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One important innovation in information and coding theory is polar code, which delivers capacity attaining error correction performance varying code rates and block lengths. In recent times, polar codes are preferred to offer channel coding in the physical control channels of the 5G (5th Generation) wireless standard by 3GPP (Third Generation Partnership Project) New Radio (NR) group. Being a part of the physical layer, Channel coding plays key role in deciding latency and reliability of a communication system. However, the error correction performance degrades with decreased message lengths. 5G NR requires channel codes with low rates, very low error floors with short message lengths and low latency in coding process. In this work, Distributed Cyclic Redundancy Check Aided polar (DCA-polar) code along with Cyclic Redundancy Check Aided polar (CA-polar) code, the two variant of polar codes have been proposed which provide significant error-correction performance in the regime of short block lengths and enable early termination of decoding processes. While CRC bits improve the performance of SCL (successive cancellation list) decoding by increasing distance properties, distributed CRC bits permit path trimming and early-termination of the decoding process. The design can reduce the decoding latency and energy consumption of hardware, which is crucial for mobile applications like 5G. The work also considers the performance analysis of NR polar codes over AWGN (Additive White Gaussian Noise) for short information block lengths at low code rates in the uplink and downlink control channels using SNR (Signal to Noise Ratio) and FAR (False Alarm Rate) as the performance measures. Simulation results illustrate different trade-offs between error-correction and detection performances comparing proposed NR polar coding schemes. [ABSTRACT FROM AUTHOR]

In this paper, an innovative system called grouping frequency-time index modulation-based differential chaos shift keying (GFTIM-DCSK-I) is presented to improve the energy efficiency (EE), spectral efficiency, and data rate of the recently proposed grouping subcarrier index modulation in differential chaos shift keying (GSIM-DCSK) system. The proposed system optimally utilizes both frequency and time resources, enabling efficient transmission at high data rates. The data information bits are divided into equal L groups, each containing (p1+p2+1) bits. Within each group, the first set of bits, referred to as p1 bits, are assigned as carrier-indexed bits, whereas the second set of bits, known as p2 bits, are designated as time-indexed bits. Additionally, one bit is assigned for modulation. The p1 bits serve as an index selector for choosing a single active subcarrier from a total of 2p1 subcarriers. p2 bits are utilized to choose a single time slot out of a total of 2p2 available options. The modulated bit undergoes DCSK modulation and is thereafter sent via the active subcarriers. Mathematical expressions are constructed to examine the system performance of energy efficiency, spectral efficiency, and system complexity, then compared with the traditional DCSK modulations. Analytical formulas for the bit error rate (BER) of the proposed system are obtained under the impact of multipath Rayleigh fading channel and additive white Gaussian noise (AWGN). Results from simulations and analysis confirm that the theoretical analysis is accurate. The performance of the suggested approach is also evaluated by comparing it to conventional DCSK techniques, showing an improvement in the performance.

Orthogonal Time Frequency Space (OTFS) modulation is proposed to support high-speed and reliable vehicular communication scenarios, which is a novel two-dimensional (2D) delay-Doppler domain modulation technique. Compared with traditional modulation schemes such as orthogonal frequency division multiplexing (OFDM), OTFS has displayed better performance in very high-mobility wireless channels. In this work, we have proposed the novel VLSI architecture for OTFS in the Additive White Gaussian Noise (AWGN) channel condition and shown the resource utilization report of our proposed architecture to implement on Field Programmable Gate Array (FPGA) board, along with the relationship between inputs and outputs of the OTFS signal in the delay-Doppler domain. [ABSTRACT FROM AUTHOR]

