Conurbations around the globe are encountering the challenges of traffic congestion. Miscellaneous smart systems have been developed to help control and improve traffic flow in a cost-effective and measurable way. Yet, the existing systems solve the traffic congestion problem with cost-intensive traffic lights and mostly handle emergency cases poorly. In this article, to minimize the traffic congestion problems for any kind of vehicles without using traffic lights, we propose three deadlock-free algorithms namely: (i) Mutual exclusion algorithm based on single instruction (MEASIR), (ii) Mutual exclusion algorithm based on priority (MEAPRI), and (iii) Mutual exclusion algorithm based on multi-agent systems (MEAMAS). Communication inside a group is accomplished via a queue structure, while an external element (e.g., a router) is used for internal communications. Besides the depiction of experimental and simulation results, a complete statistical analysis has been performed to compare the performance of MEASIR, MEAPRI, and MEAMAS with their alternatives. Our proposed deadlock-free algorithms are not only efficient but also functional with a computational cost of $O(n)$ to enter the critical section, where $n$ represents the number of all vehicles in a particular intersection.

/pdf/distributed-mutual-exclusion-algorithms-for-intersection-2gqi90xzmj.pdf

Distributed Mutual Exclusion Algorithms for Intersection Traffic Problems

Privacy of people is a key factor in surveillance systems. Video camera brings us well-off color information. How would the privacy be secured then? Besides, privacy protection should not create a hindrance for finding of objects or people under specific cases. Laser scanner takes way affluent color information. It functions with eye-safe and invisible laser beam. Yet, it provides us robust object recognition map. Images can be interpreted by humans, but laser-based systems need software applications to explain the data. Camera-based surveillance system does not focus on the problem of private life conservation. On the contrary, laser-based surveillance system ensures privacy of people inherently, as it does not record real world videos except laser scanned data points. In this paper, first, the privacy issues of people for both surveillance systems have been compared to realize their significance. Second, a qualitative performance comparison between laser-based and RGB camera-based systems has been made to hint that laser-based algorithms should be used instead of common RGB cameras. Third, a succinct survey of laser-based detection and tracking algorithms of movers has been conducted. Final, a superiority measure of the leading laser-based people-vehicles related algorithms has been performed on the basis of statistical test scores deeming the ineffectualness metrics (e.g., errors and failures) of each algorithm.

/pdf/laser-based-algorithms-meeting-privacy-in-surveillance-a-1xrul6cthc.pdf

Laser-Based Algorithms Meeting Privacy in Surveillance: A Survey

Sentiment analysis is a process of computationally detecting and classifying opinions written in a piece of writer’s text. It determines the writer’s impression as achromatic or negative or positive. Sentiment analysis became unsophisticated due to the invention of Internet-based societal media. At present, usually people express their opinions by dint of Twitter. Henceforth, Twitter is a fascinating medium for researchers to perform data analysis. In this paper, we address a handful of methods to prognosticate the sentiment on Turkish tweets by taking up polarity lexicon as well as artificial intelligence. The polarity lexicon method uses a dictionary of words and accords with the words among the harvested tweets. The tweets are then grouped into either positive tweets or negative tweets or neutral tweets. The methods of artificial intelligence use either individually or combined classifiers e.g., support vector machine (SVM), random forest (RF), maximum entropy (ME), and decision tree (DT) for categorizing positive tweets, negative tweets, and neutral tweets. To analyze sentiment, a total of 13000 Turkish tweets are collected from Twitter with the help of Twitter’s application programming interface (API). Experimental results show that the mean performance of our proposed methods is greater than 72%.

Sentiment Analysis of Turkish Twitter Data Using Polarity Lexicon and Artificial Intelligence

Leg tracking is an established field in mobile robotics and machine vision in general. These algorithms, however, only distinguish the scene between leg and nonleg detections. In application fields like firefighting, where people tend to choose squatting or crouching over standing postures, those methods will inevitably fail. Further, tracking based on a single sensor system may reduce the overall reliability if brought to outdoor or complex environments with limited vision on the target objectives. Therefore, we extend our recent work to a multiposture detection system based on laser and radar sensors, that are fused to allow for maximal reliability and accuracy in scenarios as complex as indoor firefighting with vastly limited vision. The proposed tracking pipeline is trained and extensively validated on a new data set. We show that the radar tracker reaches state-of-the-art performance, and that laser and fusion tracker outperform recent methods. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/rob.22195

Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

Laser scanner takes away the problem of private life conservation as it does not record real world videos except scanned data points. So it shows many benefits over the use of video camera. This paper portrays an approach to detect and track movers from laser scanned datasets. Laser scanned data points from each scan are deemed as a video frame. Blobs are extracted from each frame. Support vector machine (SVM) and Hungarian method along with particle filter are used to get trajectories of movers. Experimental results on the identical laser scanned dataset demonstrate that the approach of SVM with Hungarian method using particle filter outperforms both the threshold based approach with Hungarian method using Kalman filter and the approach of SVM with Hungarian method using Kalman filter.

Md. Haidar Sharif

Papers

Particle Filter for Trajectories of Movers from Laser Scanned Dataset