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Imposing nonperiodic boundary conditions for unit cell analyses may be necessary for a number of reasons in applications, for example, for validation purposes and specific computational setups. The work at hand discusses a strategy for utilizing the powerful technology behind fast Fourier transform (FFT)‐based computational micromechanics—initially developed with periodic boundary conditions in mind—for essential boundary conditions in mechanics, as well, for the case of the discretization on a rotated staggered grid. Introduced by F. Willot into the community, the rotated staggered grid is presumably the most popular discretization, and was shown to be equivalent to underintegrated trilinear hexahedral elements. We leverage insights from previous work on the Moulinec–Suquet discretization, exploiting a finite‐strain preconditioner for small‐strain problems and utilize specific discrete sine and cosine transforms. We demonstrate the computational performance of the novel scheme by dedicated numerical experiments and compare displacement‐based methods to implementations on the deformation gradient. [ABSTRACT FROM AUTHOR]

We discuss how Dirichlet boundary conditions can be directly imposed for the Moulinec–Suquet discretization on the boundary of rectangular domains in iterative schemes based on the fast Fourier transform (FFT) and computational homogenization problems in mechanics. Classically, computational homogenization methods based on the fast Fourier transform work with periodic boundary conditions. There are applications, however, when Dirichlet (or Neumann) boundary conditions are required. For thermal homogenization problems, it is straightforward to impose such boundary conditions by using discrete sine (and cosine) transforms instead of the FFT. This approach, however, is not readily extended to mechanical problems due to the appearance of mixed derivatives in the Lamé operator of elasticity. Thus, Dirichlet boundary conditions are typically imposed either by using Lagrange multipliers or a "buffer zone" with a high stiffness. Both strategies lead to formulations which do not share the computational advantages of the original FFT-based schemes. The work at hand introduces a technique for imposing Dirichlet boundary conditions directly without the need for indefinite systems. We use a formulation on the deformation gradient—also at small strains—and employ the Green's operator associated to the vector Laplacian. Then, we develop the Moulinec–Suquet discretization for Dirichlet boundary conditions—requiring carefully selected weights at boundary points—and discuss the seamless integration into existing FFT-based computational homogenization codes based on dedicated discrete sine/cosine transforms. The article culminates with a series of well-chosen numerical examples demonstrating the capabilities of the introduced technology. [ABSTRACT FROM AUTHOR]

Over the past decades, surface wave methods have been routinely employed to retrieve the physical characteristics of the first tens of meters of the subsurface, particularly the shear wave velocity profiles. Traditional methods rely on the application of the multichannel analysis of surface waves to invert the fundamental and higher modes of Rayleigh waves. However, the limitations affecting this approach, such as the 1D model assumption and the high degree of subjectivity when extracting the dispersion curve, motivate us to apply the elastic full‐waveform inversion, which, despite its higher computational cost, enables leveraging the complete information embedded in the recorded seismograms. Standard approaches solve the full‐waveform inversion using gradient‐based algorithms minimizing an error function, commonly measuring the misfit between observed and predicted waveforms. However, these deterministic approaches lack proper uncertainty quantification and are susceptible to get trapped in some local minima of the error function. An alternative lies in a probabilistic framework, but, in this case, we need to deal with the huge computational effort characterizing the Bayesian approach when applied to non‐linear problems associated with expensive forward modelling and large model spaces. In this work, we present a gradient‐based Markov chain Monte Carlo full‐waveform inversion where we accelerate the sampling of the posterior distribution by compressing data and model spaces through the discrete cosine transform. Additionally, a proposal is defined as a local, Gaussian approximation of the target density, constructed using the local Hessian and gradient information of the log posterior. We first validate our method through a synthetic test where the velocity model features lateral and vertical velocity variations. Then we invert a real dataset from the InterPACIFIC project. The obtained results prove the efficiency of our proposed algorithm, which demonstrates to be robust against cycle‐skipping issues and able to provide reasonable uncertainty evaluations with an affordable computational cost. [ABSTRACT FROM AUTHOR]

This paper presents the graphics processing unit (GPU) implementation of two-dimensional discrete cosine transform (2D DCT) and inverse discrete cosine transform (2D IDCT) for image compression applications. Based on the trigonometric properties, the transform matrices are simplified, resulting in reduced computation over the naive implementation. To assess its performance, the output image quality is measured in terms of several metrics and found to be better than all other existing transforms. To further improve the timings, a GPU implementation of the proposed transforms is obtained by exploiting the inter-level parallelism among threads and blocks in addition to efficiently accessing data from the shared memory resources. This has resulted in significant improvement in speedup (more than 5k) for both the transforms. The proposed GPU implementation of 2D DCT is compared in terms of processing time and is shown to outperform the existing work across all image dimensions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a quantum steganography scheme with a color image as the cover image. In order to enhance the security of the embedded information, Arnold disambiguation is first performed on the secret image, followed by embedding the three bits of the secret image in the cover image by using the DWT method in the R-channel of the cover image, the DCT method in the G-channel, and the LSB embedding directly in the B-channel. By performing the embedding of the secret image in each of the three channels, the embedding capacity and effect are significantly improved. In addition, the robustness of the embedded image is also greatly improved due to the use of three embedding methods. The average PSNR after image steganography is 54.33dB, and the average NCC for extracting the secret image under Gaussian noise and pretzel noise with an intensity of 0.02 is 0.96 and 0.98, respectively. Simulation and numerical analyses show that the algorithm performs well in terms of invisibility, security, and robustness. [ABSTRACT FROM AUTHOR]

The lossless coding framework of High Efficiency Video Coding (HEVC) comprises prediction and entropy coding, while omitting the transform process. However, residuals derived from prediction continue to demonstrate significant spatial redundancy, which has the potential to compromise the coding efficiency. This article presents an optimized scheme for HEVC intra lossless coding that integrates transform and quantization. In the proposed scheme, a residual block, generated by HEVC, is partitioned into two components: the coefficient block and the error block. The coefficient block, responsible for lossless coding, is obtained through the adoption of the transform and quantization processes employed in HEVC lossy coding. Subsequently, the error block is derived by predicting the residual block using the generated coefficient block. Both the coefficient block and the error block can be effectively encoded utilizing the existing entropy coding scheme in HEVC lossy coding. Rate‐distortion optimization is employed to determine whether the residual block will be partitioned into two components. The proposed scheme is implemented into the HM 12.1 software. Through the utilization of the discrete cosine transform kernel and the discrete sine transform kernel, with a quantization parameter set to 20, on HEVC standard test sequences and the all intra main configuration file, empirical findings substantiate that the proposed methodology attains an average bit‐rate reduction of 3.31%, with a maximum improvement of 12.29% in comparison to HEVC lossless coding. [ABSTRACT FROM AUTHOR]

