guesstimate Disposition

A multiple-input multiple-output (MIMO) communication system combined with the orthogonal frequency partition multiplexing (OFDM) modulation technique can achieve reliable high data rate show over broadband wireless channels. Channel state information for both single-input single-output (SISO) and MIMO systems based on pilot aided arrangement is investigated in this paper. The estimate of channel at pilot frequencies with conventional Least Square (LS) and Minimum Mean Square (MMSE) estimation algorithms is carried out through Matlab simulation. For outdoor communicationscenarios, where wireless channels are sparse in nature, pathinterruptions of different transmit-receive antenna couples share a jointsparse decoration due to the spatial correlation of MIMO channels.By simultaneously exploiting those MIMOchannel characteristics, the proposed scheme performs better thanpresent state-of-the-art systems. Furthermore, by joint processingof signals associated with different antennas, the pilot overheadcan be reduced the performance of MIMO OFDM and SISO OFDM are assessed on the basis of Bit Error Rate (BER) and Mean Square Error (MSE) level. Further enhancement of presentation can be attained through maximum diversity Space Time Block Coding and Maximum Likelihood Detection at transmission and reception ends respectively. MMSE estimation has been shown to perform much better than LS but is more complex than LS for the MIMO system using pilot carriers.

A novel algorithm for adaptive NLMS beamformer

This paper proposes a novel algorithm based on Mapped Real Transform for improving the convergence rate and reducing the computational complexity of Normalised Least Mean Square beamformer. Simulation results using the proposed One Dimensional Reduced Mapped Real Transform algorithm project an improvement over the conventional Normalised Least Mean Square beamformer by exhibiting a reduction in computational complexity and computation time with the added advantage of faster convergence. The proposed One Dimensional Reduced Mapped Real Transform algorithm based Normalised Least Mean Square beamformer also exhibits an improved performance in terms of faster convergence and reduced scattering when used with Rayleigh and Rician channels.

The Output Filter Identification of Three-Phase PWM Converter Using Weighted Least Square Method

This paper proposed the Weighted Least Square method (WLS method) to identify the output filter of three-phase PWM converter, which incorporates the signal processing as well as mathematical techniques into conventional Least Square method. It sets different weights to different measurements according to the phase where it locates, based on the discovery of the correlation between accuracy and phase of current. The algorithm is tested in both simulation and experimental environment, and the results validate that proposed method gives accurate estimation in steady state, and can response within 10ms in when grid voltage drops. This method can work under both balanced and unbalanced operating conditions, therefore provides a powerful tool for various control strategies to better understand the operating conditions. Compared with the invasive method, which intentionally inject a series of white noise in the system, proposed WLS method does not bring any turbulence, while compared with conventional Least Square method, it possesses better stability as well as higher accuracy.

Support Vector Machines Regression for Channel Estimation in MIMO LTE systems

This paper proposes an efficient scheme to track the time variant channel induced by multipath fading wireless Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system in mobility environment with the presence of Gaussian noise. The estimation of the time varying multipath fading channel is performed by using a nonlinear channel estimator based on a complex Multiple Support Vector Machines Regression (M-SVR) which is developed and applied to MIMO Long Term Evolution (LTE) Downlink with Alamouti coding. The obtained results confirm the effectiveness of the proposed technique to track the fading channel compared to the conventional Least Squares (LS), Minimum Mean Squares Error (MMSE) and Decision Feedback methods.

