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Speech is the primary means of communication for humans. A proper functioning auditory system is needed for accurate cognition of speech. Compressed sensing (CS) is a method for simultaneous compression and sampling of a given signal. It is a novel method increasingly being used in many speech processing applications. The paper aims to use Compressive sensing algorithm for hearing aid applications to reduce surrounding noise.

In this work, the authors propose a machine learning algorithm for improving the performance of compressive sensing using a neural network.

The proposed solution is able to reduce the signal reconstruction time by about 21.62% and root mean square error of 43% compared to default L2 norm minimization used in CS reconstruction. This work proposes an adaptive neural network–based algorithm to enhance the compressive sensing so that it is able to reconstruct the signal in a comparatively lower time and with minimal distortion to the quality.

The use of compressive sensing for speech enhancement in a hearing aid is limited due to the delay in the reconstruction of the signal.

In many digital applications, the acquired raw signals are compressed to achieve smaller size so that it becomes effective for storage and transmission. In this process, even unnecessary signals are acquired and compressed leading to inefficiency.

Hearing loss is the most common sensory deficit in humans today. Worldwide, it is the second leading cause for “Years lived with Disability” the first being depression. A recent study by World health organization estimates nearly 450 million people in the world had been disabled by hearing loss, and the prevalence of hearing impairment in India is around 6.3% (63 million people suffering from significant auditory loss).

The objective is to reduce the time taken for CS reconstruction with minimal degradation to the reconstructed signal. Also, the solution must be adaptive to different characteristics of the signal and in presence of different types of noises.

The purpose of this paper is to develop an improved compressive sensing algorithm for solder joint imagery compressing and recovery. The improved algorithm can improve the performance in terms of peak signal to noise ratio (PSNR) of solder joint imagery recovery.

Unlike the traditional method, at first, the image was transformed into a sparse signal by discrete cosine transform; then the solder joint image was divided into blocks, and each image block was transformed into a one-dimensional data vector. At last, a block compressive sampling matching pursuit was proposed, and the proposed algorithm with different block sizes was used in recovering the solder joint imagery.

The experiments showed that the proposed algorithm could achieve the best results on PSNR when compared to other methods such as the orthogonal matching pursuit algorithm, greedy basis pursuit algorithm, subspace pursuit algorithm and compressive sampling matching pursuit algorithm. When the block size was 16 × 16, the proposed algorithm could obtain better results than when the block size was 8 × 8 and 4 × 4.

The paper provides a methodology for solder joint imagery compressing and recovery, and the proposed algorithm can also be used in other image compressing and recovery applications.

According to the compressed sensing (CS) theory, a sparse or compressible signal can be represented by a fewer number of bases than those required by the Nyquist theorem. The findings provide fundamental guidelines to improve performance in image compressing and recovery based on compressive sensing.

The purpose of this paper is to develop a compressive sensing (CS) algorithm for noisy solder joint imagery compression and recovery. A fast gradient-based compressive sensing (FGbCS) approach is proposed based on the convex optimization. The proposed algorithm is able to improve performance in terms of peak signal noise ratio (PSNR) and computational cost.

Unlike traditional CS methods, the authors first transformed a noise solder joint image to a sparse signal by a discrete cosine transform (DCT), so that the reconstruction of noisy solder joint imagery is changed to a convex optimization problem. Then, a so-called gradient-based method is utilized for solving the problem. To improve the method efficiency, the authors assume the problem to be convex with the Lipschitz gradient through the replacement of an iteration parameter by the Lipschitz constant. Moreover, a FGbCS algorithm is proposed to recover the noisy solder joint imagery under different parameters.

Experiments reveal that the proposed algorithm can achieve better results on PNSR with fewer computational costs than classical algorithms like Orthogonal Matching Pursuit (OMP), Greedy Basis Pursuit (GBP), Subspace Pursuit (SP), Compressive Sampling Matching Pursuit (CoSaMP) and Iterative Re-weighted Least Squares (IRLS). Convergence of the proposed algorithm is with a faster rate O(k*k) instead of O(1/k).

This paper provides a novel methodology for the CS of noisy solder joint imagery, and the proposed algorithm can also be used in other imagery compression and recovery.

According to the CS theory, a sparse or compressible signal can be represented by a fewer number of bases than those required by the Nyquist theorem. The new development might provide some fundamental guidelines for noisy imagery compression and recovering.

