Quantitative Comparative Study of the Performance of Lossless Compression Methods Based on a Text Data Model

Data compression plays a key role in optimizing the use of memory storage space and also reducing latency in data transmission. In this paper, we are interested in lossless compression techniques because their performance is exploited with lossy compression techniques for images and videos generally using a mixed approach. To achieve our intended objective, which is to study the performance of lossless compression methods, we first carried out a literature review, a summary of which enabled us to select the most relevant, namely the following: arithmetic coding, LZW, Tunstall’s algorithm, RLE, BWT, Huffman coding and Shannon-Fano. Secondly, we designed a purposive text dataset with a repeating pattern in order to test the behavior and effectiveness of the selected compression techniques. Thirdly, we designed the compression algorithms and developed the programs (scripts) in Matlab in order to test their performance. Finally, following the tests conducted on relevant data that we constructed according to a deliberate model, the results show that these methods presented in order of performance are very satisfactory:- LZW- Arithmetic coding- Tunstall algorithm- BWT + RLELikewise, it appears that on the one hand, the performance of certain techniques relative to others is strongly linked to the sequencing and/or recurrence of symbols that make up the message, and on the other hand, to the cumulative time of encoding and decoding.

Optimal Classification of Minerals by Microscopic Image Analysis Based on Seven-State “Deep Learning” Combined with Optimizers

The development of artificial intelligence (AI), particularly deep learning, has made it possible to accelerate and improve the processing of data collected in different fields (commerce, medicine, surveillance or security, agriculture, etc.). Most related works use open source consistent image databases. This is the case for ImageNet reference data such as coco data, IP102, CIFAR-10, STL-10 and many others with variability representatives. The consistency of its images contributes to the spectacular results observed in its fields with deep learning. The application of deep learning which is making its debut in geology does not, to our knowledge, include a database of microscopic images of thin sections of open source rock minerals. In this paper, we evaluate three optimizers under the AlexNet architecture to check whether our acquired mineral images have object features or patterns that are clear and distinct to be extracted by a neural network. These are thin sections of magmatic rocks (biotite and 2-mica granite, granodiorite, simple granite, dolerite, charnokite and gabbros, etc.) which served as support. We use two hyper-parameters: the number of epochs to perform complete rounds on the entire data set and the “learning rate” to indicate how quickly the weights in the network will be modified during optimization. Using Transfer Learning, the three (3) optimizers all based on the gradient descent methods of Stochastic Momentum Gradient Descent (sgdm), Root Mean Square Propagation (RMSprop) algorithm and Adaptive Estimation of moment (Adam) achieved better performance. The recorded results indicate that the Momentum optimizer achieved the best scores respectively of 96.2% with a learning step set to 10−3 for a fixed choice of 350 epochs during this variation and 96, 7% over 300 epochs for the same value of the learning step. This performance is expected to provide excellent insight into image quality for future studies. Then they participate in the development of an intelligent system for the identification and classification of minerals, seven (7) in total (quartz, biotite, amphibole, plagioclase, feldspar, muscovite, pyroxene) and rocks.

Levant Drought Occurrence, Lebanon Case

Rainfall data is probably one of the longest-recorded climatic parameters in Lebanon. On the central coast of Lebanon, the Beirut weather station started collecting rainfall data in 1876. However, the recorded data is not available at one data provider source. Published data is found in historical documents but it reaches the early 1970s and then appears a data gap till 1990. Still the data is available, but it might be found to be saved privately. This study investigated the SPI variability on annual time scale between the years 1876 and 2021. The SPI was computed using R-Stat software to compare every year between 1876 and 2021. The majority (about 70% of the years) of the years are near normal in the precipitation rate. The Standardized Precipitation Index (SPI) demonstrated a normal distribution of years. Dry and wet years constitute about 15% of the total 146 years (1876-2021). Extremely dry years might appear in two consecutive years between 50 to 60 years count. After 1991, there were no wet years it was only near normal and few dry years. The last 30 years showed a trend of increasing drought years without any occurrence of wet years. This study demonstrated the importance of keeping records of at least rainfall data and it must be recorded on a daily basis or intensity on time. It is highly important on a managerial basis and for water security reasons to understand the drought event occurrence and investigate the changes in rainfall rates. Climate change scenarios always forecast a decrease in rainfall rates which will not appear without such studies.

