scholarly journals ICE-MoCha: Intelligent Crowd Engineering using Mobility Characterization and Analytics

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1025 ◽  
Author(s):  
Abdoh Jabbari ◽  
Khalid Almalki ◽  
Baek-Young Choi ◽  
Sejun Song
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Power Efficiency ◽  
Large Scale ◽  
Safety Management ◽  
Crowd Management ◽  
Detection And Identification ◽  
Direction Detection ◽  
A Minor ◽  
Mobile Crowd

Human injuries and casualties at entertaining, religious, or political crowd events often occur due to the lack of proper crowd safety management. For instance, for a large scale moving crowd, a minor accident can create a panic for the people to start stampede. Although many smart video surveillance tools, inspired by the recent advanced artificial intelligence (AI) technology and machine learning (ML) algorithms, enable object detection and identification, it is still challenging to predict the crowd mobility in real-time for preventing potential disasters. In this paper, we propose an intelligent crowd engineering platform using mobility characterization and analytics named ICE-MoCha. ICE-MoCha is to assist safety management for mobile crowd events by predicting and thus helping to prevent potential disasters through real-time radio frequency (RF) data characterization and analysis. The existing video surveillance based approaches lack scalability thus have limitations in its capability for wide open areas of crowd events. Via effectively integrating RF signal analysis, our approach can enhance safety management for mobile crowd. We particularly tackle the problems of identification, speed, and direction detection for the mobile group, among various crowd mobility characteristics. We then apply those group semantics to track the crowd status and predict any potential accidents and disasters. Taking the advantages of power-efficiency, cost-effectiveness, and ubiquitous availability, we specifically use and analyze a Bluetooth low energy (BLE) signal. We have conducted experiments of ICE-MoCha in a real crowd event as well as controlled indoor and outdoor lab environments. The results show the feasibility of ICE-MoCha detecting the mobile crowd characteristics in real-time, indicating it can effectively help the crowd management tasks to avoid potential crowd movement related incidents.

scholarly journals SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 911 ◽  
Author(s):  
Md Azher Uddin ◽  
Aftab Alam ◽  
Nguyen Anh Tu ◽  
Md Siyamul Islam ◽  
Young-Koo Lee
Keyword(s):  
Distributed Computing ◽  
Real Time ◽  
Video Surveillance ◽  
Video Processing ◽  
Large Scale ◽  
Distributed Processing ◽  
Content Management ◽  
Video Data ◽  
Video Analytics ◽  
Intelligent Video Surveillance

In recent years, the amount of intelligent CCTV cameras installed in public places for surveillance has increased enormously and as a result, a large amount of video data is produced every moment. Due to this situation, there is an increasing request for the distributed processing of large-scale video data. In an intelligent video analytics platform, a submitted unstructured video undergoes through several multidisciplinary algorithms with the aim of extracting insights and making them searchable and understandable for both human and machine. Video analytics have applications ranging from surveillance to video content management. In this context, various industrial and scholarly solutions exist. However, most of the existing solutions rely on a traditional client/server framework to perform face and object recognition while lacking the support for more complex application scenarios. Furthermore, these frameworks are rarely handled in a scalable manner using distributed computing. Besides, existing works do not provide any support for low-level distributed video processing APIs (Application Programming Interfaces). They also failed to address a complete service-oriented ecosystem to meet the growing demands of consumers, researchers and developers. In order to overcome these issues, in this paper, we propose a distributed video analytics framework for intelligent video surveillance known as SIAT. The proposed framework is able to process both the real-time video streams and batch video analytics. Each real-time stream also corresponds to batch processing data. Hence, this work correlates with the symmetry concept. Furthermore, we introduce a distributed video processing library on top of Spark. SIAT exploits state-of-the-art distributed computing technologies with the aim to ensure scalability, effectiveness and fault-tolerance. Lastly, we implant and evaluate our proposed framework with the goal to authenticate our claims.

Topic in Music: An Empirical Study of Memorability, Openness, and Emotion in Mozart's String Quintet in C Major and Beethoven's String Quartet in A Minor

1998 ◽  
Vol 16 (1) ◽  
pp. 119-134 ◽  
Author(s):  
Carol L. Krumhansl
Keyword(s):  
Empirical Study ◽  
Real Time ◽  
String Quartet ◽  
Large Scale ◽  
Musical Training ◽  
Discourse Structure ◽  
Musical Form ◽  
Psychological Reality ◽  
String Quintet ◽  
A Minor

This study examines possible parallels between large-scale organization in music and discourse structure. Two experiments examine the psychological reality of topics in the first movements of W. A. Mozart's String Quintet No. 3 in C major, K. 515, and L. van Beethoven's String Quartet No. 15 in A minor, Op. 132. Listeners made real-time judgments on three continuous scales: memorability, openness, and amount of emotion. All three kinds of judgments could be accounted for by the topics identified in these pieces by Agawu (1991) independently of the listeners' musical training. The results showed hierarchies of topics. However, these differed for the three tasks and for the two pieces. The topics in the Mozart piece appear to function as a way of establishing the musical form, whereas the topics in the Beethoven piece are more strongly associated with emotional content.