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Due to the widespread use of mobile devices and digital cameras, the usage of image data on various online platforms and social media has been rapidly increasing. However, during the data processing, digital images can exhibit various types of noise, with Additive White Gaussian Noise being one of the most common types. Various types of filters have been studied to remove such noise, but traditional methods have shown inadequate performance in either low-frequency or high-frequency regions. Therefore, this paper proposes an AWGN removal algorithm based on pixel distribution and spatial weighting. The algorithm utilizes absolute deviation values to analyze pixel data distribution, and based on this analysis, it separates each local mask into low-frequency and high-frequency regions. Additionally, it adaptively applies the algorithm by considering the pixel distribution in each region to effectively remove noise. Simulation results demonstrated that the proposed algorithm effectively preserved detailed information in high-frequency regions while also exhibiting excellent noise removal performance in low-frequency regions. [ABSTRACT FROM AUTHOR]

In recent years, digital imaging has played an essential role in medical diagnostics, security and surveillance, robotics engineering, and many other fields. However, noise can occur due to environmental factors or during data transmission, which degrades the image quality and causes information loss. AWGN(additive white gaussian noise) is a typical noise in digital images, and various filters have been studied to remove it. However, the existing methods show somewhat inadequate denoising performance by blurring the image while removing noise. Therefore, in this paper, we propose an AWGN denoising algorithm using a Gaussian weighted filter based on GMM(gaussian mixture model) clustering. The proposed algorithm simultaneously applies a mask for the probability of each pixel belonging to a cluster and a mask that follows a Gaussian distribution in space to improve the denoising performance while preserving the details in the high-frequency region. [ABSTRACT FROM AUTHOR]

In this paper, an innovative system called grouping frequency-time index modulation-based differential chaos shift keying (GFTIM-DCSK-I) is presented to improve the energy efficiency (EE), spectral efficiency, and data rate of the recently proposed grouping subcarrier index modulation in differential chaos shift keying (GSIM-DCSK) system. The proposed system optimally utilizes both frequency and time resources, enabling efficient transmission at high data rates. The data information bits are divided into equal L groups, each containing (p1+p2+1) bits. Within each group, the first set of bits, referred to as p1 bits, are assigned as carrier-indexed bits, whereas the second set of bits, known as p2 bits, are designated as time-indexed bits. Additionally, one bit is assigned for modulation. The p1 bits serve as an index selector for choosing a single active subcarrier from a total of 2p1 subcarriers. p2 bits are utilized to choose a single time slot out of a total of 2p2 available options. The modulated bit undergoes DCSK modulation and is thereafter sent via the active subcarriers. Mathematical expressions are constructed to examine the system performance of energy efficiency, spectral efficiency, and system complexity, then compared with the traditional DCSK modulations. Analytical formulas for the bit error rate (BER) of the proposed system are obtained under the impact of multipath Rayleigh fading channel and additive white Gaussian noise (AWGN). Results from simulations and analysis confirm that the theoretical analysis is accurate. The performance of the suggested approach is also evaluated by comparing it to conventional DCSK techniques, showing an improvement in the performance. [ABSTRACT FROM AUTHOR]

Na $\ddot {i}$ ve simulated additive white Gaussian noise (AWGN) may not fully characterize the complexity of real world noisy images. Owing to optimal sparsity in image representation, we propose a curvelet based model for denoising real-world RGB images. Initially, the image is decomposed in three curvelet scales, namely: the approximation scale (that retains low-frequency information), the coarser scale and the finest scale (that preserves high-frequency components). Coefficients in the approximation and finest scale are estimated using NLM filter, while a scale dependent threshold is adopted for signal estimation in the coarser scale. The reconstructed image in spatial domain is further processed using Guided Image Filter (GIF) to suppress the ringing artifacts due to curvelet thresholding. The proposed approach known as CTuNLM method is extended for color image denoising using uncorrelated YUV color space. Extensive experiments on multi-channel real noisy images are conducted in comparison with eight sate-of-the-art methods. With four encouraging qualitative and quantitative measures including PSNR and SSIM, we found that CTuNLM method achieves better denoising performance in terms of noise reduction and detail preservation. We further examined the potential of proposed approach by focusing only on the Finest scale curvelet Coefficients (FC). Features like small details, edges and textures always add up to improve the overall denoising performance, while minimizing spurious details. We studied “The Curious Case of the Finest Scale” and constructed “Deep Curvelet-Net”: an encoder-decoder-based CNN architecture, as a pilot work. The encoder uses multiscale spatial characteristics from noisy FC, while the decoder processes de-noised FC under the supervision of encoder’s multiscale spatial attention map. The “Deep Curvelet-Net” links encoder multiscale feature modeling with decoder spatial attention supervision to learn the most essential features for denoising. The CNN-based architecture only estimates FC, while all other CTuNLM stages are left unchanged to produce the denoised output. Results presented in this article validated the design of proposed CNN architecture in curvelet domain and motivated us to search beyond classical thresholding and/or filtering approaches.