The purpose of this paper is to find out the use of compressive sensing (CS) algorithm for wireless sensor networks (WSNs). As energy-efficient algorithms are required for WSNs, CS is very much useful as less than 25 per cent of the entire input data alone is required to be transmitted, and reconstruction at the receiver with this reduced data set is of good quality. But, the usefulness of the algorithm with suitable modulation schemes is not analyzed so far in the literature. Hence, this work concentrated on the algorithm performance with different modulation schemes and different channel conditions.

Compressive sensing encoding is performed by using suitable transform on the input signal. Here, DCT and DWT are used to generate the sparse signal. Random measurement matrix is used to generate the compressed output, which is reconstructed using the Basis Pursuit (BP) method. Also, an analysis for the energy-efficient modulation scheme is performed by modulating the compressed output using QPSK/BPSK/QAM and transmitted by considering the Gaussian and Rayleigh Channels. Energy required per bit transmission is modeled and computed for different schemes.

Simulation result shows that the use of CS algorithm for data compression tremendously reduces the number of transmission bits and, hence, enhances the transmission and bandwidth efficiency in WSN. Results show that DWT is a much suitable transform to be used for sparse measurement generation. In comparison with DCT, DWT is computationally simple and takes very less time, which is expected in real-time application. The reconstruction result shows that about 25 per cent of the data sample is sufficient to recover the original image, perhaps which is the most surprising result. An extensive analysis of various modulation schemes based on the energy model shows that QPSK is in the AWGN channel, and QAM modulation in the Rayleigh channel is a much suitable modulation scheme to be used in WSN for further reduction of energy consumption.

Compressive sensing is recently gaining importance for quantization, compression and noise removal in images. In this paper, this technique was used along with modulation schemes to analyze the suitability of the algorithm for WSN.

The performance of the measurement matrix directly affects the quality of reconstruction of compressive sensing signal, and it is also the key to solve practical problems. In order to solve data collection problem of wireless sensor network (WSN), the authors design a kind of optimization of sparse matrix. The paper aims to discuss these issues.

Based on the sparse random matrix, it optimizes the seed vector, which regards elements in the diagonal matrix of Hadamard matrix after passing singular value decomposition (SVD). Compared with the Toeplitz matrix, it requires less number of independent random variables and the matrix information is more concentrated.

The performance of reconstruction is better than that of Gaussian random matrix. The authors also apply this matrix to the data collection scheme in WSN. The result shows that it costs less energy and reduces the collection frequency of nodes compared with general method.

The authors design a kind of optimization of sparse matrix. Based on the sparse random matrix, it optimizes the seed vector, which regards elements in the diagonal matrix of Hadamard matrix after passing SVD. Compared with the Toeplitz matrix, it requires less number of independent random variables and the matrix information is more concentrated.

The manufacturing of intelligent and secure visual data transmission over the wireless sensor network is key requirement nowadays to many applications. The two-way transmission of image under a wireless channel needed image must compatible along channel characteristics such as band width, energy-efficient, time consumption and security because the image adopts big space under the device of storage and need a long time that easily undergoes cipher attacks. Moreover, Quizzical the problem for the additional time under compression results that, the secondary process of the compression followed through the acquisition consumes more time.

Hence, for resolving these issues, compressive sensing (CS) has emerged, which compressed the image at the time of sensing emerges as a speedy manner that reduces the time consumption and saves bandwidth utilization but fails under secured transmission. Several kinds of research paved path to resolve the security problems under CS through providing security such as the secondary process.

Thus, concerning the above issues, this paper proposed the Corvus corone module two-way image transmission that provides energy efficiency along CS model, secured transmission through a matrix of security under CS such as inbuilt method, which was named as compressed secured matrix and faultless reconstruction along that of eminent random matrix counting under CS.

Experimental outputs shows intelligent module gives energy efficient, secured transmission along lower computational timing also decreased bit error rate.

The aero-engine array electrostatic monitoring technology (AEMT) can provide more and more accurate information about the direct product of the fault, and it is a novel condition monitoring technology that is expected to solve the problem of high false alarm rate of traditional electrostatic monitoring technology. However, aliasing of the array electrostatic signals often occurs, which will greatly affect the accuracy of the information identified by using the electrostatic sensor array. The purpose of this paper is to propose special solutions to the above problems.

In this paper, a method for de-aliasing of array electrostatic signals based on compressive sensing principle is proposed by taking advantage of the sparsity of the distribution of multiple pulse signals that originally constitute aliased signals in the time domain.

The proposed method is verified by finite element simulation experiments. The simulation experiments show that the proposed method can recover the original pulse signal with an accuracy of 96.0%; when the number of pulse signals does not exceed 5, the proposed method can recover the pulse peak with an average absolute error of less than 5.5%; and the recovered aliased signal time-domain waveform is very similar to the original aliased signal time-domain waveform, indicating that the proposed method is accurate.

