Underflow Problems Research Articles

Towering Fractals

An exponential tower is obtained by repeatedly raising a number to itself, as in xˣ, x^{x^x}, and so on. When formulated as an iterative process, it may converge to a single value, diverge to infinity, cycle between multiple values, or wander forever. Representing this property graphically leads to a beautiful fractal object. In order to plot the fractal, a method was devised to handle problems of numerical overflow and underflow that appear during the computation.

Polyrate 2023: A computer program for the calculation of chemical reaction rates for polyatomics. New version announcement

Polyrate 2023: A computer program for the calculation of chemical reaction rates for polyatomics. New version announcement

Reversible data hiding scheme based on improved interpolation and three-in-one intelligent strategy

Reversible data hiding method combined with interpolation is a widely used technique in secretly transmitting large collections of information through images. However, this approach can be subjected to inaccurate prediction because of using a single prediction formula for different textures of complex images. We proposed a new method using a circular predictor interpolation (CPI) for improved accuracy performance, which adjusts the predictive radius adaptively for different image textures and takes the coefficient of distance into consideration to increase image load capacity and visual quality. Additionally, to reduce unnecessary image distortion under low load conditions, the first part of the intelligent strategy modifies the scanning sequence in images so that the secret data is preferentially embedded into the pixel with less deviation. Meanwhile, an interpolation-embedding synchronization was created as the second part to allow the interpolation domain and embedding domain to be adjusted synchronously in light of the data capacity to be embedded. In addition, the third part of the intelligent strategy is to take the original image as a benchmark for steganographic pixel value rectification to improve the overall image quality and the overflow or underflow problems could be solved in this process. The outcomes of the experiments indicate that the CPI and the intelligent strategy realize a high payload and play an important role in improving visual quality compared with six advanced schemes.

Encoding with Combination Orientation Technique for RDH in Dual Stego Images

Abstract In this paper, initially, message intensities are used to create two encoding tables: an index and a code sequence. The table is then updated with $n$ secret data encoded into it. If the code sequence of the second encoding table matches the code sequence of the preceding or succeeding half of the two encoding tables, the second encoding table is folded. The folding is done when maximum intensity occurs in the utmost succeeding part of the two encoding tables. Finally, using the combination orientation approach, the encoded indices are embedded in the stego images. In the extraction phase, using the two stego images and the encoding with the combination orientation method, the encoded indices are obtained. The decoding is performed to extract the secret message as well as the cover image and there is no occurrence of overflow and underflow problems. It has a high visual quality and a high embedding capacity. The proposed technique achieved a maximum $\mathrm{PSNR}$ of $49.49$dB and a $648\ 242$-bit embedding capacity. When compared to traditional techniques, the experimental results show that the proposed method outperforms them.

An adaptive high capacity reversible data hiding algorithm in interpolation domain

Reversible data hiding (RDH) algorithms using interpolation technology have the advantage of high capacity for single-layer embedding. However, existing algorithms usually embed secret data in the non-reference pixels based on simple additions without considering the properties of context-pixels, which results in that the visual quality of watermarked image is not optimal. To deal with this issue, a novel adaptive high capacity RDH algorithm is proposed. Firstly, a location-dependent weighted image interpolation method is proposed to improve the visual quality of the interpolated image. Then, the interpolated image is divided into overlapping blocks, which are sorted in an ascending order according to their standard deviations; secret data is preferentially embedded in the blocks with small deviation so that image quality is not distorted too much. Finally, to enhance embedding capacity as much as possible, the amount of secret data bits that can be embedded in non-reference pixels is calculated adaptively. We theoretically prove that the embedded secret data can be extracted correctly and there is no pixel overflow or underflow problem. Extensive experimental results showed that the proposed algorithm outperforms several state-of-the-art algorithms. In addition, our algorithm can resist steganalysis attacks, and demonstrated the effectiveness of the proposed algorithm.

