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Queueing Theory With Applications To Packet Telecommunication

The task of crowd counting and density map estimation is riddled with many challenges, such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Due to the development of deep learning and large crowd datasets in recent years, most crowd counting methods have achieved notable success. This paper aims to solve crowd density estimation problem for both sparse and dense conditions. To this end, we make two contributions: (1) a network named Patch Scale Discriminant Regression Network (PSDR). Given an input crowd image, it divides the image into patches and sends image patches of different density levels into different regression networks to get the corresponding density maps. It combines all patch density maps to predict the entire density map as the output. (2) A person classification activation map (CAM) method. CAM provides person location information and guides the generation of the entire density map in the final stage. Experiment confirms that CAM allows PSDR to gain another round of performance boost. For instance, on the SmartCity dataset, we achieve (8.6–1.1) MAE and (11.6–1.4) MSE. Our method combining above two methods performs better than state-of-the-art methods.

Crowd density estimation based on classification activation map and patch density level

Kernel flexible and displaceable convex hull based tensor machine for gearbox fault intelligent diagnosis with multi-source signals

Internet of things (IoT) is developed to enhance easy communication by creating a large network of billions and trillions of entities. It serves an important role in future technologies about vast attention from different organizations. IoT is integrating a number of software and applications from different cloud services. This chapter presents the current IoT application, current cloud applications, current cloud applications based on IoT, future trends of IoT, and IoT-based cloud applications and their future research challenges.

The Co-Evolution of Cloud and IoT Applications: Recent and Future Trends

We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

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A Survey of Human Gait-Based Artificial Intelligence Applications

Cloud based video surveillance systems have been proposed and implemented recently. With the advances in cloud technologies, opportunity for getting on-demand remote video surveillance service can be pursued. In this paper, we propose a novel remote display solution that allows remote surveillance users to watch real-time surveillance video, to use surveillance software and to share screen updates among users on remote desktop. Multiple encoders and parallel encoding method are adopted in remote display to meet quality of service requirement under varying situations. Our proposed system deals with dynamic workload better than traditional remote display methods since surveillance task and encoding task are separately managed. Two queuing models are designed to handle resource provisioning problem for different encoders.

Design and Evaluation of Remote Video Surveillance System on Private Cloud

Multilinear rank support tensor machine for crowd density estimation

Cloud computing offers sufficient computing and storage resources that can be used to provide multimedia services. Migrating the existing multimedia service to cloud brings a new challenging issue, i.e., remote display of video contents. To reduce the bandwidth consumption especially for mobile users, it is desired to encode video before sending to client. Existing encoding methods have unique advantages and disadvantages, differing their performance under varying situations. Thus, we propose to use multi-encoder method to solve the remote display problem for remote multimedia cloud. To select the most appropriate encoder, factors including cost, application requirement, network, client device and codec implementation are considered. In this paper, we form a non-linear programming model, and take M-JPEG encoder as an example to illustrate how to apply the proposed model for getting desired optimization.

Bingyin Zhou

Papers

Design and Evaluation of Remote Video Surveillance System on Private Cloud

Multilinear rank support tensor machine for crowd density estimation

Novel remote display method for multimedia cloud