The detection of defects on the copper chain in the production process of tobacco cutters is crucial for ensuring product quality. Traditional defect detection methods often rely on spatial domain image analysis, which not only has a large computational load but also performs poorly in handling high‐frequency noise and complex backgrounds. To address this issue, this paper proposes a novel neural network model based on frequency domain analysis, called frequency domain attention network. This network first utilizes discrete cosine transform to transform the image from the spatial domain to the frequency domain, effectively reducing computational complexity and improving processing speed. Subsequently, through the innovative frequency domain attention module, the network automatically identifies and enhances key discriminative features in the frequency domain, thereby strengthening the model's ability to identify defects. Finally, the frequency domain attention map, after feature extraction and integration, is inputted into the coupling detection head to achieve high‐precision defect detection. The experimental results show that our method outperforms the SOTA method with an increase of 0.03 in AP and 21 in FPS. [ABSTRACT FROM AUTHOR]

The accurate extraction of urban residential space (URS) is of great significance for recognizing the spatial structure of urban function, understanding the complex urban operating system, and scientific allocation and management of urban resources. The traditional URS identification process is generally conducted through statistical analysis or a manual field survey. Currently, there are also superpixel segmentation and wavelet transform (WT) processes to extract urban spatial information, but these methods have shortcomings in extraction efficiency and accuracy. The superpixel wavelet fusion (SWF) method proposed in this paper is a convenient method to extract URS by integrating multi-source data such as Point of Interest (POI) data, Nighttime Light (NTL) data, LandScan (LDS) data, and High-resolution Image (HRI) data. This method fully considers the distribution law of image information in HRI and imparts the spatial information of URS into the WT so as to obtain the recognition results of URS based on multi-source data fusion under the perception of spatial structure. The steps of this study are as follows: Firstly, the SLIC algorithm is used to segment HRI in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration. Then, the discrete cosine wavelet transform (DCWT) is applied to POI–NTL, POI–LDS, and POI–NTL–LDS data sets, and the SWF is carried out based on different superpixel scale perspectives. Finally, the OSTU adaptive threshold algorithm is used to extract URS. The results show that the extraction accuracy of the NLT–POI data set is 81.52%, that of the LDS–POI data set is 77.70%, and that of the NLT–LDS–POI data set is 90.40%. The method proposed in this paper not only improves the accuracy of the extraction of URS, but also has good practical value for the optimal layout of residential space and regional planning of urban agglomerations. [ABSTRACT FROM AUTHOR]

The Discrete Cosine Transform (DCT) can be used instead of the conventional Discrete Fourier Transform (DFT) for the Orthogonal Frequency Division Multiplexing (OFDM) construction, which offers many advantages. In this paper, the Multiple-Input-Multiple-Output (MIMO) DCT-OFDM is enhanced using a proposed Cosine Domain Equaliser (CDE) instead of a Frequency Domain Equaliser (FDE). The results are evaluated through the Rayleigh fading channel with Co-Carrier Frequency Offset (Co-CFO) of different MIMO configurations. The average bit error probability and the simulated time of the proposed scheme and the conventional one are compared, which indicates the importance of the proposed scheme. Also, a closed formula for the number of arithmetic operations of the proposed equaliser is developed. The proposed equaliser gives a simulation time reduction of about 81.21%, 83.74% compared to that of the conventional Linear Zero Forcing (LZF)-FDE and Linear Minimum Mean Square Error (LMMSE)-FDE, respectively, for the case of a 4 × 4 configuration. [ABSTRACT FROM AUTHOR]

The precise and prompt identification of skin cancer is essential for efficient treatment. Variations in colour within skin lesions are critical signs of malignancy; however, discrepancies in imaging conditions may inhibit the efficacy of deep learning models. Numerous previous investigations have neglected this problem, frequently depending on deep features from a singular layer of an individual deep learning model. This study presents a new hybrid deep learning model that integrates discrete cosine transform (DCT) with multi-convolutional neural network (CNN) structures to improve the classification of skin cancer. Initially, DCT is applied to dermoscopic images to enhance and correct colour distortions in these images. After that, several CNNs are trained separately with the dermoscopic images and the DCT images. Next, deep features are obtained from two deep layers of each CNN. The proposed hybrid model consists of triple deep feature fusion. The initial phase involves employing the discrete wavelet transform (DWT) to merge multidimensional attributes obtained from the first layer of each CNN, which lowers their dimension and provides time–frequency representation. In addition, for each CNN, the deep features of the second deep layer are concatenated. Afterward, in the subsequent deep feature fusion stage, for each CNN, the merged first-layer features are combined with the second-layer features to create an effective feature vector. Finally, in the third deep feature fusion stage, these bi-layer features of the various CNNs are integrated. Through the process of training multiple CNNs on both the original dermoscopic photos and the DCT-enhanced images, retrieving attributes from two separate layers, and incorporating attributes from the multiple CNNs, a comprehensive representation of attributes is generated. Experimental results showed 96.40% accuracy after trio-deep feature fusion. This shows that merging DCT-enhanced images and dermoscopic photos can improve diagnostic accuracy. The hybrid trio-deep feature fusion model outperforms individual CNN models and most recent studies, thus proving its superiority. [ABSTRACT FROM AUTHOR]

Recently, there have been increasing attempts to apply computer vision technology to the coffee industry. However, most research has focused on classifying the quality or characteristics of the coffee beans. This paper explore relatively low-complexity computer vision algorithms that can evaluate the quality of crema through comparative index values of images taken after espresso extraction. Twelve types of coffee beans are prepared varying in variety, blend, and roast level, and four algorithms of two-dimensional discrete cosine transform, edge detection, clustering & variance and uniform rotational-invariant local binary pattern are applied to the espresso images of them. Through statistical analysis of the resulting images after applying the algorithms, it is examined whether they have the potential to serve as crema evaluation tools. As a result, the density of edge points obtained through the Canny edge detection algorithm is found to be useful in assessing the quality of the crema, regardless of the type of coffee beans. [ABSTRACT FROM AUTHOR]