Summary: A novel scheme for designing polar codes with specific decoding schemes in the additive white Gaussian noise channel is presented in this paper. The code construction strategy is built on the genetic algorithm (GA) where successive evolution of populations (or group of information sets) leads toward fittest candidate to attain finest error‐rates. In this work, it is shown that better error‐rates can be attained by both successive cancelation list decoding using GA with no cyclic redundancy check (CRC) and belief propagation decoding using GA with no CRC and no list, compared to existing polar decoding schemes. Our proposed polar code design scheme using GA has the ability to attain a target block error rates with the least possible SNR and using no additional CRC by exploiting least belief propagation iterations or lesser successive cancelation list list size with no self‐alterations in the decoding algorithm. [ABSTRACT FROM AUTHOR]

Nowadays, there are massive demands on the broadband multimedia services. Data security in today's world is an essential issue in different aspects of life such as marketing services, defense, financial, and health care. Modulation schemes, channel coding, diversity techniques, etc., are types of physical layer security methods, which are used to ensure the data from unauthorized access. In addition, various cryptographic techniques are usually used to secure data transmission between sender and the intended receiver. The combination of cryptographic techniques and physical layer security schemes makes it better for transferring multimedia data through unsecure or potentially risky communication (wired and wireless) path. Many chaotic-based encryption algorithms have been suggested to ensure confidentiality and security for multimedia data over an insecure wireless channel. Cryptography techniques do not involve any system complexity but add a little computation complexity. Use of advanced processors mitigates the computational burden. In this paper, we have discussed efficient transmission of an encrypted color image through a multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO–OFDM) system over a Rayleigh fading with an additive white Gaussian noise channel. Four schemes of chaotic-based encryption are employed. Both 2 × 2 MIMO - OFDM and 4 × 4 MIMO - OFDM wireless communication systems are presented. Peak signal-to-noise ratio metric is used to evaluate the quality of the received image at different signal-to-noise ratios. Other metrics such as number of changing pixel rate, unified averaged changed intensity, correlation coefficient, entropy, and histogram are evaluated. [ABSTRACT FROM AUTHOR]

During the recording time, electrocardiogram (ECG) signals are subject to multiple artefact noises, such as muscle activity, white Gaussian noise (WGN), baseline wander, and power line interference (PLI). Therefore, pre-processing of ECG signals is essential to eliminate these artefacts and to obtain efficient ECG features. Many approaches have been proposed for removing ECG noises, including ECG signal denoising using wavelet transform (WT). However, the effectiveness and performance of the WT technique are strongly related to the configuration of its control parameters, which are typically fine-tuned through a laborious and time-consuming series of experiments. This paper introduces a technique that combines particle swarm optimisation (PSO) with WT for ECG signal denoising. The key contribution of this research is the use of PSO to determine the optimum settings for all WT parameters for ECG signal denoising (type of wavelet basis function Φ, thresholding function β, level of decomposition L, rule for threshold selection λ, and rescaling method ρ). The efficiency of the proposed method is evaluated using the percentage root mean square difference (PRD) and the signal-to-noise ratio (SNR), employing various ECG signals available online from the MIT-BIH Arrhythmia database. Experimental results show that the proposed PSO-WT technique yields better results than state-of-the-art techniques in terms of SNR, particularly for PLI measured at 60 Hz and still acceptable at 50 Hz. For example, a denoised ECG signal resulting from the proposed technique at an SNR input of 10 dB corresponds to an SNRoutput of 27.47 dB at 60 Hz, improving the quality of the denoised ECG signal and making it more appropriate for clinical diagnosis. Furthermore, the proposed method also shows promising efficiency in the presence of WGN, making it highly relevant for IoT applications and RF transmission.