New Approach to Rock Classification Based on Sparse Representations

In geology, classification and lithological recognition of rocks plays an important role in the area of oil and gas exploration, mineral exploration and geological analysis. In other fields of activity such as construction and decoration, this classification makes sense and fully plays its role. However, this classification is slow, approximate and subjective. Automatic classification curbs this subjectivity and fills this gap by offering methods that reflect human perception. We propose a new approach to rock classification based on direct-view images of rocks. The aim is to take advantage of feature extraction methods to estimate a rock dictionary. In this work, we have developed a classification method obtained by concatenating four (4) K-SVD variants into a single signature. This method is based on the K-SVD algorithm combined with four (4) feature extraction techniques: DCT, Gabor filters, D-ALBPCSF and G-ALBPCSF, resulting in the four (4) variants named K-Gabor, K-DCT, KD-ALBPCSF and KD-ALBPCSF respectively. In this work, we developed a classification method obtained by concatenating four (4) variants of K-SVD. The performance of our method was evaluated on the basis of performance indicators such as accuracy with other 96% success rate.

Detection of Burned Areas through Spectral Indices Analysis of Sentinel-2A Satellite Images in the Abokouamékro Wildlife Reserve (Central, Côte D’Ivoire)

In Côte d’Ivoire, the recurring and unregulated use of bushfires, which cause ecological damage, presents a pressing concern for the custodians of protected areas. This study aims to enhance our comprehension of the dynamics of burnt areas within the Abokouamékro Wildlife Reserve (AWR) by employing the analysis of spectral indices derived from satellite imagery. The research methodology began with the calculation of mean indices and their corresponding spectral sub-indices, including NDVI, SAVI, NDWI, NDMI, BAI, NBR, TCW, TCG, and TCB, utilizing data from the Sentinel-2A satellite image dated January 17, 2022. Subsequently, a fuzzy classification model was applied to these various indices and sub-indices, guided by the degree of membership α, with the goal of effectively distinguishing between burned and unburned areas. Following the classification, the accuracies of the classified indices and sub-indices were validated using the coordinates of 100 data points collected within the AWR through GPS technology. The results revealed that the overall accuracy of all indices and sub-indices declines as the degree of membership α decreases from 1 to 0. Among the mean spectral indices, NDVI-mean, SAVI-mean, NDMI-mean exhibited the highest overall accuracies, achieving 97%, 95%, and 90%, respectively. These results closely mirrored those obtained by sub-indices using band 8 (NDVI-B8, SAVI-B8, and NDMI-B8), which yield respective overall accuracies of 93%, 92%, and 89%. At a degree of membership α = 1, the estimated burned areas for the most effective indices encompassed 2144.38 hectares for NDVI-mean, 1932.14 hectares for mean SAVI-mean, and 4947.13 hectares for mean NDMI-mean. A prospective approach involving the amalgamation of these three indices could have the potential to yield improved outcomes. This study could be a substantial contribution to the discrimination of bushfires in Côte d’Ivoire.

FIXED POINT THEOREMS FOR MAPPINGS SATISFYING IMPLICIT RELATION ON TWO COMPLETE AND COMPACT METRIC SPACES

First, the implicit relations were given. A common fixed point theorem was proved for two mappings satisfying implicit relation functions. A further fixed point theorem was proved for mappings satisfying implicit relation functions on two compact metric spaces.

Multichannel imaging for monitoring chemical composition and germination capacity of cowpea(Vigna unguiculata) seeds during development and maturation

This study aimed to set a computer-integrated multichannel spectral imaging system as a high-throughput phenotyping tool for the analysis of individual cowpea seeds harvested at different developmental stages. The changes in germination capacity and variations in moisture, protein and different sugars during twelve stages of seed development from 10 to 32 days after anthesis were nondestructively monitored. Multispectral data at 20 discrete wavelengths in the ultraviolet, visible and near infrared regions were extracted from individual seeds and then modelled using partial least squares regression and linear discriminant analysis(LDA) models. The developed multivariate models were accurate enough for monitoring all possible changes occurred in moisture, protein and sugar contents with coefficients of determination in prediction R^(2) of 0.93, 0.80 and 0.78 and root mean square errors in prediction(RMSEP) of 6.045%, 2.236% and 0.890%, respectively. The accuracy of PLS models in predicting individual sugars such as verbascose and stachyose was reasonable with R~2 of 0.87 and 0.87 and RMSEP of 0.071%and 0.485%, respectively;but for the prediction of sucrose and raffinose the accuracy was relatively limited with R^(2) of 0.24 and 0.66 and RMSEP of 0.567% and 0.045%, respectively. The developed LDA model was robust in classifying the seeds based on their germination capacity with overall correct classification of96.33% and 95.67% in the training and validation datasets, respectively. With these levels of accuracy,the proposed multichannel spectral imaging system designed for single seeds could be an effective choice as a rapid screening and non-destructive technique for identifying the ideal harvesting time of cowpea seeds based on their chemical composition and germination capacity. Moreover, the development of chemical images of the major constituents along with classification images confirmed the usefulness of the proposed technique as a non-destructive tool for estimating the concentrations and spatial distributions of moisture, protein and sugars during different developmental stages of cowpea seeds.

