Image Compression Using Wavelet Methods

SummaryThe ability to design smarter, more predictive healthcare solutions for use in the community (at work and at home) and in healthcare facilities has been greatly enhanced by recent developments in the Internet of Health Things (IoHT) and cyber physical systems (CPS). These data collected by such medical sensors will be sent in a large quantity to the fog gateway of IoHT networks. These data should be forwarded to far cloud for further analysis and processing. Therefore, sending all of these data over the IoHT network to the data center will impose a significant burden on the IoHT network. This paper proposed a new lossless electroencephalogram (EEG) compression technique (NeLECoT) for fog computing‐based IoHT networks. It encodes the data of the patient at the fog gateway prior to sending it to the data center, thereby reducing the data's size. In the fog node, the NeLECoT combines three efficient techniques: clustering based on density‐based spatial clustering of applications with noise (DBSCAN), RLE (run length encoding), and Huffman encoding (HE). The clustering based on DBSCAN separates a massive volume of captured data into small groups of closely related (or similar) data. RLE encodes clustered EEG data, and the resulting file is encoded with HE. The fog gateway then transmits the encoded file to the cloud. Numerous simulation experiments were carried out, and the findings demonstrated that the suggested NeLECoT achieved better results than the competing techniques in terms of transmitted data size, compression ratio, compression power, compression time, decompression time, and average compression power.

In this paper, we evaluate lossless and lossy compression techniques to compress quantitative phase images of red blood cells (RBCs) obtained by an off-axis digital holographic microscopy (DHM). The RBC phase images are numerically reconstructed from their digital holograms and are stored in 16-bit unsigned integer format. In the case of lossless compression, predictive coding of JPEG lossless (JPEG-LS), JPEG2000, and JP3D are evaluated, and compression ratio (CR) and complexity (compression time) are compared against each other. It turns out that JP2k can outperform other methods by having the best CR. In the lossy case, JP2k and JP3D with different CRs are examined. Because some data is lost in a lossy way, the degradation level is measured by comparing different morphological and biochemical parameters of RBC before and after compression. Morphological parameters are volume, surface area, RBC diameter, sphericity index, and the biochemical cell parameter is mean corpuscular hemoglobin (MCH). Experimental results show that JP2k outperforms JP3D not only in terms of mean square error (MSE) when CR increases, but also in compression time in the lossy compression way. In addition, our compression results with both algorithms demonstrate that with high CR values the three-dimensional profile of RBC can be preserved and morphological and biochemical parameters can still be within the range of reported values.

Lead-free perovskite solar cell based on methyl ammonium tin iodide: Possible power conversion efficiency enhancement by device simulation

Choosing a liquid chromatography (LC) column for a particular application can be a surprisingly challenging task. On the one hand, column manufacturers provide us many options to choose from, including particle types, pore sizes, particle sizes, and different lengths and diameters. On the other hand, we usually do not have time to experimentally evaluate many combinations of these parameters, and sometimes we end up picking something similar to the columns that are already available. The “kinetic plot” is a powerful graphical tool that can help leverage the best available theory to help us understand how different combinations of parameters (that is, particle size, length, among others) will perform in regard to the time needed to get to a particular column efficiency (and thus resolution), and therefore make well-informed decisions when choosing columns.

The transmission of image data acquired by remote sensing missions, based on space borne platforms is a major bottleneck, as a result of the limitation of on-board power, the huge volume of transmitted data, and the number of accessible ground receiving stations. In particular these arguments hold true for small satellites that faces additional design constraints in terms of size, mass and cost .To overcome downlink restriction, image compression has to be applied, although the provided data transmission rates are constantly growing, they can't keep up with the exponentially increasing data flood provided by the scanners, the reasons are twofold: First the acquisition rate exceeds the transmission rate. Second the satellite is not in constant visibility of ground receiving station, which effectively limits the downlinkable data volume per orbit. This paper introduces implementation of some of the compression techniques for both lossy and lossless which are used for the On-Board satellite missions, taking into consideration that the introduced compression method should support and enable the real time imaging and transmission. Also, a comparison study between both techniques is introduced based on measures like Compression Ratio (CR), Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR).The experimental results showed that: Huffman code is the most suitable code for lossless compression technique for satellite images, since it gives a considerable high compression ratio with respect to the other lossless algorithms (Run-Length Encode, Lempel-Ziv-Welch code and Arithmetic code). Huffman code does not require much storage capacity such as LZW or more computational requirements such as arithmetic code. Also, the most suitable lossy compression techniques for satellite images is applying Discrete Wavelet Transform (DWT) on the four Most Significant Bit Planes (MSPB) of the image then applying the arithmetic code over the resultant DWT coefficients. This technique achieves higher average compression ratio (8.28:1) than the two other techniques and at the same time the image quality is accepted.

