Classification of soil is the dissolution to soil sets to particular group having a like characteristics and similar manners. Almost all countries do product exporting, in which those countries exporting higher agricultural product are very much depend on the soil characteristics. Thus, soil characteristics identification and classification is very much important. Identification of the soil type helps to avoid agricultural product quantity loss. A classification for engineering purpose should be based mainly on mechanical properties. This paper explains support vector machine based classification of the soil types. Soil classification includes steps like image acquisition, image preprocessing, feature extraction and classification. The texture features of soil images are extracted using the low pass filter, Gabor filter and using color quantization technique. Mean amplitude, HSV histogram, Standard deviation are taken as the statistical parameters.

Performance of SVM classifier for image based soil classification

Computer Vision, and hence Artificial Intelligence-based extraction of information from images, has increasingly received attention over the last years, for instance in medical diagnostics. While the algorithms’ complexity is a reason for their increased performance, it also leads to the ‘black box’ problem, consequently decreasing trust towards AI. In this regard, “Explainable Artificial Intelligence” (XAI) allows to open that black box and to improve the degree of AI transparency. In this paper, we first discuss the theoretical impact of explainability on trust towards AI, followed by showcasing how the usage of XAI in a health-related setting can look like. More specifically, we show how XAI can be applied to understand why Computer Vision, based on deep learning, did or did not detect a disease (malaria) on image data (thin blood smear slide images). Furthermore, we investigate, how XAI can be used to compare the detection strategy of two different deep learning models often used for Computer Vision: Convolutional Neural Network and Multi-Layer Perceptron. Our empirical results show that i) the AI sometimes used questionable or irrelevant data features of an image to detect malaria (even if correctly predicted), and ii) that there may be significant discrepancies in how different deep learning models explain the same prediction. Our theoretical discussion highlights that XAI can support trust in Computer Vision systems, and AI systems in general, especially through an increased understandability and predictability.

Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in Computer Vision-Based Decision Support.

In this paper, we have proposed a hybrid adaptive coding and decoding scheme for multi-hop wireless sensor networks (WSNs). Energy consumption and transmission reliability are used as performance metrics for multi-hop communications in WSNs. The presented scheme takes into account distance, channel conditions and correction codes performance to decide coding and decoding procedure, and considers Reed Solomon code and Low Density Parity Check code to provide error protection on the transmitted data. The proposed approach aims to reduce the decoding power consumption and to prolong the lifetime of the network as well as improve the reliability of the transmission. Simulation results show that our performed scheme enhances both energy efficiency and communication reliability of multi-hop sensor networks.

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A Hybrid Adaptive Coding and Decoding Scheme for Multi-hop Wireless Sensor Networks

Studies of microbiome variation in wildlife often emphasize host physiology and diet as proximate selective pressures acting on host-associated microbiota. In contrast, microbial dispersal and ecological drift are more rarely considered. Using amplicon sequencing, we characterized the bacterial microbiome of adult female (n = 86) Sable Island horses (Nova Scotia, Canada) as part of a detailed individual-based study of this feral population. Using data on sampling date, horse location, age, parental status, and local habitat variables, we contrasted the ability of spatiotemporal, life history, and environmental factors to explain microbiome diversity among Sable Island horses. We extended inferences made from these analyses with both phylogeny-informed and phylogeny-independent null modelling approaches to identify deviations from stochastic expectations. Phylogeny-informed diversity measures were correlated with spatial and local habitat variables, but null modelling results suggested that heterogeneity in ecological drift, rather than differential selective pressures acting on the microbiome, was responsible for these correlations. Conversely, phylogeny-independent diversity measures were best explained by host spatial and social structure, suggesting that taxonomic composition of the microbiome was shaped most strongly by bacterial dispersal. Parental status was important but correlated with measures of β-dispersion rather than β-diversity (mares without foals had lower alpha diversity and more variable microbiomes than mares with foals). Our results suggest that between host microbiome variation within the Sable Island horse population is driven more strongly by bacterial dispersal and ecological drift than by differential selective pressures. These results emphasize the need to consider alternative ecological processes in the study of microbiomes.