Bone Marrow-Based Biomarkers for Predicting aGVHD Using Targeted RNA Next Generation Sequencing and Machine Learning

Bone Marrow-Based Biomarkers for Predicting aGVHD Using Targeted RNA Next Generation Sequencing and Machine Learning

Determining Clinical Course of Diffuse Large B-Cell Lymphoma Using Targeted Transcriptome and Machine Learning Algorithms

Determining Clinical Course of Diffuse Large B-Cell Lymphoma Using Targeted Transcriptome and Machine Learning Algorithms

Explicit-duration Hidden Markov Models for quantum state estimation

An explicit-duration Hidden Markov Model with a nonparametric kernel estimator of the state duration distribution is specified. The motivation comes from the physical problem of extracting the maximum information from an open quantum system subject to an external perturbation, which induces a change in the dynamics of the system. A nonparametric kernel estimator for discrete data is introduced, which is consistent and improves the estimates accuracy in presence of sparse data. To reconstruct the hidden dynamics, a Viterbi algorithm is used, which is robust against the underflow problem. Finite sample properties are investigated through an extensive Monte Carlo study showing that our formulation outperforms the original one both in small and in large samples.

Embedding in medical images with contrast enhancement and tamper detection capability

This paper presents an efficient data hiding technique capable of providing improved visual quality of watermarked images, besides having the ability to detect the tamper, if any. It is a spatial domain approach in which major emphasis is on improving the visual quality rather than increasing the PSNR or the embedding capacity. The medical images have been divided into Region of Interest (ROI) and Non-Region of Interest (NROI). Bringing out details that lie within the low dynamic range is very important in medical images for effective diagnosis. ROI being diagnostically critical region is enhanced using contrast stretching and subsequently, data is reversibly embedded into the peak bins of ROI. Only those peak bins are employed for reversible data embedding that have an adjacent empty bin to overcome the problem of overflow and underflow. In NROI, the uniform intensity and redundant information region, Least Significant Bit (LSB) embedding is employed for increasing the payload. For tamper detection, a fragile watermark has been embedded in the ROI. To evaluate the scheme various parameters like peak signal to noise ratio (PSNR), No-Reference Quality Metric for contrast-distorted images (NR-CDIQA) and Structural Similarity Index Matrix (SSIM) have been calculated. The experimental results show a remarkable increase in visual quality compared to state-of-art.

Efficient computation of high-order Meixner moments for large-size signals and images analysis

In this paper, we present an in-depth study on the computational aspects of high-order discrete orthogonal Meixner polynomials (MPs) and Meixner moments (MMs). This study highlights two major problems related to the computation of MPs. The first problem is the overflow and the underflow of MPs values (“Nan” and “infinity”). To overcome this problem, we propose two new recursive Algorithms for MPs computation with respect to the polynomial order n and with respect to the variable x. These Algorithms are independent of all functions that are the origin the numerical overflow and underflow problem. The second problem is the propagation of rounding errors that lead to the loss of the orthogonality property of high-order MPs. To fix this problem, we implement MPs based on the following orthogonalization methods: modified Gram-Schmidt process (MGS), Householder method, and Givens rotation method. The proposed Algorithms for the stable computation of MPs are efficiently applied for the reconstruction and localization of the region of interest (ROI) of large-sized 1D signals and 2D/3D images. We also propose a new fast method for the reconstruction of large-size 1D signal. This method involves the conversion of 1D signal into 2D matrix, then the reconstruction is performed in the 2D domain, and a 2D to 1D conversion is performed to recover the reconstructed 1D signal. The results of the performed simulations and comparisons clearly justify the efficiency of the proposed Algorithms for the stable analysis of large-size signals and 2D/3D images.

An improved reversible and secure patient data hiding algorithm for telemedicine applications

In the telemedicine framework, a standout among the most significant issues is the exchange of electronic patient information (EPI) between patient and a doctor that are remotely connected. A minute change to EPI may result in a wrong diagnosis for the patient. To ensure secure and safe communication for telemedicine applications, an enhanced reversible data hiding method in the encrypted domain has been presented in this paper. All compared reversible data hiding methods have been seen to show predominant outcomes, yet just on natural images not on medical images because underflow problem may arise in medical images due to a large number of pixels have low-intensity values. Thus, an enhanced reversible data hiding method in the encrypted domain has been introduced here that gives a higher embedding rate than all the looked at reversible data hiding methods by embedding k, ( $$k\ge 1$$ ) binary bits of a secret message at every pixel of a cover image without any occurrence of underflow and overflow problem. The proposed method has not been suffering from underflow and overflow problem so that empowering it to embed and recover information precisely from low-intensity pixels too. This property makes our proposed method truly reasonable for its utilization on medical images. For all test images, the proposed method altogether beat all the compared methods in its ability to embed secret information and precisely recover it with maintaining the visual quality of stego images too.