A Geographic Information System (GIS) is a computer system for gathering, storing, transmitting, and presenting data related to positions on Earth's surface. This research aims to authenticate the GIS data during transmission via internet based on transform-based invisible watermarking. The proposed framework makes use of the singular value decomposition and discrete cosine transformation in the frequency domain. The proposed framework is evaluated on National DEM images obtained from the Geospatial Information Agency's (Badan Informasi Geospasial--BIG) geoportal, under several types of attacks. Two performance metrics (Peak to Signal Noise Ratio (PSNR) and Mean Square Error (MSE)) were considered for the evaluation of the security of the designed framework. Likewise, Normalized Correlation (NC) was computed to assess the robustness by calculating the similarity between the original and the extracted images. The experimental outcomes show that the extracted logos are readable even if they are altered, which guarantees that the received DEM data are authentic. [ABSTRACT FROM AUTHOR]

In recent years, location check-in on mobile components is a trending topic over social media. At the same time, hackers grasp the geographical position (geo-position) data that destruct the security of users. Hence, it is crucial to detect the originality of geo-position. A plethora of methods have been developed for geo-position spoofing identification that depends on geo-position data. Nonetheless, such techniques are incapable in terms of missing prior data or insufficient of large samples. To counterpart this issue, an effective model is invented to detect spoofing activity by Adaptive Honey Badger Spider Monkey Optimization_Deep residual Network (AHBSMO-based DRN). Here, neuro camera footprint refining is performed using Neuro Fuzzy filter and extracted footprint image obtained while considering the input and spoofed image are fused using Pearson correlation coefficient. Meanwhile, geo-tagged value of input image and spoofed image is also fused based on same Pearson coefficient. Finally, fusion is performed and then, spoofing detection is accomplished by comparing the Discrete Cosine Transform (DCT) foot print of two images to find if the input image is spoofed or not. Moreover, AHBSMO-based DRN model has gained outstanding outcomes in regard of accuracy of 0.921, True Positive Rate (TPR) 0 of 0.911, and False Positive Rate (FPR) of 0.136. [ABSTRACT FROM AUTHOR]

This study presents an approach that integrates compressed sensing technology with two-dimensional hyperchaotic coupled Fourier oscillator systems (2D-HCFOS) to address the challenge of slow encryption speeds in agricultural unmanned aerial vehicles (UAVs). The primary challenge in enhancing encryption speed lies in the limited capacity inherent in traditional chaotic-based systems and the computational complexity of their processes. The 2D-HCFOS utilizes a complex two-dimensional hybrid chaotic system, which significantly enhances the security of agricultural UAV image data. Notably, the image encryption process is performed on a personal computer connected to the drone, ensuring efficient processing. By integrating advanced Fourier series and nonlinear coupled oscillators, the model surpasses existing chaotic-based methods, improving both the pseudo-randomness and robustness of encryption. Additionally, incorporating Bonouille functions into the discrete cosine transform (DCT) domain results in a sparser measurement matrix, which is essential for efficient encryption on personal computers. The effectiveness of 2D-HCFOS in securely encrypting agricultural drone images has been rigorously validated through simulations and analytical evaluations using sophisticated row, rotation, and matrix encryption techniques. The improved security performance is further verified by comparative analysis. Compared with other models, the Lyapunov index of 2D-HCFOS is 15.1039, and the sample entropy is 2.4987, indicating that it possesses superior chaotic performance and encryption reliability. [ABSTRACT FROM AUTHOR]

Currently, security enhancement of biometric systems is an important issue that deserves consideration. This is attributed to the threats facing traditional recognition systems, which depend on Personal Identification Numbers (PINs) that can be stolen, easily. Utilization of original biometrics to access user services may lead to loss of the biometrics forever, if hacking attempts succeed in gaining access to the storage database of original templates. To address this concern and to avoid the utilization of original biometrics, we keep them away from being compromised through the utilization of cancelable biometric templates. This paper introduces a novel methodology for user authentication with multiple biometrics to generate distorted non-invertible cancelable templates to be stored in the database. The proposed framework begins with Discrete Cosine Transform (DCT) to achieve data compression in a multi-biometric scenario. After that, Double Random Phase Encoding (DRPE) is applied to increase the security level of the generated templates. Finally, an adaptive filter is used to induce an effect of whitening to generate the cancelable biometric templates. The generated patterns are uncorrelated due to the effect of encryption and adaptive filtering, which improves the security level against identity theft and provides good performance. Simulation results prove a good performance of the proposed cancelable biometric recognition framework with an Area under the Receiver Operating Characteristic curve (AROC) of 52.12% and an Equal Error Rate (EER) of 44.8462%. [ABSTRACT FROM AUTHOR]

Several medical consultations and examinations have been undertaken online since the Covid outbreak. However, when private data was communicated over the internet or uploaded to the cloud, medical information became more susceptible to security risks. Steganography is a technique that can be used to hide sensitive information within a cover image. This paper presents four improved algorithms to enhance steganography's performance in medical images. A full-size hidden image that is as huge as a cover image cannot be handled by previous methods, which is what the algorithmic design is meant to address. Several creative methods are presented, including the computation of Discrete Cosine Transform (DCT) coefficients based on scaled floating values, the addition of an adaptive compression matrix, and a new approach for systematically dispersing a concealed number of bits across multiple separate locations in the cover image. The results of the experiment showed a notable advancement over the earlier research. Our secret image size is substantially larger than the past studies, yet the structure similarity index matrix (SSIM) of the best reconstructed secret image is close to ideal, the peak signal-to-noise ratio (PSNR) and the payload capacity are higher than in the previous studies. This research is beneficial since it contributes to a medical application for enhancing the security of information concealed in chest X-ray images. Medical personnel can generate an image that conceals patient information in a secure manner. [ABSTRACT FROM AUTHOR]