Write-Aware Replacement Policies for PCM-Based Systems

The gap between processor and memory speeds is one of the greatest challenges that current designers face in order to develop more powerful computer systems. In addition, the scalability of the Dynamic Random Access Memory (DRAM) technology is very limited nowadays, leading one to consider new memory technologies as candidates for the replacement of conventional DRAM. Phase-Change Memory (PCM) is currently postulated as the prime contender due to its higher scalability and lower leakage. However, compared with DRAM, PCM also exhibits some drawbacks, like lower endurance or higher dynamic energy consumption and write latency, that need to be mitigated before it can be used as the main memory technology for the next generation of computers. This work addresses the PCM endurance constraint. For this purpose, we present an analysis of conventional cache replacement policies in terms of the amount of writebacks to main memory that they imply and we also propose some new replacement algorithms for the last-level cache (LLC) with the goal of cutting down the write traffic to memory and consequently, to increase PCM lifetime without degrading system performance. In this paper, we target general purpose processors provided with this kind of non-volatile main memory and we exhaustively evaluate our proposed policies in both single- and multi-core environments. Experimental results show that, on average, compared with a conventional Least Recently Used (LRU) algorithm, some of our proposals manage to reduce the amount of writes to main memory up to 20–30% depending on the scenario evaluated, which leads to memory endurance extensions of up to 20–45%, also reducing the energy consumption in the memory hierarchy by up to 9% and hardly degrading performance.

PERFORMANCE ANALYSIS OF DIGITAL BEAMFORMING ALGORITHMS

Interference reduction is needed for being able to effectively communicate with mobile users. One method that can be used as a solution to reduce interference is using smart antenna technology. Along with interference reduction there is increase in capacity and coverage. Adaptive beam forming techniques forms a narrow beam and directs it towards desired users and thereby forms nulls in interference directions This improves signal to noise ratio. The study documented in this paper shows the performance of variants of the Least Mean Square (LMS) Algorithm based approaches, in the realm of beam formation, used earlier in adaptive filtering, aiming at reduction in complexity, without considerable degradation in performance. The proposed algorithms namely Signum- Error Least Mean Square Algorithm along with LMS Algorithm are tested for computational complexity in weight vector updating and observed that the ones proposed perform better than the conventional Least Mean Square algorithm with respect to reduced complexity in beam formation, making them suitable for high speed digital communication systems. This paper will analyze Least Mean square algorithm and Sample Matrix Inverse algorithm for the performance. The consequence will demonstrate the Least Mean Square as a Solution to jammer cancellation when compared to Sample Matrix Inverse.

Identification of noisy dynamical systems with parameter estimation based on Hopfield neural networks

An algorithm for estimating time-varying parameters of dynamical systems is proposed, within the large family of prediction error methods. The algorithm is based on the ability of Hopfield neural networks to solve optimisation problems, since its formulation can be summarized as minimisation of the prediction error by means of a continuous Hopfield network. In previous work, it was proved, under mild assumptions, that the estimates converge towards the actual values of parameters and the estimation error remains asymptotically bounded in the presence of measurement noise. The novelty of this work is the advance in the robustness analysis, by considering deterministic disturbances, which do not fulfil the usual statistical hypothesis such as normality and uncorrelatedness. A model of HIV epidemics in Cuba is used as suitable benchmark, which is confirmed by the computation of the sensitivity matrix. The results show a promising performance, in comparison to the conventional Least Squares Estimator. Indeed, the estimation error is almost always lower in the proposed method that in least squares, and it is never significantly higher. Further, from a qualitative point of view, the estimate provided by the Hopfield estimator is smoother, with no overshoot that could eventually destabilize a closed control loop. A significant finding is the fact that the form of the perturbation affects critically the dynamical behaviour and magnitude of the estimation, since the estimation error asymptotically vanishes when the disturbances are additive, but not when they are multiplicative. To summarize, we can conclude that the proposed estimator is an efficient and robust method to estimate time-varying parameters of dynamical systems.