The Classical Occupancy Distribution: Computation and Approximation

We examine the discrete distributional form that arises from the “classical occupancy problem,” which looks at the behavior of the number of occupied bins when we allocate a given number of balls uniformly at random to a given number of bins. We review the mass function and moments of the classical occupancy distribution and derive exact and asymptotic results for the mean, variance, skewness and kurtosis. We develop an algorithm to compute a cubic array of log-probabilities from the classical occupancy distribution. This algorithm allows the computation of large blocks of values while avoiding underflow problems in computation. Using this algorithm, we compute the classical occupancy distribution for a large block of values of balls and bins, and we measure the accuracy of its asymptotic approximation using the normal distribution. We analyze the accuracy of the normal approximation with respect to the variance, skewness and kurtosis of the distribution. Based on this analysis, we give some practical guidance on the feasibility of computing large blocks of values from the occupancy distribution, and when approximation is required.

Improved Real Time GPS RF Data Capturing for GNSS SDR Applications

Global Navigation Satellite System (GNSS) software-defined radio (SDR) is a programmable software receiver, primarily used to receive satellite data and provide user position. It is flexible and cost-effective compared to conventional satellite receivers. In GNSS SDR, recording the live raw data in real-time is a challenging task as large data are sent by the satellites at a very high speed. These data are further processed by the baseband algorithms which provide the user location. The accuracy of the tracked location depends on the precision of the data captured. Among the available hardware receivers, USRP RF front end kit provides flexibility. This paper proposes an efficient approach to capture the real-time GPS data by using the USRP N210 kit. If the receiver is not tuned with the exact parameters, the problems of data overflow and underflow occur. A novel data controlling queue thread method is introduced to overcome this problem. It is evident that the results obtained from our method are very close to those of Ublox GPS.

A square lattice oriented reversible information hiding scheme with reversibility and adaptivity for dual images

This paper proposes an adaptive dual-image-based reversible information hiding scheme. First, for a pixel pair in the original image, an associated square lattice is selected to determine the maximum distortion that will be induced by hiding information. Then, an embedding rule, which combines the selected square lattice with integer rounding, is constructed. In addition, a novel histogram shifting mechanism to solve the problems of overflow and underflow is designed to decrease the distortions that are caused by the shifting of a large number of pixels. In our approach, both the original image and the secret messages can be recovered losslessly. Our experimental results show that the proposed scheme achieved performance that was superior to the state-of-the-art schemes, resulting in embedding rates up to 1.40 bpp and average PSNRs of approximately 47.60 dB. The proposed scheme also can be used in real-time systems due to the simplicity of its computations.

Difference expansion based reversible data embedding and edge detection

Difference Expansion (DE) based techniques are evolving in the last decade. The DE based techniques aims at reversibly embedding the data into cover content. Most of the DE based reversible embedding techniques aims to reduce/avoid the amount of auxiliary information to be embedded into the cover images and then state that the auxiliary information generated in the process has no applications rather to keep track of the overflow and/or underflow problems that may arise during the reversible embedding. Our objective is to address the above issues as well as address other related issues as stated below. In this article, we present multiple contributions based on difference expansion technique. At first, we propose a DE based reversible scheme to reduce the amount of auxiliary information to be embedded into the image, to increase the length of the watermark, using the pixel pair information of the changeable differences. The application which emphasizes the need for preserving the auxiliary information, generated by layer-2 DE embedding, is identified. A simple mechanism is suggested to increase the visual quality when the data is embedded using layer-2 embedding. Next, we propose an edge detection scheme using the DE technique. We used PSNR and SSIM visual quality assessment metrics and embedding capacity measure to assess the effectiveness of the proposed data embedding algorithms. The metrics Precision and Recall are used to assess the DE based edge detection scheme. The experimental tests have been conducted on two different datasets having complex visual distinctness features. The analytical and experimental results demonstrate that the proposed work significantly improved state of the art.