Particularly, for real time monitoring-IOT has just evolved to support massive capacity Cellular LTE networks. The fundamental issue is the constrained bandwidth that NB-IOT channels allow. Therefore, goal of paper is to enhance the capacity of the NB-IOT physical layer shared channels for uplink and down links. The primary objective of this article is to improve the efficiency of an LTE-OFDM framework for NB-IOT-WAN via BER and BLER assessment. The proposed approach improves performance by selecting suitable cyclic prefix sampling with FFT size. It is planned to adopt M-QAM for increased data transmission capacity and reducing OFDM BER. In order to enhance the capacity approach of diversity reception is considered along with the modified modulation parameters. Increased FFT size is offered for OFDM capacity enhancement for narrowband physical downlink shared channel (NPDSCH) and narrowband physical uplink shared channel (NPUSCH) transceivers. The block error rate (BLER) is used as the performance evaluation parameters for the LTE-OFDM based NB-IOT system. The impact of the higher block transmission length is assessed for the improvement in the BLER rates. It is proposed to use M-QAM for NPUSCH which may offer better capacity of data transmission. The performance is compared with the standard QPSK modulation. For the uplink design under AWGN and frequency selective channels are considered for evaluation. The numbers of receiver antenna are increased for offering the better diversity reception to further enhance the channel capacity. The optimal design parameter selection might improve the performance of the NB-IOT cellular networks. The BLER is evaluated for different case of uplink and downlink designs against the wide range of the SNR. The entire document takes into account all factors in order to get the greatest system efficiency, and it offers capacity enhancement. [ABSTRACT FROM AUTHOR]

In this paper, a no-reference quality assessment method for image contaminated with additive white Gaussian noise (AWGN) is proposed. The proposed spatial domain method is based on the fact that if the portion of an image having structure is distorted by AWGN, then variance of the distorted image increases. On the other hand, if the smooth portion of image is contaminated by AWGN, then the distorted image will have proportionate variance as that of AWGN. Therefore, variance of the smoother parts of the image contaminated with AWGN reflects the level of noise and hence can be used as an indicator of the image quality. Extensive simulation results show that the proposed method is highly accurate and lower in complexity in comparison to the existing algorithms. [ABSTRACT FROM AUTHOR]

Gradient descent strategy, viewed as an important model optimization method, has been widely used for various tasks (such as model-based image denoising) of computer vision. In the gradient descent denoising model, the learning rate (LR) and residual component are two important parts to be adaptively estimated for its stable point. This letter proposes a deep gradient descent network (DGDNet), including two key points: one is that the LR is designed with eigenvalues of Hessian matrix of remotely sensed images (RSIs) and their local weighted factor (LWF), which can recognize structures from RSIs degraded by additive white Gaussian noise (AWGN). The other is that the residual part is calculated by an U-shaped network (USNet) to speed up the DGDNet convergent to a fixed point. Finally, the two components are plugged into the gradient descent scheme and contribute to an enjoyable result with a few iterations. Quantitatively and qualitatively experimental results demonstrate that the proposed DGDNet can obtain a stable solution efficiently, and produce competitive denoising performance which is even better than that yielded by the state-of-the-art noise reduction methods. [ABSTRACT FROM AUTHOR]

This study presents a general and closed-form expression for the bit error probability (BEP) of Gray-coded M-ary phase shift keying (MPSK) in the presence of phase noise in additive white Gaussian noise (AWGN) channel. Closed-form expressions are obtained by converting the AWGN channel noise to equivalent phase noise, under the approximation that the AWGN channel noise and the phase error are both Gaussian distributed. The only existing work in the literature with similar assumption only calculates the BEP for M ≤ 16. The proposed analysis in this study will facilitate the BEP calculation for higher order MPSK. [ABSTRACT FROM AUTHOR]

