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Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

Speckle noise removal in ultrasound images is one of the important tasks in biomedical-imaging applications. Many filtering -based despeckling methods are discussed in many existing works. Two-dimensional (2-D) transforms are also used enormously for the reduction of speckle noise in ultrasound medical images. In recent years, many soft computing-based intelligent techniques have been applied to noise removal and segmentation techniques. However, there is a requirement to improve the accuracy of despeckling using hybrid approaches.

The work focuses on double-bank anatomy with framelet transform combined with Gaussian filter (GF) and also consists of a fuzzy kind of clustering approach for despeckling ultrasound medical images. The presented transform efficiently rejects the speckle noise based on the gray scale relative thresholding where the directional filter group (DFB) preserves the edge information.

The proposed approach is evaluated by different performance indicators such as the mean square error (MSE), peak signal to noise ratio (PSNR) speckle suppression index (SSI), mean structural similarity and the edge preservation index (EPI) accordingly. It is found that the proposed methodology is superior in terms of all the above performance indicators.

Fuzzy kind clustering methods have been proved to be better than the conventional threshold methods for noise dismissal. The algorithm gives a reconcilable development as compared to other modern speckle reduction procedures, as it preserves the geometric features even after the noise dismissal.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

This paper aims to improve the performance of approximate multiplier in terms of peak signal to noise ratio (PSNR) and quality of the image.

The paper proposes an approximate circuit for 4:2 compressor, which shows a significant amount of improvement in performance metrics than that of the existing designs. This paper also reports a hybrid architecture for the Dadda multiplier, which incorporates proposed 4:2 compressor circuit as a basic building block.

Hybrid Dadda multiplier architecture is used in a median filter for image de-noising application and achieved 20% more PSNR than that of the best available designs.

The proposed 4:2 compressor improves the error metrics of a Hybrid Dadda multiplier.

The criteria importance through intercriteria correlation (CRITIC) and range of value (ROV) combined methods were used to determine a single index for all multiple responses.

This paper used cold metal transfer (CMT) and pulse metal-inert gas (MIG) welding processes to study the weld-on-bead geometry of AA2099-T86 alloy. This study used Taguchi's approach to find the optimal setting of the input welding parameters. The welding current, welding speed and contact-tip-to workpiece distance were the input welding parameters for finding the output responses, i.e. weld penetration, dilution and heat input. The L9 orthogonal array of Taguchi's approach was used to find out the optimal setting of the input parameters.

The optimal input welding parameters were determined with combined output responses. The predicted optimum welding input parameters were validated through confirmation tests. Analysis of variance showed that welding speed is the most influential factor in determining the weld bead geometry of the CMT and pulse MIG welding techniques.

The heat input and weld bead geometry are compared in both welding processes. The CMT welding samples show superior defect-free weld beads than pulse MIG welding due to lesser heat input and lesser dilution.

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

The purpose of this paper is to present an up-to-date survey on the non-orthogonal multiple access (NOMA) technique with co-operative strategy, a fast-evolving fifth-generation (5 G) technology. NOMA is used for serving many mobile users, both in power and code domains. This paper considers the power-domain NOMA, which is now discussed as NOMA.

The first part of the paper discusses NOMA-based cooperative relay systems using different relay strategies over different channel models. In various research works, the analytical expressions of many performance metrics were derived, measured and simulated for better performance of the NOMA systems. In the second part, a brief introduction to diversity techniques is discussed. The multiple input and multiple output system merged with cooperative NOMA technology, and its future challenges were also presented in this part. In the third part, the paper surveys some new conceptions such as cognitive radio, index modulation multiple access, space-shift keying and reconfigurable intelligent surface that can be combined with NOMA systems for better performance.

The paper presents a brief survey of diverse research projects being carried out in the field of NOMA. The paper also surveyed two different relaying strategies that were implemented in cooperative NOMA over different channels and compared several performance parameters that were evaluated and derived in these implementations.

The paper provides a scope for recognizable future work and presents a brief idea of the new techniques that can be united with NOMA for better performance in wireless systems.

Currently, the design, technological features of voices, and their analysis of various applications are being simulated with the requirement to communicate at a greater distance or more discreetly. The purpose of this study is to explore how voices and their analyses are used in modern literature to generate a variety of solutions, of which only a few successful models exist.

The mel-frequency cepstral coefficient (MFCC), average magnitude difference function, cepstrum analysis and other voice characteristics are effectively modeled and implemented using mathematical modeling with variable weights parametric for each algorithm, which can be used with or without noises. Improvising the design characteristics and their weights with different supervised algorithms that regulate the design model simulation.

Different data models have been influenced by the parametric range and solution analysis in different space parameters, such as frequency or time model, with features such as without, with and after noise reduction. The frequency response of the current design can be analyzed through the Windowing techniques.

A new model and its implementation scenario with pervasive computational algorithms’ (PCA) (such as the hybrid PCA with AdaBoost (HPCA), PCA with bag of features and improved PCA with bag of features) relating the different features such as MFCC, power spectrum, pitch, Window techniques, etc. are calculated using the HPCA. The features are accumulated on the matrix formulations and govern the design feature comparison and its feature classification for improved performance parameters, as mentioned in the results.

With the advent of technology, a huge amount of data is being transmitted and received through the internet. Large bandwidth and storage are required for the exchange of data and storage, respectively. Hence, compression of the data which is to be transmitted over the channel is unavoidable. The main purpose of the proposed system is to use the bandwidth effectively. The videos are compressed at the transmitter’s end and reconstructed at the receiver’s end. Compression techniques even help for smaller storage requirements.

The paper proposes a novel compression technique for three-dimensional (3D) videos using a zig-zag 3D discrete cosine transform. The method operates a 3D discrete cosine transform on the videos, followed by a zig-zag scanning process. Finally, to convert the data into a single bit stream for transmission, a run-length encoding technique is used. The videos are reconstructed by using the inverse 3D discrete cosine transform, inverse zig-zag scanning (quantization) and inverse run length coding techniques. The proposed method is simple and reduces the complexity of the convolutional techniques.

Coding reduction, code word reduction, peak signal to noise ratio (PSNR), mean square error, compression percent and compression ratio values are calculated, and the dominance of the proposed method over the convolutional methods is seen.

With zig-zag quantization and run length encoding using 3D discrete cosine transform for 3D video compression, gives compression up to 90% with a PSNR of 41.98 dB. The proposed method can be used in multimedia applications where bandwidth, storage and data expenses are the major issues.