The air temperature is a vital measuring variable widely applied in science and engineering as it is considered one vital part of the lives of plants and animals. From the point of view of time series analysis and data-driven modeling, changes in daily 24h air temperature can be described as one time-series data. Compared to relatively complicated models, simple, efficient and of even better performance are advantages of data-driven-based concise modeling methods, especially for embedded applications which usually have limited resources such as low RAM and low processing power. This study takes the perspective of data-driven modeling and time-series analysis to present one succinct and efficient solution based on discrete cosine transform (DCT)-extended modeling for the hourly air temperature forecast (HTF) in engineering and embedded applications. Characteristics of the DCT lie in optimal de-correlation and energy compaction. Orienting upon extended-DCT modeling, one concise least-squares (LS)-extended DCT predictive algorithm for the HTF is introduced. Then one Mbed-based embedded system design utilizing the proposed LS-extended DCT predictive algorithm as well as theoretical analyses and fast computation of arbitrary length DCT is presented. Verification results indicate the suitability of the proposed predictive algorithm. Only the temperature data without other parameters being straightly employed in forecast modeling is one major advantage of the proposed data-driven DCT-based HTF method, which benefits it to be suitable for engineering and embedded applications. [ABSTRACT FROM AUTHOR]

Most of the existing research on video watermarking schemes focus on improving the robustness of watermarking. However, in application scenarios such as judicial forensics and telemedicine, the distortion caused by watermark embedding on the original video is unacceptable. To solve this problem, this paper proposes a robust reversible watermarking (RRW)scheme based on multi-layer embedding in the video compression domain. Firstly, the watermarking data are divided into several sub-secrets by using Shamir's (t, n)-threshold secret sharing. After that, the chroma sub-block with more complex texture information is filtered out in the I-frame of each group of pictures (GOP), and the sub-secret is embedded in that frame by modifying the discrete cosine transform (DCT) coefficients within the sub-block. Finally, the auxiliary information required to recover the coefficients is embedded into the motion vector of the P-frame of each GOP by a reversible steganography algorithm. In the absence of an attack, the receiver can recover the DCT coefficients by extracting the auxiliary information in the vectors, ultimately recovering the video correctly. The watermarking scheme demonstrates strong robustness even when it suffers from malicious attacks such as recompression attacks and requantization attacks. The experimental results demonstrate that the watermarking scheme proposed in this paper exhibits reversibility and high visual quality. Moreover, the scheme surpasses other comparable methods in the robustness test session. [ABSTRACT FROM AUTHOR]

Due to the advancement in biomedical technologies, to diagnose problems in people, a number of psychological signals are extracted from patients. We should be able to ensure that psychological signals are not altered by adversaries and it should be possible to relate a patient to his/her corresponding psychological signal. As far as our awareness extends, none of the existing methods possess the capability to both identify and verify the authenticity of the ECG signals. Consequently, this paper introduces an innovative dual-layer data-embedding approach for electrocardiogram (ECG) signals, aiming to achieve both signal identification and authenticity verification. Since file name-based signal identification is vulnerable to modifications, we propose a robust watermarking method which will embed patient-related details such as patient identification number, into the medically less-significant portion of the ECG signals. The proposed robust watermarking algorithm adds data into ECG signals such that the patient information hidden in an ECG signal can resist the filtering attack (such as high-pass filtering) and noise addition. This is achieved via the use of error buffers in the embedding algorithm. Further, modification-sensitive fragile watermarks are added to ECG signals. By extracting and checking the fragile watermark bits, we can determine whether an ECG signal is modified or not. To ensure the security of the proposed mechanism, two secret keys are used. Our evaluation demonstrates the usefulness of the proposed system. [ABSTRACT FROM AUTHOR]

This paper studied the problem of multi-user blind signal separation (BSS) in wireless communications. The existing separation algorithms work on quadrature phase shift keying (QPSK). Through our work, two proposed algorithms were presented to enhance the BSS performance. The first proposed algorithm uses wavelet denoising to remove noise from the received signals in time domain. It adopts different modulation techniques such as minimum shift keying (MSK), QPSK, and Gaussian minimum shift keying (GMSK). Then several BSS algorithms such as independent component analysis (ICA), principle component analysis (PCA), and multi-user kurtosis (MUK) algorithms were implemented. The second proposed algorithm transferred the problem of BSS to transform domain and used wavelet denoising to reduce noise effect on received mixture. The BSS with Discrete Sine Transform (DST) and Discrete Cosine Transform (DCT) were investigated and compared to time domain performance. Minimum square error (MSE) and signal to noise ratio (SNR) were used as the evaluating metrics for comparison. Simulation results proved that in time domain, MUK with QPSK gave best performance and wavelet denoising was found to enhance the performance of BSS under all conditions. Signal separation in transform domain was found to give better performance than that in time domain due to the energy compaction process of these transforms and noise reduction due to their averaging effect. [ABSTRACT FROM AUTHOR]

The problem of solving the equivalent resistance between two points for resistor networks has important significance in physics. This paper mainly changes and rewrites the formula for calculating the resistance between two points of an unconventional m × n cylindrical resistor network with a zero resistor axis and any two left and right boundaries. To enhance the efficiency of calculating the equivalent resistance between two points, Chebyshev polynomials and hyperbolic cosine functions are employed to represent the new formula. And in the inference process, the famous discrete cosine transform of the third kind (DCT-III) is used to process the matrix. We give the equivalent resistance formula for several special cases, and display them by a three-dimensional graph. Subsequently, the calculation efficiency of the original formula and the rewritten formula are compared. At the end of the paper, a heuristic algorithm suitable for robot path planning on cylindrical environment is proposed. [ABSTRACT FROM AUTHOR]

Developing efficient and secure image encryption techniques for transmitting multiple images has become crucial due to the inadequacy of single-image encryption techniques in handling the increasing volume of big data over unprotected networks. This paper introduces a novel multiple-image encryption (MIE) technique that utilizes mixed image elements in conjunction with the RSA cryptosystem, fractional discrete cosine transform (FrDCT), and Henon chaotic map (HCM). To encrypt k images together, the first step involves making three big images B 1 , B 2 and B 3 from these k images using matrix theory. The three images B 1 , B 2 and B 3 are converted into three indexed images I 1 , I 2 , and I 3 by extracting their color maps. Indexed images I 1 , I 2 and I 3 are then treated as a single RGB image by taking I 1 as a red (R) component, I 2 as a green (G) component and I 3 as a blue (B) component. The RSA cryptosystem is then applied on each component individually, followed by FrDCT and HCM to enhance security and key space. The resulting encrypted image is a single-channel real-valued image that is easy to display, store, and transmit over an unsecured network. The suggested technique offers multi-layer security in frequency, time and coordinate domains. Private keys, their arrangements and parameter positions are critical for decryption. Simulation analysis supports the robustness and effectiveness of the proposed technique. The sensitivity analysis demonstrates its extreme sensitivity towards private keys and their arrangement. Statistical analysis, including measures such as MSE, SSIM, PSNR, NPCR, UACI, entropy analysis, correlation coefficient, histogram analysis, computational complexity and comparison analysis confirm the effectiveness and viability of the introduced algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a method for the classification of anomalous heartbeats from compressed ECG signals is proposed. The method operating on signals acquired by compressed sensing is based on a feature extraction stage consisting of the evaluation of the Discrete Cosine Transform (DCT) coefficients of the compressed signal and a classification stage performed by means of a set of k-nearest neighbor ensemble classifiers. The method was preliminarily tested on five classes of anomalous heartbeats, and it achieved a classification accuracy of 99.40%. [ABSTRACT FROM AUTHOR]