With the development of IoT and AI technology in modern society, diverse fields are being automatized. The automation system using images detects characteristics of the ones gained with a camera or sensors or categorizes objects. Yet, noise can be made for different reasons in the process of getting images, and the part damaged by noise can be detected in error or cause the detection rate to become reduced. In this paper, the learning algorithm to restore images damaged by AWGN based on the residual connection was suggested. Regarding the proposed algorithm, the hidden layer of the convolution neural network was divided into three blocks depending on the depth of the network, and the learning data of each blcok was delivered to the link layer using the residual link. The final result was obtained using the output of the link layer, and the noise of images was removed by obtaining the estimated noise with the studied neural network. [ABSTRACT FROM AUTHOR]

With advances in digital image technology, researchers of various fields have developed image processing technologies. However, noise can affect image quality significantly in visual monitoring equipment or special devices —such as CMOS sensors, CT, MRI, and SEM—that are inevitably affected by the surrounding environment. AWGN generated in images is a noise that appears in all frequency bands and is characterized by the additive noise function that follows a Gaussian distribution. Many studies have been proposed in the past to remove AWGN. However, they could not improve image quality sufficiently and had poor edge preservation performance due to severe blurring. Therefore, this study proposes an algorithm that uses spatial and adaptive weights to restore damaged images effectively. The proposed filter uses a noise density weight and a median weight as spatial weights to remove surrounding pixels that have been severely damaged. In addition, it performs adaptive weight filtering on the center pixel to restore noisy images in detail. [ABSTRACT FROM AUTHOR]

As research on IoUT using underwater communication is actively progressing worldwide, in this study, research was conducted to increase the transmission rate by utilizing LFM (Linear Frequency Modulation) signals using chirp signals, one of the data transmission techniques. In this paper, we propose a system that combines the corresponding LFM signal into Up-LFM, Down-LFM, and Up&Down-LFM. The proposed system transmits and receives multiple bits in one chirp signal. The simulated channel environment is an AWGN (Additive White Gaussian Noise) channel, and the first simulation result showed that the performance of Up&Down-LFM was superior by about 2.1dB on average compared to Up-LFM and Down-LFM, and BPSK (Binary Phase Shift Keying), the performance comparison result with QPSK (Quadrature Phase Shift Keying) was excellent by about 5.86dB on average. The second simulation result confirmed a performance difference of about 5dB between Tx 1 bits and 5 bits at frequency sampling 50kHz, about 4dB at 100kHz, and 5dB at 200kHz. [ABSTRACT FROM AUTHOR]

In today’s society, automation and artificial intelligence technology are used in various fields, and image data is acquired using cameras and sensors to recognize or classify objects. However, noise can be caused by sensor defects or external signals in the process of acquiring image data, and noise removal is used in the preprocessing process because images damaged by noise adversely affect the system. This study proposes a filter algorithm using composite weights to minimize blurring that occurs in the process of removing AWGN. The proposed algorithm compares two regions using a central mask and a matching mask, and obtains weight by structural features, weight by pixel value comparison, and weight by pixel distribution, respectively. The output of the filter was calculated by convolutioning three weights to the pixel values of the matching area. Simulations were conducted to analyze the noise removal performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Aiming to address the problems of the high bit error rate (BER) of demodulation or low classification accuracy of modulation signals with a low signal-to-noise ratio (SNR), we propose a double-residual denoising autoencoder method with a channel attention mechanism, referred to as DRdA-CA, to improve the SNR of modulation signals. The proposed DRdA-CA consists of an encoding module and a decoding module. A squeeze-and-excitation (SE) ResNet module containing one residual connection is modified and then introduced into the autoencoder as the channel attention mechanism, to better extract the characteristics of the modulation signals and reduce the computational complexity of the model. Moreover, the other residual connection is further added inside the encoding and decoding modules to optimize the network degradation problem, which is beneficial for fully exploiting the multi-level features of modulation signals and improving the reconstruction quality of the signal. The ablation experiments prove that both the improved SE module and dual residual connections in the proposed method play an important role in improving the denoising performance. The subsequent experimental results show that the proposed DRdA-CA significantly improves the SNR values of eight modulation types in the range of −12 dB to 8 dB. Especially for 16QAM and 64QAM, the SNR is improved by 8.38 dB and 8.27 dB on average, respectively. Compared to the DnCNN denoising method, the proposed DRdA-CA makes the average classification accuracy increase by 67.59∼74.94% over the entire SNR range. When it comes to the demodulation, compared with the RLS and the DnCNN denoising algorithms, the proposed denoising method reduces the BER of 16QAM by an average of 63.5% and 40.5%, and reduces the BER of 64QAM by an average of 46.7% and 18.6%. The above results show that the proposed DRdA-CA achieves the optimal noise reduction effect. [ABSTRACT FROM AUTHOR]

