INFORMATION SYSTEM SECURITY USING THE DISCRETE COSINE TRANSFORM (DCT) METHOD

هدف از انجام این پژوهش، تعیین بهینه رخساره‌های پتروفیزیکی از روی داده‌های نموداری چاه است. با استفاده از روش دسته‌بندی خودکار K نزدیک­ترین همسایه می‌توان رخساره‌های پتروفیزیکی را تعیین کرد؛ اما نه به­صورت بهینه. برای تعیین بهینه رخساره‌ها، روش K نزدیک­ترین همسایه با روش‌های تحلیل سریع مؤلفه­های مستقل و تبدیل کسینوسی گسسته تلفیق می‌شود که این امر موجب افزایش نرخ موفقیت روش K نزدیک­ترین همسایه و تعیین بهینه رخساره‌های پتروفیزیکی می‌شود که به ­دنبال آن می‌توان مدل‌سازی و توصیف مخازن هیدروکربنی را انجام داد. پژوهش در دو مسیر متفاوت انجام می‌شود. در مسیر اول، روش تحلیل سریع مؤلفه­های مستقل روی داده اعمال‌ و سپس توسط روش K نزدیک­ترین همسایه دسته‌بندی می‌شود و در مسیر دوم روش‌های تحلیل سریع مؤلفه­های مستقل و تبدیل کسینوسی گسسته روی داده‌ها اعمال و سپس توسط روش K نزدیک­ترین همسایه دسته‌بندی می‌شود. در پایان نرخ موفقیت دسته‌بندی توسط K نزدیک­ترین همسایه در هر دو مسیر به­ منظور تعیین بهینه رخساره‌های پتروفیزیکی مورد ارزیابی قرار می‌گیرد. ارزیابی انجام‌ شده نشان می‌دهد که اعمال روش مسیر دوم روی داده‌ها به‌طور معنی‌داری نرخ موفقیت دسته‌بندی توسط روش K نزدیک­ترین همسایه را افزایش می‌دهد که این امر موجب تعیین بهینه رخساره‌های پتروفیکی می‌شود که همان هدف از انجام این پژوهش است. داده‌های مورد استفاده، نمودارهای صوتی (Sonic Log)، پرتوی گاما (Gamma Ray)، چگالی (FDC یا RHOB)، تخلخل نوترون (CNL یا NPHI) و نگارهای القایی ژرف (ILD) از میدان نفتی مارون در جنوب کشور ایران هستند.

WITHDRAWN: Contrasting the performance metrics of discrete transformations on digital image steganography using artificial intelligence

A novel technique is proposed to reduce the computational complexity of discrete cosine transform (DCT)-based video encoders. The proposed method merges the DCT and quantization into a single procedure, which is referred to as the novel quantized DCT (NQDCT), such that the DCT output does not need to be explicitly quantized. Thus, a lot of computations related to quantization can be saved. The video encoder's performance using NQDCT is evaluated by comparing it with those using the traditional separate DCT and quantization method and the quantized DCT (QDCT) method. Simulation results demonstrate that the proposed NQDCT outperforms the other two methods in improving the real-time performance for video encoding.

An efficient coding method using a three-dimensional discrete cosine transform (DCT) for still images is presented. This is an extended version of the traditional DCT coding method. The adaptive application of the three-dimensional DCT to each sub-block makes the coding more efficient than the other DCT methods.

The technique used to hide copyright information (watermark) into the digital audio signal is termed as audio watermarking. Audio watermarking is an excellent approach to provide a solution to mitigate challenges that occur from easy copying and distribution of audio files that are being downloaded or uploaded through the web. The audio watermarking algorithms proposed earlier were implemented by image or binary logo or a unique pattern as watermark. To tackle the difficulty of online musical piracy, unethical attempt to alter purposefully copyrighted audio data and unauthorized distribution and tampering of audio files an efficient audio watermarking method is suggested here. In this method the copyright information (watermark) which is an audio signal of shorter length is imperceptibly added into the original audio signal with greater duration. Here two audio watermarking methods using DCT (Discrete Cosine Transform) and then with DWT-SVD (Discrete Wavelet Transform — singular value decomposition) methods are proposed with audio signal encrypted with secure hash algorithm as watermark. A comparative study on performance evaluation of both DCT and DWT-SVD methods are made in terms of PSNR (Peak-Signal to Noise Ratio) and MSE (Mean Square Error). The results show that transparency is best for each methods and DWT-SVD method is more efficient against signal attacks. The elementary notion of this document is to provide a beneficial and satisfactory method for secure audio file transmission by providing copyright protection.