The current 3D-ConvNet-based action recognition algorithms generally use GAP to compress feature information. However, it leads to issues of information loss, redundancy, and network overfitting. To address these issues and enhance the retention of high-level semantic information extracted by the convolutional layer, this paper proposed an action recognition al- gorithm based on GFDP. Firstly, DCT shows that GAP is a special case of feature decomposition in the frequency domain. Therefore, the algorithm introduced more frequency components to increase the specificity between feature channels and reduce the information redundancy after information compression. Secondly, to better suppress the overfitting problem, the algorithm introduced the batch normalization strategy to the convolutional layer and extended it to the fully connected layer of the action recognition model with ERB-Res3D as the skeleton to optimize the data distribution. Finally, this paper verified the proposed method on the UCF101 dataset. The results reveals that the model's computational load is 3.5 GFlops, with 7.4 million para- meters. The final recognition accuracy improved by 3.9% based on the ERB-Res3D model and 17.4% based on the original Res3D model. This improvement effectively achieves more accurate behavior recognition results. [ABSTRACT FROM AUTHOR]

A new efficient nonparametric estimator for Toeplitz covariance matrices is proposed. This estimator is based on a data transformation that translates the problem of Toeplitz covariance matrix estimation to the problem of mean estimation in an approximate Gaussian regression. The resulting Toeplitz covariance matrix estimator is positive definite by construction, fully data driven and computationally very fast. Moreover, this estimator is shown to be minimax optimal under the spectral norm for a large class of Toeplitz matrices. These results are readily extended to estimation of inverses of Toeplitz covariance matrices. Also, an alternative version of the Whittle likelihood for the spectral density based on the discrete cosine transform is proposed. [ABSTRACT FROM AUTHOR]

Medical image encryption has currently drawn particular attention because of the sensitive nature of medical data, the lack of efficient image encryption employing innovative encryption methods. To enhance the encryption of medical images, a number of encryption approaches were suggested and created. This research proposes a hyperchaotic map as well as a deep learning (DL) based selective medical image encryption technique. Initially, the 3D_Att_ResU-Net segments more sensitive region of interest (ROI) which is the brain tumours. The process continues with a hyperchaotic map for encrypting the ROI of plain medical image. On the other hand, the region of non interest (RONI) is compressed based on discrete cosine transform (DCT). The encrypting of ROI as well as compression of the background could provide the best possible balance between encryption performance and security. The samples for the simulation included a variety of common medical image types as well as a few normal images. The outcomes have been analysed using standard image cryptanalysis techniques. The findings demonstrated that cipher-images had uniformly distributed histogram, high information entropy, low correlation between neighbouring pixels, and good visual quality. The method has a large key space as well as low time complexity, yet it is sensitive to the plain image and the initial key. Interestingly, it has been found that the values of NPCR and UACI are higher than 99.75% and 36. 53%, respectively besides large key space registered as an order of 2339. The entropy of the Region of Interest (ROI) is roughly estimated to be 8, which is significantly represent a better security for the current system. The estimated encryption time required for the ROI of size 8.09 KB is 0.3s and to compress the RONI of size 73.81 is 1.02s while the total time required to encrypt whole image of size 81.9 is 2.4737s making it suitable for real time applications. The results depict that the current presented encryption scheme gives high security with less computational time and complexity. [ABSTRACT FROM AUTHOR]

For secure image communications, the Internet must be shielded against unauthorized acquisition or malevolent access. The need for image encryption methods with enough capacity and good efficiency is growing due to the current situation. The purpose of this research work is to solve the problems in today's communication security using a proposed image encryption algorithm. The proposed image encryption system includes the combination of a chaotic system, compressive sensing and Hyper optimization algorithm. The initial values of chaos are extracted using the SHA512. Discrete Wavelet Transform (DWT) is applied to sparse image pixels. The image is shuffled using a FAN transform with a circular map. Next, the image is divided into blocks to facilitate the application of block compressive sensing that utilized the Hadamard measurement matrix. These blocks are masked as one part to quantify the pixels. The logistic map is improved by a hybrid transform which is the combination of Discrete Cosine Transform, Arnold Transform and Discrete Wavelet Transform (CAW). The image is finally scrambled using a Circular Map with a Hyper Optimization that combines two meta-heuristics algorithms namely GWO and COOT. According to simulation experiment findings and security assessments, the algorithm was extremely resilient to differential, statistical, and interference assaults. Based on the experimental results, it was found that the average rate of PSNR was 33.8189, the rate of entropy was 7.99454, the average rate of SSIM was 0.96144, the rate of UACI was 99.62892 and the average rate of NPCI was 33.50304. The results showed that it is an effective method of encryption and strong enough against various types of attacks. [ABSTRACT FROM AUTHOR]

Digital watermarking conceals data within host images to safeguard against unauthorized distribution of multimedia content. It offers content protection and anti-piracy measures, maintaining content quality by embedding invisible information. This process involves inserting and extracting watermarks. We introduce a robust algorithm, combining discrete Haar wavelet transform (DHWT) and discrete cosine transform (DCT), yielding effective watermarking with high resistance to extraction without data loss. This combination of transforms represents a hybrid approach that we call HyDHWCT in this work. Evaluations reveal our approach's superior accuracy compared to state-of-the-art methods. Our hardware watermarking solution excels in robustness and energy efficiency, even under diverse attack scenarios. FPGA and ASIC assessments show our HyDHWCT's exceptional area and power performance, with the algorithm achieving a lossless watermark extraction (NC = 1), outperforming prior methods in accuracy-quality, and energy-, and area-savings (approximately 2.621 × and 1.174 × , respectively). Accuracy-quality results confirm a perfect extraction rate (NC = 1), ensuring 100% accuracy in watermark extraction. [ABSTRACT FROM AUTHOR]