The proposed method is one of the key technologies of AEMT.

Internet of Things (IoT) is an up-and-coming conception that intends to link multiple devices with each other. The aim of this study is to provide a significant analysis of Green IoT. The IoT devices sense, gather and send out significant data from their ambiance. This exchange of huge data among billions of devices demands enormous energy. Green IoT visualizes the concept of minimizing the energy consumption of IoT devices and keeping the environment safe.

This paper attempts to analyze diverse techniques associated with energy-efficient protocols in green IoT pertaining to machine-to-machine (M2M) communication. Here, it reviews 73 research papers and states a significant analysis. Initially, the analysis focuses on different contributions related to green energy constraints, especially energy efficiency, and different hierarchical routing protocols. Moreover, the contributions of different optimization algorithms in different state-of-the-art works are also observed and reviewed. Later the performance measures computed in entire contributions along with the energy constraints are also checked to validate the effectiveness of entire contributions. As the number of contributions to energy-efficient protocols in IoT is low, the research gap will focus on the development of intelligent energy-efficient protocols to build up green IoT.

The analysis was mainly focused on the green energy constraints and the different robust protocols and also gives information on a few powerful optimization algorithms. The parameters considered by the previous research works for improving the performance were also analyzed in this paper to get an idea for future works. Finally, the paper gives some brief description of the research gaps and challenges for future consideration that helps during the development of an energy-efficient green IoT pertaining to M2M communication.

To the best of the authors’ knowledge, this is the first work that reviews 65 research papers and states the significant analysis of green IoT.

The interdisciplinary nature of video networking, coupled with various recent developments in standards, proposals and applications, poses great challenges to the research and industrial communities working in this area. The main purpose of this paper is to provide a tutorial and survey on recent advances in video networking from an integrated perspective of both video signal processing and networking.

Detailed technical descriptions and insightful analysis are presented for recent and emerging video coding standards, in particular the H.264 family. The applications of selected video coding standards in emerging wireless networks are then introduced with an emphasis on scalable video streaming in multihomed mobile networks. Both research challenges and potential solutions are discussed along the description, and numerical results through simulations or experiments are provided to reveal the performances of selected coding standards and networking algorithms.

The tutorial helps to clarify the similarities and differences among the considered standards and networking applications. A number of research trends and challenges are identified, and selected promising solutions are discussed. This practice would provoke further thoughts on the development of this area and open up more research and application opportunities.

Not all the concerned video coding standards are complemented with thorough studies of networking application scenarios.

The discussed video coding standards are either playing or going to play indispensable roles in the video industry; the introduced networking scenarios bring together these standards and various emerging wireless networking paradigms towards innovative application scenarios.

The comprehensive overview and critiques on existing standards and application approaches offer a valuable reference for researchers and system developers in related research and industrial communities.

Non-orthogonal multiple access (NOMA) is a much hopeful scheme, which is deployed to enhance the spectral efficiency (SE) significantly, and it also enhances the massive access that has attained substantial concern from industrial and academic domains. However, the deployment of superposition coding (SC) at the receiver side resulted in interference. For reducing this interference, “multi-antenna NOMA” seems to be an emerging solution. Particularly, by using the channel state information at the transmitter, spatial beam forming could be deployed that eliminates the interference in an effective manner.

This survey analyzes the literature review and diverse techniques regarding the NOMA-based spatial modulation (SM) environment. It reviews a bunch of research papers and states a significant analysis. Initially, the analysis depicts various transmit antenna selection techniques that are contributed in different papers. This survey offers a comprehensive study regarding the chronological review and performance achievements in each contribution. The analytical review also concerns on the amplitude phase modulation (APM) selection schemes adopted in several contributions. Moreover, the objective functions adopted in the reviewed works are also analyzed. Finally, the survey extends with various research issues and its gaps that can be useful for the researchers to promote improved future works on NOMA-based SM.

This paper contributes to a review related to NOMA-based SM systems. Various techniques and performance measures adopted in each paper are analyzed and described in this survey. More particularly, the selection of transmission antenna and APM are also examined in this review work. Moreover, the defined objective function of each paper is also observed and made a chronological review as well. Finally, the research challenges along with the gaps on NOMA-based SM systems are also elaborated.

This paper presents a brief analysis of NOMA-based SM systems. To the best of the authors’ knowledge, this is the first work that uses NOMA-based SM systems to enhance SE.