This paper presents the construction of Run Length Limited (RLL) Error Control Codes (ECCs) from four Low Density Parity Check (LDPC) Codes specified by Consultative Committee for Space Data Systems (CCSDS). The obtained RLL-ECCs present a practical alternative to the CCSDS codes with pseudorandomizers. Their advantage is that the maximal runlengths of equal symbols in their codeword sequences are guaranteed, which is not the case if the common approach with pseudo-randomizers is used. The other advantages are that no additional redundancy is introduced into encoded codewords and that the encoding and decoding procedures of the original error control CCSDS codes do not have to be modified in the following cases. In the first case if hard decoding is used and the transmission channel can be modeled as a Binary Symmetric Channel (BSC) or in the second case if soft decoding and coherent Binary Phase Shift Keying (BPSK) modulation is used and the appropriate transmission channel model is an Additive White Gaussian Noise (AWGN) channel. [ABSTRACT FROM AUTHOR]

Cellular licensed networks are now widely being used for Internet of Things (IOT) network deployment. The prime goal is to enhance the coverage and capacity of the data usage. Any performance enhancement is highly recommended for these IOT networks. The NB-IOT cellular communication network is a narrow-band IOT technology developed for wide-area network (WAN}) applications. NB-IOT can be used to upload data to internet servers by practically all mobile devices. The physical layer performance of the NB-IOT network is evaluated in this research using the efficient Orthogonal-Frequency Division Multiplexing (OFDM) technology used in long-term evaluation (LTE) communication standards. For larger capacity OFDM system design, a predefined set of phase offsets and bandwidth is taken into account. The higher FFT levels' bit error rate (BER) performance is compared to M-PSK modulation approaches. [ABSTRACT FROM AUTHOR]

Orthogonal Frequency Division Multiplexing (OFDM) is the best choice for wireless and wired high data rate communications. The OFDM system has many advantages such as high spectral efficiency, robustness to channel fading, and immunity to impulse interference. However, it has a main drawback, which is the Peak to Average Power Ratio (PAPR). Selecting Mapping (SLM) and Partial Transmit Sequences (PTS) are the two essential techniques for reducing the PAPR; however, they need to send side information to indicate how the transmitter generates signals. In this paper, Particle Swarm Optimization (PSO)-based dummy sub-carriers are inserted with the data to reduce PAPR without transmitting side information. The incorporation of PSO-based Dummy Sub-carriers Insertion (DSI) method on this system is performed by measuring the PAPR at the IFFT output (Inverse Fast Fourier Transform) after adding 6 adaptive sub-carrier sequences to the input data of IFFT. The PSO-based dummy algorithm decides to send the data once the PAPR of this data sequence and dummy sub-carrier is under a specific threshold. Using MATLAB as a mathematical simulation tool, simulation results are presented for a standard OFDM network model. The network model has been simulated in an Additive White Gaussian Noise (AWGN) channel environment to investigate PAPR and Bit-Error Rate (BER) performance. The simulation results show that the dummy sequence based on PSO reduces PAPR down to 4 dB compared to a conventional OFDM system and down to 3 dB compared with the clipping technique for the same BER performance.