Estimation of Non-WSSUS Channel for OFDM Systems in High Speed Railway Environment Using Compressive Sensing

Non Wide Sense Stationary Uncorrelated Scattering (Non-WSSUS) is one of characteristics for high-speed railway wireless channels. In this paper, estimation of Non-WSSUS Channel for OFDM Systems is considered by using Compressive Sensing (CS) method. Given sufficiently wide transmission bandwidth, wireless channels encountered here tend to exhibit a sparse multipath structure. Then a sparse Non-WSSUS channel estimation approach is proposed based on the delay-Doppler-spread function representation of the channel. This approach includes two steps. First, the delay-Doppler-spread function is estimated by the Compressive Sensing (CS) method utilizing the delay-Doppler basis. Then, the channel is tracked by a reduced order Kalman filter in the sparse delay-Doppler domain, and then estimated sequentially. Simulation results under LTE-R standard demonstrate that the proposed algorithm significantly improves the performance of channel estimation, comparing with the conventional Least Square (LS) and regular CS methods.

Automated Classification of Dementia Using PSO based Least Square Support Vector Machine

Machine learning techniques are widely used now for neuro-imaging based diagnosis. These methods yield fully automated clinical decisions, unbiased by variable radiological expertise. This research paper compares and evaluates the performance and reliability of conventional Least Square Support Vector Machine (LSSVM) with that of Particle Swarm Optimization (PSO) based LSSVM in the diagnosis of dementia. The manual interpretation of large volume of brain MRI and cognitive measures may lead to incomplete diagnosis. The PSO-LSSVM approach is trained with multiple biomarkers to facilitate effective, accurate classification which is a requirement of the hour. Wavelet based texture features and multiple biomarkers are fed as input to the classifier. PSO-LSSVM yields 98% accurate results and outperforms LSSVM classifier in terms of sensitivity, specificity and accuracy in this analysis

Complex Support Vector Machine Regression for Robust Channel Estimation in Lte Downlink System

In this paper, the problem of channel estimation for LTE Downlink system in the environment of high mobility presenting non-Gaussian impulse noise interfering with reference signals is faced. The estimation of the frequency selective time varying multipath fading channel is performed by using a channel estimator based on a nonlinear complex Support Vector Machine Regression (SVR) which is applied to Long Term Evolution (LTE) downlink. The estimation algorithm makes use of the pilot signals to estimate the total frequency response of the highly selective fading multipath channel. Thus, the algorithm maps trained data into a high dimensional feature space and uses the structural risk minimization principle to carry out the regression estimation for the frequency response function of the fading channel. The obtained results show the effectiveness of the proposed method which has better performance than the conventional Least Squares (LS) and Decision Feedback methods to track the variations of the fading multipath channel.

Adaptive Noise Cancellation Method for Fiber Optic Gyroscope

A variable step size LMS algorithm for signal processing is introduced to solve the random drift problem of Fiber Optic Gyroscope (FOG)’s output signal. The algorithm shows a significant effect in noise elimination. Two improvements are carried out based on the conventional Least Mean Square Algorithm (LMS): on the one hand, normalized input signal power is introduced to improve filtering accuracy and stability; on the other hand, real-time sign function of error is used to simplify calculation and improve the real-time property. The step size of the LMS algorithm can be determined by convergence analysis. In this paper, we use this algorithm in our simulation and the results are compared with the outcome of conventional LMS.

MIMO-OFDM Channel Estimation Using Pilot Carries

A multiple-input multiple-output (MIMO) communication system combined with the orthogonal frequency division multiplexing (OFDM) modulation technique can achieve reliable high data rate transmission over broadband wireless channels. Channel state information for both single-input single-output (SISO) and MIMO systems based on pilot aided arrangement is investigated in this paper. The estimation of channel at pilot frequencies with conventional Least Square (LS) and Minimum Mean Square (MMSE) estimation algorithms is carried out through Matlab simulation. The performance of MIMO OFDM and SISO OFDM are evaluated on the basis of Bit Error Rate (BER) and Mean Square Error (MSE) level. Further enhancement of performance can be achieved through maximum diversity Space Time Block Coding (STBC) and Maximum Likelihood Detection at transmission and reception ends respectively. MMSE estimation has been shown to perform much better than LS but is more complex than LS for the MIMO system using pilot carriers.