Motion Estimation (ME) and the two-dimensional (2D) discrete cosine transform (2D-DCT) are both computationally expensive parts of HEVC standard, therefore real-time performance of the HEVC may not be free from glitches. To address this issue, this study deploys the graphics processing units (GPUs) to perform the ME and 2D-DCT tasks. In this concern, authors probed into four levels of parallelism (i.e., frame, macroblock, search area, and sum of the absolute difference (SAD) levels) existing in ME. For comparative analysis, authors involved full search (FS), test zone search (TZS) of HEVC, and hierarchical diamond search (EHDS) ME algorithms. Similarly, two levels of parallelism (i.e., macroblock and sub-macroblock) are also explored in 2D-DCT. Notably, the least computationally complex multithreaded Loeffler DCT algorithm is utilized for computing 2D-DCT. Experimental results show that ME processing task corresponding to 25 frames, with each frame of size (3840×2160) pixels, is accomplished in 0.15 seconds on the NVIDIA GeForce GTX 1080, whereas the 2D-DCT task along with the image reconstruction and differencing corresponding to 25 frames took 0.1 seconds. Collectively, both ME and 2D-DCT tasks are processed in 0.25 seconds, which still leaves enough room for the encoder's remaining parts to be executed within one second. Due to this enhancement, the resultant encoder can safely be used in real-time applications. [ABSTRACT FROM AUTHOR]

Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model's capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE. [ABSTRACT FROM AUTHOR]

The discrete cosine transform (DCT) is widely used for image and video compression. Lossy algorithms such as JPEG, WebP, BPG and many others are based on it. Multiple modifications of DCT have been developed to improve its performance. One of them is adaptive DCT (ADCT) designed to deal with heterogeneous image structure and it may be found, for example, in the HEVC video codec. Adaptivity means that the image is divided into an uneven grid of squares: smaller ones retain information about details better, while larger squares are efficient for homogeneous backgrounds. The practical use of adaptive DCT algorithms is complicated by the lack of optimal threshold search algorithms for image partitioning procedures. In this paper, we propose a novel method for optimal threshold search in ADCT using a metric based on tonal distribution. We define two thresholds: pm, the threshold defining solid mean coloring, and ps, defining the quadtree fragment splitting. In our algorithm, the values of these thresholds are calculated via polynomial functions of the tonal distribution of a particular image or fragment. The polynomial coefficients are determined using the dedicated optimization procedure on the dataset containing images from the specific domain, urban road scenes in our case. In the experimental part of the study, we show that ADCT allows a higher compression ratio compared to non-adaptive DCT at the same level of quality loss, up to 66% for acceptable quality. The proposed algorithm may be used directly for image compression, or as a core of video compression framework in traffic-demanding applications, such as urban video surveillance systems. [ABSTRACT FROM AUTHOR]

The Discrete Cosine Transform (DCT) is fundamental to high-capacity data hiding schemes due to its ability to condense signals into a few significant coefficients while leaving many high-frequency coefficients relatively insignificant. These high-frequency coefficients are often replaced with secret data, allowing for the embedding of many secret bits while maintaining acceptable stego signal quality. However, because high-frequency components still affect the stego signal's quality, preserving their structure is beneficial. This work introduces a method that maintains the structure of high-frequency DCT components during embedding through polynomial modeling. A scaled-down version of the secret signal is added to or subtracted from the polynomial-generated signal to minimize the error between the cover signal and the polynomial-generated signal. As a result, the stego image retains a structure similar to the original cover image. Experimental results demonstrate that this scheme improves the quality and security of the stego image compared to current methods. Notably, the technique's robustness is confirmed by its resistance to detection by deep learning methods, as a Convolutional Neural Network (CNN) could not distinguish between the cover and stego images. [ABSTRACT FROM AUTHOR]

Addressing the intricate task of aircraft pose estimation in challenging airport environments, we introduce a dual attention module (DAM) inspired by the human visual system. Comprising two innovative sub-modules, the Inverse Parallel Multi-Spectral Channel Attention Module (IPMS-CAM) and the Gaussian Spatial Attention Module (G-SAM), the DAM collaboratively captures global aircraft features. Specifically, the IPMS-CAM employs the discrete cosine transform and integrates attention mechanism to adapt to different levels of visual cues, effectively addressing the "where to look" challenge within complex airport scenes. In contrast, the G-SAM emulates the spatial attention process of the cerebral cortex, effectively tackling the "what to see" problem, and thereby emphasizing pertinent information while intelligently filtering out extraneous details. Our proposed approach is rigorously evaluated through experiments on two distinct airport surface datasets. The results substantiate the compelling competitiveness of our method when juxtaposed against prevailing attention mechanism techniques. Our approach achieves a noteworthy performance advancement of approximately 3 % over the baseline method, all while incurring negligible computational overhead. [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Advanced internet technology allows unauthorized individuals to modify and distribute digital images. Image watermarking is a popular solution for copyright protection and ensuring digital security. This research presents an embedding scheme with a set of conditions using non-subsampled Shearlet transform (NSST), lifting wavelet transform (LWT), and discrete cosine transform (DCT). Red and green channels are employed for the embedding process. The red channel is converted by NSST-LWT. The low-frequency area (LL) frequency is then split into small blocks of 8×8, each partition block is then transformed by DCT. The DCT coefficient of (3,4), (5,2), (5,3), (3,5), called matrix M1, and (2,5), (4,3), (6,2), (4,4), called matrix M2 are selected for singular value decomposition (SVD) process. With a set of conditions, the watermark bits are incorporated into those singular values. The green channel is cropped to get the center image before splitting into 4×4 pixels. The block components are then selected based on the least entropy value for the embedding regions. The selected blocks are then computed using LWT-SVD. A set of conditions for U(1,1) and U(2,1) are used to incorporate the watermark logo. The experimental findings reveal that the suggested scheme achieves high imperceptibility and resilience under various evaluating attacks with an average peak signal-to-noise ratio (PSNR) and correlation value (NC) values are up to 43.89 dB and 0.96, respectively. [ABSTRACT FROM AUTHOR]