Mapping Glitches of Juniper Forests in Lebanon under Natural Conditions and Anthropogenic Activities

In 1965, the first forest map of Lebanon was produced. It is the oldest spatial distribution representation of junipers. Landcover maps of 2002 and 2010 are the most detailed spatial distribution that spatially shows forests. Juniper forests are found in Lebanon as mainly as clear to low density coverage. High-density juniper forests are rarely found and only on Mount-Lebanon. Juniper forests are also mixed with oaks on the Eastern flank of Mount-Lebanon. Mapping juniper forests have demonstrated high degree of complexity, especially because of their low density and being mixed. The spatial representation of juniper forests was compared between the 1965 forest map and the landcover maps of 2002 and 2010. GIS environment was used to extract juniper forests from all maps. The degree of matching between juniper forests was investigated regarding the total area and spatial overlapping. Juniper forests were examined to their spatial locations, comparing the three maps. Spatial changes and anthropogenic effect were obtained, using Google Earth facilities. Google earth had satellite images acquired since 2014. Landcover maps of 2002 and 2010 have spatially matched forest map of 1965 by about 90% and 50% respectively. Spatial coverage of juniper forests were about 12,000, 26,000 and 28,000 ha on the 1965 forest map, landcover maps of 2003 and 2010 respectively. Anti-Lebanon juniper forests were not well represented on both landcover maps. Anthropogenic activities were mainly agriculture that affected juniper forests. Cultivations have replaced about 2% of the spatial coverage of 1965 Juniper forests. Quarries and urban existed inside juniper forests but in very limited areas. Juniper forests delineation did not completely match neither between the available maps, nor to the ground. Some juniper forests were not spatially represented on all maps or existing maps represented only portion of juniper forests. Juniper forest mapping requires more consideration and field investigation. High spatial resolution satellite images are among the solutions but delimiting juniper would require extensive fieldwork and specific remote sensing treatments. Being centuries old forests and characterized by High Mountain elevations, these important conifer forests are needed to be mapped with higher accuracy for better statistics and conservation.

An Alternative Model to Radon Transform for Gamma Ray Emission Tomography

The Radon transform fits badly Single Photon Emission Tomography (SPECT). However, Thin Holes Collimator (THC) and Radon model are widely used. The CACAO project has been proposed to enhance the quality of SPECT images. CACAO is a short hand notation for computer aided collimation tomography. The main idea of this project is to use collimators with much larger holes to increase the sensitivity, and slightly longer holes to increase the spatial resolution. The acquisition sequence includes a translation. The Radon projection is replaced by a 2D sum. A dedicated reconstruction algorithm has been developed. If the physical advantage of the project in terms of sensitivity and spatial resolution is generally admitted, a question remains unanswered: Would the ill-posedness of the inverse problem ruin the quality of the reconstructed images? In this article, a representation of the 2D direct problem matrix is derived. This allows us to compare the two inverse problems (CACAO versus THC). The condition number was used for this comparison. We studied the variation of these condition numbers with several parameters. For a proper set of parameters, the CACAO inverse problem may appear easier to solve and more accurately than the THC one.

Materialized Views Selection Problem in Decision Supporting Systems: Issues and Challenges

The data warehouse is the most widely used database structure in many decision support systems around the world. This is the reason why a lot of research has been conducted in the literature over the last two decades on their design, refreshment and optimization. The manipulation of hypercubes (cubes) of data is a frequently used operation in the design of multidimensional data warehouses, due to their better adaptation to OLAP (On-Line Analytical Processing). However, the updating of these hypercubes is a very complicated process due mainly to the mass and complexity of the data presented. The purpose of this paper is to present the state of the art of works based on multidimensional modeling using the hypercube as a unit of presentation of data stores. It starts with the base of this process which is the choice of the views (cubes) forming our data warehouse base. The objective of this work is to describe the state of the art of research works dealing with the selection of materialized views in decision support systems.

