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Abstract-Distributed nature of transactions arising at different sites and needing resources from diverse locations pose various operational problems, such as deadlocks, concurrency and data recovery. A deadlock may occur when a transaction enters into wait state that request resource from other blocked transactions. Deadlock detection and resolving is very difficult in a distributed database system because no controller has complete and current information about the system and data dependencies. In this paper, an enhanced technique for deadlock resolution is presented, which minimizes the abortion or waiting of the selected victim transactions. The proposed system includes the use of fuzzy logic by creating a set of fuzzy rules in order to deal with criticalness and similarity attributes of transactions. By using these rules, fuzzy logic will try to provide an easy conflict resolution method between transactions to diminish transactions wasted restart, and guaranteeing temporal consistency of data and transactions. Furthermore, the presented deadlock handling algorithm does not detect any false deadlock or exclude any really existing deadlocks. Experimental results show performance of the recommended system benefits such as increase in commit rate and decrease in re-execution or waiting of the transactions.

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Indoor object detection is a fundamental activity for the development of applications of mobility-assistive technology for visually impaired people (VIP). The challenge of seeing interior objects in a real indoor environment is a challenging one since there are numerous complicated issues that need to be taken into consideration, such as the complexity of the background, occlusions, and viewpoint shifts. Electronic travel aids that are composed of the necessary sensors may assist VIPs with their navigation. The sensors have the ability to detect any obstacles, regardless of whether they are static or dynamic, and offer information on the context of an interior scene. The characteristics of an interior scene are not very clear and are subject to a great deal of variation. Recent years have seen the emergence of methods for dealing with issues of this kind, some of which include the use of neural networks, probabilistic methods, and fuzzy logic. This study describes a method for detecting indoor objects using a rotational ultrasonic array and neutrosophic logic. A neutrosophic set has been seen as the next evolution of the fuzzy set because of its indeterminate membership value, which is absent from conventional fuzzy sets. The suggested method is constructed to reflect the position of the walls (obstacle distance) and to direct the VIP to move freely (ahead, to the right, or to the left) depending on the degree of truthiness, the degree of indeterminacy, and the degree of falsity for the reflected distance. The results of the experiments show that the suggested indoor object detecting system has good performance, as its accuracy rate (a mean average precision) is 97.2 ± 1%.

Abstract-Gait based recognition is one of the emerging new biometric technology for human identification, surveillance and other security applications. Gait is a potential behavioral feature to identify humans at a distance based on their motion. The use of new methods for handling inaccurate information about gait features is of fundamental important. This paper deals with the design of an intelligent gait recognition system using interval type-2 fuzzy K-nearest neighbor (IT2FKNN) for diminishing the effect of uncertainty formed by variations in energy deviation image (EDI). The proposed system is built on top of the well-known principal component analysis (PCA) method that is utilized to remove correlation between the features and also to reduce its dimensionality. Our system employs IT2FKNN to compute fuzzy within and in-between class scatter matrices of PCA to refine classification results. This employment makes the system able to distinguish between normal, abnormal and suspicious walk of a person so that an alarming action may be taken well in time. Interval type-2 fuzzy set is involved to extend the membership values of each gait signatures by using several initial K in order to handle and manage uncertainty that exist in choosing initial K. The result of the experiments conducted on gait database show that the proposed gait recognition approach can obtain encouraging accurate recognition rate.Index Terms-Biometric, gait recognition, interval type-2 fuzzy KNN, PCA.