Steganography is a technique for hiding secret messages while sending and receiving communications through a cover item. From ancient times to the present, the security of secret or vital information has always been a significant problem. The development of secure communication methods that keep recipient-only data transmissions secret has always been an area of interest. Therefore, several approaches, including steganography, have been developed by researchers over time to enable safe data transit. In this review, we have discussed image steganography based on Discrete Cosine Transform (DCT) algorithm, etc. We have also discussed image steganography based on multiple hashing algorithms like the Rivest–Shamir–Adleman (RSA) method, the Blowfish technique, and the hash-least significant bit (LSB) approach. In this review, a novel method of hiding information in images has been developed with minimal variance in image bits, making our method secure and effective. A cryptography mechanism was also used in this strategy. Before encoding the data and embedding it into a carry image, this review verifies that it has been encrypted. Usually, embedded text in photos conveys crucial signals about the content. This review employs hash table encryption on the message before hiding it within the picture to provide a more secure method of data transport. If the message is ever intercepted by a third party, there are several ways to stop this operation. A second level of security process implementation involves encrypting and decrypting steganography images using different hashing algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel watermarking method in order to enhance the security in biometric voice/speech transmission systems basing on sub-sampling, discrete cosine transform (DCT) and adaptive watermark embedding. To improve imperceptibility, we use sub-sampling and adaptive embedding in DCT high energy coefficients. We employ a significant watermark represented in a biometric unique fingerprint. Introducing bits, only in high energy fraction, provides us with further agreeing element depicted by decreasing running time, both in embedding and extraction processes, which can help minimizing hardware consumption. Achieved results reveal the stability and flexibility of our proposed scheme and confirm its robustness against additive noise. In addition, we enhanced our previous published approaches and the expected limitations of our proposed model will appear in the hardware implementation where the attacks will be more considered. Another limitation will be the number of quantization bits used for high energy DCT coefficients. All these may affect the speaker secured identification and verification system. [ABSTRACT FROM AUTHOR]

Radiologists employ mammograms for the detection of breast cancer in patients, particularly as breast cancer exhibits higher incidence rates in women. Early identification of breast cancer significantly reduces the risk of mortality. Mammograms serve as a valuable imaging technique for the early detection of breast cancer. However, accurately characterizing breast cancer images poses a considerable challenge. A recent research study introduced an innovative algorithm for Mammogram Pectoral Muscle Removal, leveraging Entropy-Based Fuzzy C-means clustering and Classification using RMCNN (Root Mean Squares Convolutional Neural Network). The process involves extracting and pre-processing the input breast image from the dataset through Gaussian filtering. Subsequently, pectoral muscle removal is achieved via entropy-based fuzzy C-Means clustering, followed by DCT (Discrete Cosine Transform) and DWT (Discrete wavelet transforms) feature extraction. The Root Mean Square Value-based Convolutional Neural Network classifier effectively clusters mammography images into normal, malignant, and benign classes, achieving an impressive 99.45% accuracy far better than existing methods with accuracies of 93%, 89%, and 60%. [ABSTRACT FROM AUTHOR]

Digital watermarking is an effective technique for image copyright protection. Many digital image watermarking algorithms are sensitive to geometric distortions. These distortions make it difficult to detect and extract watermarks. In this study, a robust color watermarking algorithm combining the Radon transform and DCT transform is proposed. First, the color carrier image is converted to the YUV color space from RGB, and the U component of YUV is transformed to the Radon domain using the Radon transform. Then, a two-dimensional 8 × 8 discrete cosine transform (2D-DCT) is carried out on the selected blocks of the Radon domain, and some fixed midfrequency coefficients are selected to embed watermark information. Moreover, the Arnold transform is applied to encrypt the watermarks and the random permutation function to scramble the embedding positions. Finally, the watermark embedding and blind extraction processes are completed by modifying the midfrequency coefficients using the presented rules. A series of simulation experiments show that the watermark algorithm has high imperceptibility and good robustness to various attacks. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices (FDCM) with discrete cosine transform (DCT) operation. After that, a two-dimensional (2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually, under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc. [ABSTRACT FROM AUTHOR]

This research paper presents a modified blind and robust image watermarking scheme that combines dual-tree complex wavelet transform (DTCWT) and discrete cosine transform (DCT) domains. A key challenge for researchers is to determine the optimal locations for embedding watermarks in the low-frequency coefficients of the hybrid domains, ensuring both imperceptibility and security. To identify the most effective sequence for the watermark embedding process, a differential approach is implemented on two correlated DCT-transformed vectors derived from DTCWT wavelet low-frequency coefficients. The watermark data does not need to be extracted from the original image. The proposed scheme aims to assess the efficiency improvement against various image processing attacks. We utilized fifteen grayscale images from the UCI-sipi image database, each with a size of 512 × 512 pixels, to evaluate the proposed scheme. The experimental results demonstrate that our scheme outperforms existing schemes in common image attacks such as geometric attacks, compression, filtering, and noise addition. [ABSTRACT FROM AUTHOR]

The growing use of the internet results in significant difficulties for the copyright protection of images. To resolve the copyright problem, a scheme is proposed that utilizes the advantages of Zigzag scanning (ZS), Schur decomposition (SD), Discrete cosine transform (DCT) and Redundant discrete wavelet transform (RDWT). In the proposed scheme, Henon map is applied on the watermarked image for its secure transfer and further enhancement of the security. Medical image security assurance is the need of the hour. Therefore, ultrasound images of the liver of six different patients are taken for experimental result analysis. The use of Singular value decomposition (SVD) is avoided here due to its false positive problem. RDWT is used to split the input ultrasound liver image into different sub-bands. Then DCT is applied on the suitable sub-band to improve the robustness of the presented work. The ZS is used to avoid artifacts which may occur at higher value of the gain factor. SD and RDWT are applied to the watermark logo (MNNIT logo) and selective pixel adding is used to embed the watermark logo into the host image. The authentication process is done by using binary robust invariant scalable keypoints (BRISK) features. The presented scheme perform satisfactorily against various attacks such as Histogram equalization, Region of interest (ROI) filtering, Cropping, Scaling, Rotation, Average and Median filtering attacks, Salt and Pepper, Gaussian noise attack, Flipping, etc. The proposed scheme shows better performance as compared to existing schemes in terms of various performance parameters such as Peak signal-to-noise ratio (PSNR), Structural similarity index measurement (SSIM) and Normalized correlation coefficient (NCC). [ABSTRACT FROM AUTHOR]