A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis

Local Binary Patterns (LBPs) have been highly used in texture classification for their robustness, their ease of implementation and their low computational cost. Initially designed to deal with gray level images, several methods based on them in the literature have been proposed for images having more than one spectral band. To achieve it, whether assumption using color information or combining spectral band two by two was done. Those methods use micro structures as texture features. In this paper, our goal was to design texture features which are relevant to color and multicomponent texture analysis without any assumption. Based on methods designed for gray scale images, we find the combination of micro and macro structures efficient for multispectral texture analysis. The experimentations were carried out on color images from Outex databases and multicomponent images from red blood cells captured using a multispectral microscope equipped with 13 LEDs ranging from 375 nm to 940 nm. In all achieved experimentations, our proposal presents the best classification scores compared to common multicomponent LBP methods. 99.81%, 100.00%, 99.07% and 97.67% are maximum scores obtained with our strategy respectively applied to images subject to rotation, blur, illumination variation and the multicomponent ones.

Java速度试验

PC Magazine的Java性能测试揭示了今天Java环境的优点和不足。

Optimal Flame Detection of Fires in Videos Based on Deep Learning and the Use of Various Optimizers

Deep learning has recently attracted a lot of attention with the aim of developing a fast, automatic and accurate system for image identification and classification. In this work, the focus was on transfer learning and evaluation of state-of-the-art VGG16 and 19 deep convolutional neural networks for fire image classification from fire images. In this study, five different approaches (Adagrad, Adam, AdaMax, Nadam and Rmsprop) based on the gradient descent methods used in parameter updating were studied. By selecting specific learning rates, training image base proportions, number of recursion (epochs), the advantages and disadvantages of each approach are compared with each other in order to achieve the minimum cost function. The results of the comparison are presented in the tables. In our experiment, Adam optimizers with the VGG16 architecture with 300 and 500 epochs tend to steadily improve their accuracy with increasing number of epochs without deteriorating performance. The optimizers were evaluated on the basis of their AUC of the ROC curve. It achieves a test accuracy of 96%, which puts it ahead of other architectures.

A New Hybrid Order Approach to Morphological Color Image Processing Based on Reduced Order with Adaptive Absolute Reference

Mathematical morphology can process the binary and grayscale image successfully. This theory cannot be extended to the color image directly. In color space, a vector represents a pixel, so in order to compare vectors, vectoriel orderings must be defined first. This paper addresses the question of the extension of morphological operator to the case of color images. The proposed method used the order by bit mixing to replace the conditional order. Our order is based on a combination of reduced and bit mixing ordering of the underlying data. Additionally it is a total ordering. Since it not only solves the problems of false color generated by the marginal order but also those of multiple extrema generated by reduced order. The performance of the introduced operators is illustrated by means of different applications: color gradients for segmenting, image smoothing (noise suppression) by median filter operator and Laplacian operators. Examples of natural color images and synthetic color images are given. Experimental results show the improvement brought by this new method.

GenoDrawing:An Autoencoder Framework for Image Prediction from SNP Markers

Advancements in genome sequencing have facilitated whole-genome characterization of numerous plant species,providing an abundance of genotypic data for genomic analysis.Genomic selection and neural networks(NNs),particularly deep learning,have been developed to predict complex traits from dense genotypic data.Autoencoders,an NN model to extract features from images in an unsupervised manner,has proven to be useful for plant phenotyping.This study introduces an autoencoder framework,GenoDrawing,for predicting and retrieving apple images from a low-depth single-nucleotide polymorphism(SNP)array,potentially useful in predicting traits that are difficult to define.GenoDrawing demonstrates proficiency in its task using a small dataset of shape-related SNPs.Results indicate that the use of SNPs associated with visual traits has substantial impact on the generated images,consistent with biological interpretation.While using substantial SNPs is crucial,incorporating additional,unrelated SNPs results in performance degradation for simple NN architectures that cannot easily identify the most important inputs.The proposed GenoDrawing method is a practical framework for exploring genomic prediction in fruit tree phenotyping,particularly beneficial for small to medium breeding companies to predict economically substantial heritable traits.Although GenoDrawing has limitations,it sets the groundwork for future research in image prediction from genomic markers.Future studies should focus on using stronger models for image reproduction,SNP information extraction,and dataset balance in terms of phenotypes for more precise outcomes.