The classification of mango fruit ripeness levels is currently predominantly done manually, which unfortunately has several drawbacks. One of the primary shortcomings is the lack of consistency in accuracy, often resulting in differences among operators conducting the sorting. On the other hand, in image classification processes, a combination of the Discrete Cosine Transform (DCT) and Convolutional Neural Network (CNN) methods is utilized. DCT is a technique commonly used in image processing, especially for image pictures. In this research, there is a proposal to merge the Discrete Wavelet Transform method with the Convolutional Neural Network (CNN). Currently, CNN is one of the methods that provides the most significant results in image recognition. CNN attempts to mimic the image recognition system in the human brain, particularly the visual cortex, allowing it to efficiently process image information. The DCT method is used to transform image data into a frequency image form, which is subsequently employed in feature extraction in the Deep Neural Networks classification method. The research results indicate that the combined method of Discrete Cosine Transform and Convolutional Neural Network achieves the highest accuracy rate of 93.33% in classifying mango fruit ripeness levels. This outcome demonstrates significant potential for automating the mango ripeness classification process with high accuracy, overcoming the inconsistencies associated with manual approaches.

One of the most important problems in pattern recognition is texture-based image classification. In this paper, the combination of Discrete Cosine Transform (DCT) and Gray Level Co-Occurrence Matrix (GLCM) methods for feature extraction was proposed. The attributes extracted from DCT method were mean and variance, while the attributes extracted from GLCM method were energy and entropy. Adaptive Neuro-Fuzzy Inference System (ANFIS) was used as a classifier. The classifier model was trained using 50% of the texture images and remaining images were used for testing. Four classes of texture images were downloaded from KTH-TIPS (Textures under varying Illumination, Pose and Scale) image database, three of which were used in each experiments thus there were four data combination. The best data testing accuracy result towards textures of crumpled aluminium foil, corduroy, and orange peel is 98.3%, which is 1.6% better than one hidden-layer feed forward neural network classifier. In average, testing accuracy result of ANFIS excelled one hidden-layer feed forward neural network with 93.7% over 90.4%.

A hybrid compression method for the NMR data based on window averaging and Discrete Cosine Transform

In the contemporary era, safeguarding vast amounts of sensitive data and ensuring secure communication are paramount concerns, especially given the prevalence of insecure networks. The fields of cryptography and steganography have emerged as pivotal tools in addressing these challenges, attracting significant scientific attention due to their established effectiveness. This paper proposes a sophisticated double-layered security system for protecting textual data, initially utilizing 2-D Cellular automata (CA) to establish robust encryption protocols. The incorporation of the Von Neumann neighborhood of 2-D CA enhances the generation of secure cryptographic keys, leveraging its complex and chaotic behavior to improve randomness and unpredictability. Subsequently, to enhance security, the discrete cosine transform (DCT) technique is integrated, facilitating discreet embedding of encrypted data into the least significant bit (LSB) of DCT coefficients. An exhaustive range of tests and evaluations, encompassing analysis of key space, complexity, performance metrics, avalanche effect, security attacks, peak signal-to-noise ratio (PSNR) and mean square error (MSE), substantiates the effectiveness and resilience of this multifaceted approach. The findings reveal a significant average avalanche effect of 76%, indicating resilience against cryptographic attacks. Furthermore, superior preservation of image quality is observed during encryption, as evidenced by improved PSNR and MSE values compared to alternative technique. The overall analyses validate the proposed method as a proficient solution for secure data encryption in cyberspace.

A comparative analysis of image steganography based on DCT algorithm and steganography tool to hide nuclear reactors confidential information

ABSTRACTA spectral discrete cosine transform (DCT) method was used to solve numerically the spatio-temporal nonlinear Cahn-Hilliard equation for a temperature-induced phase separation process of a polymer solution. The properties of a high molecular weight polystyrene solution were used in the model to reflect the behavior of a real world polymer system. Based on the value of the initial concentration, three different final morphologies of the phase separated system, granular, interconnected, and microcellular, were identified. It was shown that the initial concentration is the main factor to determine the high rate of the phase separation when the phase separation time is the primary desired parameter. The degree of phase separation, as a quantitative measure of the phase separation process, indicates the rate and amount of separated polymer material for practical applications. The model is capable of providing quantitative information of morphology evolution during phase separation processes for microstructure control purposes. The structure factor profile was extracted as a theoretical output that enables comparison with scattering experiments observations as the finger-print of phase separation morphological evolution. It is shown that the DCT method is a very suitable and feasible method for solution of the nonlinear partial differential equation of phase separation kinetics.

Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor that could lead to death. It is considered the most common pediatric cancerous brain tumor. Precise and timely diagnosis of pediatric MB and its four subtypes (defined by the World Health Organization (WHO)) is essential to decide the appropriate follow-up plan and suitable treatments to prevent its progression and reduce mortality rates. Histopathology is the gold standard modality for the diagnosis of MB and its subtypes, but manual diagnosis via a pathologist is very complicated, needs excessive time, and is subjective to the pathologists’ expertise and skills, which may lead to variability in the diagnosis or misdiagnosis. The main purpose of the paper is to propose a time-efficient and reliable computer-aided diagnosis (CADx), namely MB-AI-His, for the automatic diagnosis of pediatric MB and its subtypes from histopathological images. The main challenge in this work is the lack of datasets available for the diagnosis of pediatric MB and its four subtypes and the limited related work. Related studies are based on either textural analysis or deep learning (DL) feature extraction methods. These studies used individual features to perform the classification task. However, MB-AI-His combines the benefits of DL techniques and textural analysis feature extraction methods through a cascaded manner. First, it uses three DL convolutional neural networks (CNNs), including DenseNet-201, MobileNet, and ResNet-50 CNNs to extract spatial DL features. Next, it extracts time-frequency features from the spatial DL features based on the discrete wavelet transform (DWT), which is a textural analysis method. Finally, MB-AI-His fuses the three spatial-time-frequency features generated from the three CNNs and DWT using the discrete cosine transform (DCT) and principal component analysis (PCA) to produce a time-efficient CADx system. MB-AI-His merges the privileges of different CNN architectures. MB-AI-His has a binary classification level for classifying among normal and abnormal MB images, and a multi-classification level to classify among the four subtypes of MB. The results of MB-AI-His show that it is accurate and reliable for both the binary and multi-class classification levels. It is also a time-efficient system as both the PCA and DCT methods have efficiently reduced the training execution time. The performance of MB-AI-His is compared with related CADx systems, and the comparison verified the powerfulness of MB-AI-His and its outperforming results. Therefore, it can support pathologists in the accurate and reliable diagnosis of MB and its subtypes from histopathological images. It can also reduce the time and cost of the diagnosis procedure which will correspondingly lead to lower death rates.

There is a huge resource of data available on the internet which one can access without realizing that the person is even authorized to access it or not. The hard work of the original resource person can easily be compromised. Digital watermarking is an answer to overcome this issue. This paper introduces a hybrid of LSB and DCT techniques, LSB(Least Significant Bit) and DCT(Discrete Cosine Transformation). The paper studies the two techniques along with the proposed technique and a comparison is drawn between the three based on MSE and PSNR values obtained. The introduced hybrid technique performs better in comparison to DCT and LSB. The MSE calculated was 64% better than LSB and 60% better than DCT resultantly PSNR is also observed to be better than LSB and DCT.

Transform coding has been the focus of the research for image compression. In previous research, the Amplitude and Group Partitioning (AGP) coding scheme is proved to be a low complexity algorithm with higher performance, clearly one of the state-of-art transform coding techniques. However, the previous AGP is used along with the Discrete Cosine Transform (DCT) and the discrete wavelet transform. In this paper, a different transform, the Lapped Orthogonal Transform (LOT), replaces the DCT in conjunction with the AGP. This is the first time LOT and AGP have been combined in a coding method. The definition and design of the LOT are discussed. An objective metric to measure the performance of transform, coding gain, is calculated for both the DCT and the LOT. The LOT has slightly higher coding gain than the DCT. The principles of the LOT based AGP image codec (LOT-AGP) are presented and a complete codec, encoder and decoder, is implemented in software. The performance of the LOT-AGP is compared with other block transform coding schemes: the baseline JPEG codec and the DCT based AGP image codec (DCT- AGP) by both objective evaluation and subjective evaluation. The Peak Signal to Noise Ratio (PSNR) is calculated for these three coding schemes. The two AGP codecs are much better than the JPEG codec on PSNR, from about 1.7 dB to 3 dB depending on bit rate. The two AGP schemes have PSNR differences only to a small degree. Visually, the LOT-AGP has the best-reconstructed images among these three at all bit rates. In addition, the coding results of two other state-of-art progressive image codecs are cited for further comparison. One is the Set Partitioning in Hierarchical Trees (SPIHT) algorithm with a dyadic wavelet transform, and the other is Tran and Nguyen's method with the generalized LOT transform. The AGP coding and the adaptive Huffman entropy coding of LOT-AGP are less complex and the memory usage is smaller than in these two progressive codecs. Comparing these three codecs, i.e. the LOT-AGP and the two progressive codecs in PSNR small only small differences in PSNR. SPIHT has about 1 dB higher PSNR than the LOT-AGP and Tran and Nguyen's method for the test image Lena. For the test image Barbara, the PSNR of the LOT- AGP is about 0.5 dB higher than that of the SPIHT and 0.5 dB lower than that of Tran and Nguyen's method. This low- complexity and high performance codec may provide a new direction for the implementation of image compression.

Human iris is one of the most reliable biometrics because of its uniqueness, stability and noninvasive nature.Thus it has attracted the attention of biometrics based identification and verification research and development community.Iris recognition has been a fast growing, challenging and interesting area in real-time applications.A large number of iris recognition algorithms have been developed for decades.This paper presents a comparative study of the performance from the iris authentication using Discrete Cosine Transform (DCT), Haar wavelet transform and a proposed Column Mean Method based features.Here iris recognition is done using the image feature set extracted from DCT, Haar Wavelet transform and Column Mean.Analysis was performed with the mentioned methods, consisting of the False Acceptance Rate (FAR) and the Genuine Acceptance Rate (GAR).The proposed technique is tested on an iris image database having 384 images.The iris recognition systems that produce very low error rates were successfully designed.DCT and Column Mean Methods give better performance with the accuracy of 98.44% and Haar Wavelet Transform gives a performance with the accuracy of 97.66%.The proposed Column Mean Method is superior in having faster recognition rate.