Visual question answering (VQA) is a pivotal topic at the intersection of computer vision and natural language processing. This paper addresses the challenges of linguistic bias and bias fusion within invalid regions encountered in existing VQA models due to insufficient representation of multi-modal features. To overcome those issues, we propose a multi-feature enhancement scheme. This scheme involves the fusion of one or more features with the original ones, incorporating discrete cosine transform (DCT) features into the counterfactual reasoning framework. This approach harnesses finegrained information and spatial relationships within images and questions, enabling a more refined understanding of the indirect relationship between images and questions. Consequently, it effectively mitigates linguistic bias and bias fusion within invalid regions in the model. Extensive experiments are conducted on multiple datasets, including VQA2 and VQA-CP2, employing various baseline models and fusion techniques, resulting in promising and robust performance. [ABSTRACT FROM AUTHOR]

Copy detection is a key task of image copyright protection. This article proposes a robust image hashing algorithm by CP decomposition and discrete cosine transform (DCT) for copy detection. The first contribution is the third-order tensor construction with low-frequency coefficients in the DCT domain. Since the low-frequency DCT coefficients contain most of the image energy, they can reflect the basic visual content of the image and are less disturbed by noise. Hence, the third-order tensor construction with the low-frequency DCT coefficients can ensure robustness of our algorithm. Another contribution is the application of the CP decomposition to the third-order tensor for learning a short binary hash. As the factor matrices learned from the CP decomposition can preserve the topology of the original tensor, the binary hash derived from the factor matrices can reach good discrimination. Lots of experiments and comparisons are done to validate effectiveness and advantage of our algorithm. The results demonstrate that our algorithm has superior classification and copy detection performances than several baseline algorithms. In addition, our algorithm is also better than some baseline algorithms with regard to hash length and computational time. [ABSTRACT FROM AUTHOR]

Wireless image sensor networks (WISNs) are widely applied in wildlife protection as they present a better performance in remote, real-time monitoring. However, traditional WISNs suffer from the limitations of low processing capability, power consumption restrictions, and narrow transmission bandwidth, which leads to a shorter working lifetime of the monitoring system when transmitting the wildlife monitoring image with high resolution. We propose a high-efficiency distributed image compression coding method based on soft threshold iteration and quantitative perception for wildlife monitoring images to rationally assign the electricity resource. Specifically, we first utilize the histogram contrast algorithm to detect the saliency object region from the original samples and use it to generate the mask image of the wildlife region. After the mask image is obtained, the distributed image compression coding method is utilized to transmit the wildlife image, in which the saliency image region is directly transmitted as a cluster head to ensure the transmission efficiency of the wildlife region. Then the background region is assigned to the other four monitoring nodes at the same level for processing and transmission, extending the lifetime of the network. Furthermore, the soft threshold iteration algorithm is utilized to encode the image data; this is suitable for WISNs. The experimental results on our own wildlife dataset show improvements of 7.47 and 9.06% for the peak signal-tonoise ratio and 16.98 and 19.50% for the structural similarity index on the reconstructed image compared with those of the discrete cosine transform and embedded zerotree wavelets algorithms, respectively. Compared with the multihop and single-hop transmission methods, the power consumption is reduced by 29.96 and 40.84%, respectively. The results of this study indicate that the WISN technique can provide feasible solutions for the intelligent monitoring of forest biological resources. [ABSTRACT FROM AUTHOR]

Face recognition is one of the most important research topics in computer vision. Indeed, the face is an important means of communication with humans and it is highly needed for daily contact. Face recognition technology is applied in many biometric applications such as security, video surveillance, access control systems, and forensics. In this technology, hashing has recently made encouraging progress due to its fast retrieval speed and low storage cost. In this work, we propose an effective face recognition framework based on hashing functions. It attempts to leverage a cascaded architecture with two stages of analyzing different visual information based on image hashing. Specifically, we first introduce a filter to overlook a large number of dissimilar identities in terms of local visual information. Similar identities are found quickly through random independent hash functions inspired by Locality Sensitive Hashing (LSH). Next, we further refine candidates and recognize the most similar identities according to global visual information. The global feature is obtained by hashing each face into a high-quality binary feature space using Discrete Cosine Transform (DCT) coefficients. The proposed method is evaluated on three well-known and one combined face dataset. The obtained results, and the provided face recognition application program, demonstrate that the proposed framework improves the recognition rate and significantly reduces recognition time. [ABSTRACT FROM AUTHOR]

With the advancements in multimedia communications, it has become apparent that there is a bad need to perform image communication with a confidence guarantee. We need to have a guarantee of image integrity at the receiver. Towards this objective, a proposed framework is presented in this paper. This framework comprises self-signature embedding in the transmitted images. The signatures are extracted from image blocks in the Discrete Cosine Transform (DCT) domain and embedded in other blocks in the same domain with a certain weight to avoid deteriorating the resulting image quality. A verification process is performed at the receiver to check whether the content has been modified or not. In addition, image watermarking is also used with DCT and Discrete Wavelet Transform (DWT) algorithms for authentication or verification. Moreover, Double Random Phase Encoding (DRPE) algorithm is used to secure the content of transmitted images. Then, to get a higher level of security for transmitted images, a hybrid technique depending on DCT-based signature embedding and the DRPE algorithm is presented. [ABSTRACT FROM AUTHOR]

Lossy compression of remote-sensing images is a typical stage in their processing chain. In design or selection of methods for lossy compression, it is commonly assumed that images are noise-free. Meanwhile, there are many practical situations where an image or a set of its components are noisy. This fact needs to be taken into account since noise presence leads to specific effects in lossy compressed data. The main effect is the possible existence of the optimal operation point (OOP) shown for JPEG, JPEG2000, some coders based on the discrete cosine transform (DCT), and the better portable graphics (BPG) encoder. However, the performance of such modern coders as AVIF and HEIF with application to noisy images has not been studied yet. In this paper, analysis is carried out for the case of additive white Gaussian noise. We demonstrate that OOP can exist for AVIF and HEIF and the performance characteristics in it are quite similar to those for the BPG encoder. OOP exists with a higher probability for images of simpler structure and/or high-intensity noise, and this takes place according to different metrics including visual quality ones. The problems of providing lossy compression by AVIF or HEIF are shown and an initial solution is proposed. Examples for test and real-life remote-sensing images are presented. [ABSTRACT FROM AUTHOR]