The numerous medical images generated by various imaging devices require a huge amount of storage space and minimum transmission bandwidth. So, it is necessary to use an appropriate technique for compression of medical images in order to meet the above concerns. This paper proposes a technique for lossless compression of significant parts by extracting the region of interest in DICOM images. The extracted region is compressed using Huffman encoding and genetic algorithm for the enhancement of compression ratio. Additionally, the value of PSNR has also been calculated. When the results of the proposed technique are compared with the previous method, it is found that there is an increase of more than 400% in the compression ratio. It also provides an average increase of 20% in the compression ratio by using Huffman encoding.

Wavelet threshold based ECG compression using USZZQ and Huffman coding of DSM

We present a fast compression technique for natural language texts. The novelties are that (1) decompression of arbitrary portions of the text can be done very efficiently, (2) exact search for words and phrases can be done on the compressed text directly, using any known sequential pattern-matching algorithm, and (3) word-based approximate and extended search can also be done efficiently without any decoding. The compression scheme uses a semistatic word-based model and a Huffman code where the coding alphabet is byte-oriented rather than bit-oriented. We compress typical English texts to about 30% of their original size, against 40% and 35% forCompressandGzip, respectively. Compression time is close to that ofCompressand approximately half of the time ofGzip, and decompression time is lower than that ofGzipand one third of that ofCompress. We present three algorithms to search the compressed text. They allow a large number of variations over the basic word and phrase search capability, such as sets of characters, arbitrary regular expressions, and approximate matching. Separators and stopwords can be discarded at search time without significantly increasing the cost. When searching for simple words, the experiments show that running our algorithms on a compressed text is twice as fast as running the best existing software on the uncompressed version of the same text. When searching complex or approximate patterns, our algorithms are up to 8 times faster than the search on uncompressed text. We also discuss the impact of our technique in inverted files pointing to logical blocks and argue for the possibility of keeping the text compressed all the time, decompressing only for displaying purposes.

This study proposes the Huffman Tree and Binary Conversion (HTB) which is a preprocessing algorithm to transform the Huffman tree into binary representation before the encryption process. In fact, HTB can improve the structural readiness of plaintext by combining the Huffman code with a deterministic binary representation of the Huffman tree. In addition, binary representation of the Huffman tree and the compressed information will be encrypted by standard cryptographic algorithms. Six datasets, divided into two groups (short and long texts), were chosen to evaluate compression behavior and the processing cost. Moreover, AES and RSA are chosen to combine with the proposed method to analyze the encryption and decryption cycles. The experimental results show that HTB introduces a small linear-time overhead. That means, it is slightly slower than applying only the Huffman code. Across these datasets, HTB maintained a consistently low processing cost. The processing time is below one millisecond in both encoding and decoding processes. However, for long texts, the structural conversion cost becomes amortized across larger encoded messages, and the reduction in plaintext size leads to fewer encryption blocks for both AES and RSA. The reduced plaintext size lowers the number of AES encryption blocks by approximately 30–45% and decreases the number of encryption and decryption rounds in RSA. The encrypted binary representation of the Huffman tree also decreased structural ambiguity and reduced the potential exposure of frequency-related metadata. Although HTB does not replace cryptographic security, it enhances the structural consistency of compression. Therefore, the proposed method demonstrates scalability, predictable overhead, and improved suitability for cryptographic workflows.

The solubility of hen egg-white lysozyme

Negative hypergeometric distribution arises as a waiting time distribution when we sample without replacement from a finite population. It has applications in many areas such as inspection sampling and estimation of wildlife populations. However, as is well known, the negative hypergeometric distribution is over-dispersed in the sense that its variance is greater than the mean. To make it more flexible and versatile, we propose a modified version of negative hypergeometric distribution called COM-Negative Hypergeometric distribution (COM-NH) by introducing a shape parameter as in the COM-Poisson and COMP-Binomial distributions. It is shown that under some limiting conditions, COM-NH approaches to a distribution that we call the COM-Negative binomial (COMP-NB), which in turn, approaches to the COM Poisson distribution. For the proposed model, we investigate the dispersion characteristics and shape of the probability mass function for different combinations of parameters. We also develop statistical inference for this model including parameter estimation and hypothesis tests. In particular, we investigate some properties such as bias, MSE, and coverage probabilities of the maximum likelihood estimators for its parameters by Monte Carlo simulation and likelihood ratio test to assess shape parameter of the underlying model. We present illustrative data to provide discussion.

