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Robots that can move autonomously and can make intelligent decisions by perceiving their environments and surrounding objects are known as autonomous mobile robots. Such robots have rapidly moved from laboratories to automated industries to fill a variety of roles in our lives, homes, offices, hospitals, industries, and even on the streets. The interest in mobile robots is growing rapidly, prompting an enormous amount of research over the last 30 years, on critical factors of mobile robots such as locomotion, perception, localization, mapping, ego-motion tracking, and dynamic navigation. This article surveys these essential factors of autonomous mobile robots in terms of mathematical modeling, control issues, and challenging factors. Brief discussions are provided on the fundamentals of these technologies, popular algorithms in comprehensive mode, future challenges, and promising directions to guide the construction of an autonomous mobile robot with high accuracy and effectiveness. Since it is difficult to find complete coverage of those topics in a single location, this article provides a guideline for researchers entering the field or for innovators in the mobile robotics sector. The paper also examines open challenges in indoor mobile robots and identifies potential futures for autonomous mobile robots.

Classifying user emails correctly from penetration of spam is an important research issue for anti-spam researchers. This paper has presented an effective and efficient email classification technique based on data filtering method. In our testing we have introduced an innovative filtering technique using instance selection method (ISM) to reduce the pointless data instances from training model and then classify the test data. The objective of ISM is to identify which instances (examples, patterns) in email corpora should be selected as representatives of the entire dataset, without significant loss of information. We have used WEKA interface in our integrated classification model and tested diverse classification algorithms. Our empirical studies show significant performance in terms of classification accuracy with reduction of false positive instances.

Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.INDEX TERMS Battery datasets, battery data repository, remaining useful life (RUL), battery management, li-ion battery, RUL prediction methods.

In this paper we propose a new technique of email classification based on grey list (GL) analysis of user emails. This technique is based on the analysis of output emails of an integrated model which uses multiple classifiers of statistical learning algorithms [8]. The GL is a list of classifier/(s) output which is/are not considered as true positive (TP) and true negative (TN) but in the middle of them. Many works have been done to filter spam from legitimate emails using classification algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection the FP problem is unacceptable, sometimes. The proposed technique will provide a list of output emails, called "grey list (GL)", to the analyser for making decisions about the status of these emails. It has been shown that the performance of our proposed technique for email classification is much better compare to existing systems, in order to reducing FP problems and accuracy.

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SUMMARYThis paper presents an innovative fusion-based multi-classifier e-mail classification on a ubiquitous multicore architecture. Many previous approaches used text-based single classifiers to identify spam messages from a large e-mail corpus with some amount of false positive tradeoffs. Researchers are trying to prevent false positive in their filtering methods, but so far none of the current research has claimed zero false positive results. In e-mail classification false positive can potentially cause serious problems for the user. In this paper, we use fusion-based multi-classifier classification technique in a multi-core framework. By running each classifier process in parallel within their dedicated core, we greatly improve the performance of our multi-classifier-based filtering system in terms of running time, false positive rate, and filtering accuracy. Our proposed architecture also provides a safeguard of user mailbox from different malicious attacks. Our experimental results show that we achieved an average of 30% speedup at an average cost of 1.4 ms. We also reduced the instances of false positives, which are one of the key challenges in a spam filtering system, and increases e-mail classification accuracy substantially compared with single classification techniques.

<p>Blood veins detection process can be cumbersome for nurses and medical practioners when it comes to special overweight type of patients.This simple routine procedure can lead the process into an extreme calamity for these patients. In this paper, we emphasized on a process for the detection of the vein in real time using the consecrations of Matlab to prevent or at least reduce the number of inescapable calamity for patients during the infusion of a needle by phlebotomy or doctor in everyday lives. Hemoglobin of the blood tissues engrossed the Near Infrared (NIR) illuminated light and Night vision camera is used to capture the scene and enhance the vein pattern clearly using Contrast Limited Adaptive Histogram Equalization (CLAHE) method. This simple approach can successfully also lead to localizing bleeding spots, clots from stroke …etc among other things.</p>