Adaptive reversible watermarking for authentication and privacy protection of medical records

Medical systems, such as PACS or scanners, are vulnerable to security and forgery attacks. Consequently, medical records, such as patient information and medical imagery, can be easily leaked or forged. Reversible watermarking is an efficient solution used to protect medical records. However, previous studies have not sufficiently addressed medical applications. This study proposes an adaptive reversible watermarking algorithm that is directly applicable to medical systems that preserves the quality of medical imagery. In particular, the characteristics of medical imagery are considered. Once object and background regions are segmented, the reversible watermarking algorithm is applied based on an estimated error expansion approach. The watermark is embedded by expanding the estimated error from adjacent pixels. This watermark can include patient information or a hash code to detect forgery. When the watermark is extracted, original imagery is perfectly reconstructed without any quality degradation. Inherent over- and underflow problems are solved using an error pre-compensation technique. With the use of medical images from MRI, CT, and X-ray scanners, intensive experiments are performed to analyze the performance of the proposed algorithm with respect to capacity, perceptual quality, and reconstruction rate.

Novel Scheme of Reversible Watermarking With a Complementary Embedding Strategy

In this paper, a new scheme of reversible watermarking is proposed using a complementary embedding strategy in the spatial domain. The proposed scheme consists of two stages: horizontal direction embedding and vertical direction embedding. A complementary embedding strategy is designed to increase the embedding capacity and decrease the distortion of the watermarked image in the vertical direction embedding. Specifically, in the horizontal direction, the proposed scheme embeds one secret data bit by increasing values of pixels in even rows and decreasing values of pixels in odd rows by one. In the vertical direction, it embeds another secret data bit by decreasing values of pixels in even rows and increasing values of pixels in odd rows by one. In addition, a histogram shrinkage technique is adopted to prevent overflow and underflow problems. Experimental results demonstrate that the proposed reversible watermarking scheme outperforms state-of-the-art methods in terms of both embedding capacity and watermarked image quality.

Diagnosis and Prognosis of Degradation Process via Hidden Semi-Markov Model

The intelligent estimation of degradation state and the prediction of remaining useful life (RUL) are important for the maintenance of industrial equipment. In this study, the degradation process of equipment is modeled as an improved hidden semi-Markov model (HSMM), in which the dependence of durations of adjacent degradation states is described and modeled in the HSMM. To avoid underflow problem in computing the forward and backward variables, a modified forward–backward algorithm is proposed in the HSMM. Based on the improved algorithm, online estimation of degradation state and the distribution of RUL can be obtained. Case studies on tool wearing diagnosis and prognosis have verified the effectiveness of this model.

Metoda 'spoljasnje spirale" za resavanje linearnog sistema sa velikim brojem nepoznatih

Solving a linear system of n × n equations can be very difficult for the computer, especially if one needs the exact solution, even when the number n - of equations and of unknown variables is relatively small (a few thousands). All existing methods have to overcome at least one of the following problems: 1. Computational complexity, which is expressed with the number of arithmetic operations required in order to determine a solution; 2. The possibility of overflow and underflow problems; 3. Causing variations in the values of some coefficients in the initial system, which may be leading to instability of the solution; 4. Requiring additional conditions for convergence; 5. In cases of a large number of equations and unknown variables it is often required that the systems matrix be: either sparse, or symmetrical, or diagonal, etc. This paper presents a method for solving a system of linear equations of arbitrary order (any number of equations and unknown variables) to which the problems listed above do not reflect.

A New Estimation for Informed Trading Based on SSNF Method

This paper estimates the information-based trading using spatially selective noise filtration (SSNF) method. The SSNF method is a kind of filtration technique based on the different spatial correlation of the wavelet transform at several adjacent scales. Using SSNF method, the information shock caused by the informed traders could be extracted from the prices effectively, then the PINs at different scales could be calculated. The measure of informed trading can capture some asymmetric information properties, which is consistent with some empirical consensuses. Furthermore, compared with the MLE method in EKOP model, the method has computational facilities in avoiding the overflow or underflow problem, the boundary solutions problem and the initial values problem. And the method could be applied to the high-frequency world in both order-driven and quote-driven market.