Context. Recurrent novae are, by definition, novae observed in outburst more than once or identified by the presence of vast super-shells, ejected in previous eruptions, surrounding the system. These systems are characterized by remarkably short recurrence times between outbursts, typically ranging from 1 to about 100 yr. Such short recurrence times require very high mass-accretion rates, white dwarf masses approaching the Chandrasekhar limit, and very high initial white dwarf luminosities. Aims. T Coronae Borealis (T CrB) is one of the eleven known recurrent novae in our Galaxy. It was observed in outburst in 1866 and 1946, with additional likely eruptions recorded in 1217 and 1787. Given its predicted recurrence period of approximately 80 yr, the next outburst is anticipated to occur imminently, thus motivating a thorough examination of the main characteristics of this system. Methods. We present 11 new hydrodynamic models of the explosion of T CrB for different combinations of parameters (i.e., the mass, composition, and initial luminosity of the white dwarf, the metallicity of the accreted matter, and the mass-transfer rate). We also report on 8 additional hydrodynamic models that include mixing at the interface between the accreted envelope and the outermost layers of the underlying white dwarf, and 3 models for 1.20 M⊙ white dwarfs. Results. We show that mass-accretion rates of Ṁacc ∼ 10−8−10−7 M⊙ yr−1 are required to trigger an outburst after 80 yr of accretion of solar-composition material onto white dwarfs with masses MWD∼1.30−1.38 M⊙ and initial luminosities LWD∼0.01−1 L⊙. For lower white dwarf luminosities, less massive white dwarfs, or reduced metallicity in the accreted material, higher mass-accretion rates are required to drive an explosion within this timescale. A decrease in metallicity or initial white dwarf luminosity leads to higher accumulated masses and ignition pressures, resulting in more violent outbursts. These outbursts exhibit higher peak temperatures, higher ejected masses, and greater kinetic energies. Models computed for different white dwarf masses but identical initial luminosities reveal significant differences in the elemental abundances of a wide range of species, including Ne, Na, Mg, Al, Si, P, S, Ar, K, Ca, and Sc. These compositional differences offer a potential diagnostic tool for constraining the parameter space and discriminating between the various T CrB models reported in this study.

The detection of emotions from speech is the aim of this paper. Speech consists of anger, joy and fear have very high and wide range in pitch, whereas Speech consists of sad and tired emotion have very low pitch. Speech Emotion detection technology can recognize human emotions to help machines better for understanding intentions of a user to improve the human-computer interaction. Classification model named Convolutional Neural Network (CNN) based on mainly Mel Frequency Cepstral Coefficient (MFCC) feature to detect emotion have been presented here. Different approaches have been discussed and compared to find best CNN model using different combinations of parameters. The models have been trained to distinguish eight different emotions such as calm, neutral, angry, sad, happy, disgust, fear, surprise. The proposed work shows that CNN 3 Layer model with RMSprop optimizer when trained with 80 Epochs works best among other CNN models for the RAVDESS dataset.

Determination of sliding body saturated permeability coefficient has an important significance for groundwater dynamic field, deformation and stability of reservoir landslide. Based on in-depth analysis of landslide geological characteristics, groundwater monitoring data, rainfall and reservoir water lever in Three Gorges Reservoir, it is found that reservoir water level is the main factor which affecting the groundwater dynamic field. According to two layers of sliding body, the saturated permeability coefficient test plan of two factors and three levels has been designed. Based on saturated-unsaturated seepage finite element and the reservoir water level fluctuation curve, groundwater saturation line has obtained under different combinations of parameters. Combined with genetic algorithm, an optimal neural network model was established to describe the nonlinear relationship between the sliding body saturated permeability coefficient and borehole water level. Then based on the monitoring data of borehole water level, the saturated permeability coefficient of two layers sliding body can be gotten. Finally, with the inversion parameters, it is found that the calculating water level results and the actual level of underground water are in good agreement. Results show that the inverse thought and method is feasible and effective, which can provide reference for the similar engineering.

Continued investigation of Tamed FM (TFM) has led to an extension, called Generalized Tamed FM (GTFM). The features of GTFM are based on the possibility to select different combinations of parameters which characterize the process of waveform generation. This still allows for the generation of a constant envelope modulated signal with a spectrum similar to TFM. However, a joint optimization of coherent and noncoherent detection with regard to error performance can be achieved. Attention is given to a low-complexity, noncoherent receiver, comprising a frequency discriminator and a maximum likelihood estimator. The generation of GTFM signals can largely be based on a programmable LSI circuit.