A new bound on the error probability of coding with limited code length over additive white Gaussian noise (AWGN) channels is proposed. The developed bound is proved to be universal for two connected encoding ways. On the one hand, we conceive folding the conventional codes, such as Hadamard and binary random ones, in order to adapt shorter code length. On the other hand, we further extend the above folded structure to Gaussian random coding and hence to bound its error probability. Finally, we demonstrate that the bound of the above two constructions can be unified as $\sqrt {\frac {log_{2}e}{2\pi nC}}2^{-n(C/2-R)}$ where $C$ represents the capacity of the AWGN channel, $n$ and $R$ stand for the code length and rate, respectively. This theoretical contribution confirms that, in the context of short code length and low rate, the developed two constructions exhibit excellent performance even close to the Shannon bound on AWGN channels.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM), stands out among conventional communication technologies, as it uses the indices of the available transmit entities. Thanks to such an approach, it offers a novel method for the transmission of extra data bits. Recent years have seen a great interest in chaos-based communications. The spectrum-spreading signals used in chaotic signal modulation technologies are orthogonal to the existing mixed signals. This paper presents how well a non-coherent differential chaos shift keying communication system performs across an AWGN. Different types of detection methods are simulated, bit error rate and power spectral density are calculated and then compared with standard OFDM with index modulation. The results of simulations concerning the performance of a DCSK system, adding to the security of the proposed solution and offering a comparable bit error rate performance, are presented in the paper as well.

Image noise occurs during acquisition and transmission and adversely affects processes, such as image segmentation and object recognition and classification. Various techniques are being studied for noise removal, and with the recent development of hardware and image processing algorithms, noise removal techniques that combine non-local techniques are attracting attention. However, one disadvantage of this method is that blurring occurs in the edges and boundary line of the resulting image. In this study, we proposed a modified local steering kernel based on image matching to improve these shortcomings. The proposed technique uses image matching to differentiate the weight obtained by the steering kernel according to the local characteristics of the image and calculates the weight of the filter based on the similarity between the center window and the matching window. The resulting images were quantitatively evaluation and enlargement of images were used and compared with the existing noise removal algorithms. The proposed algorithm showed clearer contrast in the enlarged images and better results than the conventional image restoration techniques in the quantitative evaluation using peak signal-to-noise ratio and structural similarity index. [ABSTRACT FROM AUTHOR]

This paper reviews novel methods for reducing image noise. These techniques address noise that is impulsive, multiplicative, and gaussian. While the picture mixture method is utilized as a generic model for instinct sound, the still built medium strainer is declared for the reduction of salt and common pepper sound. Multiplicative sound discount strategies make use of Lee filtering and adaptive windowing. To reduce additive white Gaussian noise, a unique frequency domain method called sliding double window filtering was employed. The Fibonacci Fourier Transform is used in this procedure. These techniques offer effective edge preservation and noise suppression capabilities, as shown by the quantitative analysis and simulation results. [ABSTRACT FROM AUTHOR]

Orthogonal frequency division multiplexing with index modulation (OFDM-IM), stands out among conventional communication technologies, as it uses the indices of the available transmit entities. Thanks to such an approach, it offers a novel method for the transmission of extra data bits. Recent years have seen a great interest in chaos-based communications. The spectrum-spreading signals used in chaotic signal modulation technologies are orthogonal to the existing mixed signals. This paper presents how well a non-coherent differential chaos shift keying communication system performs across an AWGN. Different types of detection methods are simulated, bit error rate and power spectral density are calculated and then compared with standard OFDM with index modulation. The results of simulations concerning the performance of a DCSK system, adding to the security of the proposed solution and offering a comparable bit error rate performance, are presented in the paper as well. [ABSTRACT FROM AUTHOR]