Bacterial dispersal and drift drive microbiome diversity patterns within a population of feral hindgut fermenters

The methodology description of the adaptive multi-threshold decoding of self-orthogonal codes in the telecommunication channels of information transfer is shown in this paper. The method of multi-threshold decoder modification is described on the basis of adaptive filtration algorithms. Principles of adaptive algorithms application provide for necessary data transmission validity in the case of the multi-threshold decoding are explored. The graphic charts of multi-threshold decoders noise immunity of self-orthogonal block and convolutional codes are presented. It is determined the coding gain (CG) for multi-threshold decoding schemes. The result of research conducted in the course of the paper is to develop a set of scientifically grounded theoretical positions and practical recommendations and proposals for the development of mechanisms of formalization of description of method of increasing of noise immunity of telecommunication systems transmitting information to the synthesis and improving receiver circuit modulated signals on the theory and practice the use of signal-code constructions (SCC) when deciding maximize system capacity information transmission in the presence of noise.

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Method of the adaptive decoding of self-orthogonal codes in telecommunication

Soft decision decoding (SDD) provides a better coding gain by making use of the unquantized channel output. In this paper we introduce the concept of a Reliability Level List (RLL); based on the RLL a new SDD algorithm for Binary Phase Shift Keying (BPSK) based binary cyclic block codes is proposed. The algorithm guarantees to extract the most reliable codeword in an iterative manner. The formation of the RLL involves a search for the next possible entry into the RLL based on the error probability which is a reflection of the reliability values of the bits of the received word obtained from the channel. The procedure for the formation of RLL which is the central idea of the paper is given as a structured algorithm.

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A Reliability Level List based SDD Algorithm for Binary Cyclic Block Codes

Energy efficiency and error free transmission have become prime concerns in wireless communication in recent years. Such networks are much more affected by errors due to dynamic channel conditions than normal wired networks. Error control coding is commonly used in the entire range of information com-munication to reduce the harmful effects of the channel. In order to overcome the communication errors in an energy efficient way, an error control mechanism with less complexity is required. Energy efficient error control techniques to prolong network lifetime in resource limited network and wireless communication remains a challenge. This paper forms a survey of recent developments on the various energy efficient error control coding techniques used in wireless communication and resource limited networks/hardware.

Optimum energy efficient error control techniques in wireless systems: a survey

Given a received polynomial rx with coefficients in finite field soft decision decoding SDD is the process of identifying the most probable error polynomial ex with coefficients in finite field and hence the corresponding code polynomial cx ∈{C x}, where {C x} is the code set. The paper presents an algorithm for identifying this cx - called the target code word TCW here - and extends the same to identify the list of codewords as well. The uniqueness of the approach lies in identifying the TCW directly without the need for search over a much larger set as with many of the existing methods. The proposed method seeks codewords with the most likely error positions guessed in advance; this ushers in a further simplification with cyclic codes obviating the need for repeated algebraic decoding. The fact that restricting the error correcting range to d

A minimal search soft decision list decoding algorithm for Reed-Solomon codes

Pernicious Trojan circuits inserted during the design or fabrication phases of an integrated circuit may cause undesirable effects in the designed system. In the case of communication systems, incorrect decoding of the received information is an issue that may lead to serious consequences. In this paper, we study the performance degradation of a noisy coded communication system, due to the presence of hardware Trojans in the decoding unit. It can be seen that the Trojans have negligible effect at low SNRs while there is a statistically significant increase in the bit error rate (p < 0.01) at higher SNRs. At high SNRs, we expect the bit error rate to tend towards zero, but it is observed that even with the Trojan being activated only once during the entire duration of the transmission, there is a constant bit error rate of the order of 10−4.

Effect of hardware Trojans on the performance of a coded communication system

Demand for transmission using short block length messages has been increased recently in applications including satellite communication, mobile communication, wireless sensor networks, and machine ...

B. Yamuna

Papers

A Reliability Level List based SDD Algorithm for Binary Cyclic Block Codes

Optimum energy efficient error control techniques in wireless systems: a survey

A minimal search soft decision list decoding algorithm for Reed-Solomon codes

Effect of hardware Trojans on the performance of a coded communication system

Performance Analysis of Reliability-Based Decoding Algorithm for Short Block Length Turbo Codes