An Energy-Aware Real-Time VM-Provisioning Framework For Heterogeneous Data Centres

2021 ◽  
Author(s):  
Salam Ismaeel
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Power Efficiency ◽  
Large Scale ◽  
Virtual Machines ◽  
Heterogeneous Data ◽  
Energy Aware ◽  
Time Dynamic ◽  
Data Centre ◽  
Data Centres

<div>Increasing power efficiency is one of the most important operational factors for any data centre providers. In this context, one of the most useful approaches is to reduce the number of utilized Physical Machines (PMs) through optimal distribution and re-allocation of Virtual Machines (VMs) without affecting the Quality of Service (QoS). Dynamic VMs provisioning makes use of monitoring tools, historical data, prediction techniques, as well as placement algorithms to improve VMs allocation and migration. Consequently, the efficiency of the data centre energy consumption increases.</div><div>In this thesis, we propose an efficient real-time dynamic provisioning framework to reduce energy in heterogeneous data centres. This framework consists of an efficient workload preprocessing, systematic VMs clustering, a multivariate prediction, and an optimal Virtual Machine Placement (VMP) algorithm. Additionally, it takes into consideration VM and user behaviours along with the existing state of PMs. The proposed framework consists of a pipeline successive subsystems. These subsystems could be used separately or combined to improve accuracy, efficiency, and speed of workload clustering, prediction and provisioning purposes.<br></div><div>The pre-processing and clustering subsystems uses current state and historical workload data to create efficient VMs clusters. Efficient VMs clustering include less consumption resources, faster computing and improved accuracy. A modified multivariate Extreme Learning Machine (ELM)-based predictor is used to forecast the number of VMs in each cluster for the subsequent period. The prediction subsystem takes users’ behaviour into consideration to exclude unpredictable VMs requests.<br></div><div>The placement subsystem is a multi-objective placement algorithm based on a novel Machine Condition Index (MCI). MCI represents a group of weighted components that is inclusive of data centre network, PMs, storage, power system and facilities used in any data centre. In this study it will be used to measure the extent to which PM is deemed suitable for handling the new and/or consolidated VM in large scale heterogeneous data centres. It is an efficient tool for comparing server energy consumption used to augment the efficiency and manageability of data centre resources.</div><div> The proposed framework components separately are tested and evaluated with both synthetic and realistic data traces. Simulation results show that proposed subsystems can achieve efficient results as compared to existing algorithms. <br></div>

scholarly journals A novel high-throughput method for molecular serotyping and serotype-specific quantification of Streptococcus pneumoniae using a nanofluidic real-time PCR system

2014 ◽  
Vol 63 (4) ◽  
pp. 528-539 ◽  
Author(s):  
Bhim Gopal Dhoubhadel ◽  
Michio Yasunami ◽  
Lay-Myint Yoshida ◽  
Hien Anh Nguyen Thi ◽  
Thu Huong Vu Thi ◽  
...  
Keyword(s):  
Streptococcus Pneumoniae ◽  
Real Time ◽  
Real Time Pcr ◽  
Copy Number ◽  
Large Scale ◽  
Nasopharyngeal Swab ◽  
System A ◽  
Chest X Ray ◽  
A Minor ◽  
Sensitivity Specificity

Serotype-specific quantification data are essential for elucidating the complex epidemiology of Streptococcus pneumoniae and evaluating pneumococcal vaccine efficacy. Various PCR-based assays have been developed to circumvent the drawback of labour-intensive and time-consuming culture-based procedures for serotype determination and quantification of pneumococcus. Here, we applied a nanofluidic real-time PCR system to establish a novel assay. Twenty-nine primer pairs, 13 of which were newly designed, were selected for the assay to cover 50 serotypes including all currently available conjugate and polysaccharide vaccine serotypes. All primer pairs were evaluated for their sensitivity, specificity, efficiency, repeatability, accuracy and reproducibility on the Fluidigm Biomark HD System, a nanofluidic real-time PCR system, by drawing standard curves with a serial dilution of purified DNA. We applied the assay to 52 nasopharyngeal swab samples from patients with pneumonia confirmed by chest X-ray to validate its accuracy. Minimum detection levels of this novel assay using the nanofluidic real-time PCR system were comparable to the conventional PCR-based assays (between 30 and 300 copies per reaction). They were specific to their targets with good repeatability (sd of copy number of 0.1), accuracy (within ±0.1 fold difference in log10 copy number) and reproducibility (sd of copy number of 0.1). When artificially mixed DNA samples consisting of multiple serotypes in various ratios were tested, all the serotypes were detected proportionally, including a minor serotype of one in 1000 copies. In the nasopharyngeal samples, the PCR system detected all the culture-positive samples and 22 out of 23 serotypes identified by the conventional method were matched with PCR results. We conclude that this novel assay, which is able to differentially quantify 29 pneumococcus groups for 45 test samples in a single run, is applicable to the large-scale epidemiological study of pneumococcus. We believe that this assay will facilitate our understanding of the roles of serotype-specific bacterial loads and implications of multiple serotype detections in pneumococcal diseases.

scholarly journals A real-time control approach based on intelligent video surveillance for violations by construction workers

2018 ◽  
Vol 24 (1) ◽  
pp. 67-78 ◽  
Author(s):  
Shengyu GUO ◽  
Chaohua XIONG ◽  
Peisong GONG
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Safety Management ◽  
Dynamic Monitoring ◽  
Real Time Control ◽  
Intelligent Video Surveillance ◽  
Control Approach ◽  
Time Control ◽  
Unsafe Behavior ◽  
Unsafe Behaviors

The unsafe behavior of workers is the main object of construction safety management, in which violations require increased attention due to their pernicious consequences. However, existing studies have merely discussed violations separately from unsafe behaviors. To respond quickly to workers’ violations on site, this study proposes a real-time control approach based on intelligent video surveillance. First, scenes reflecting unsafe behaviors are automatically acquired through camera-based behavior analysis technology. Meanwhile, the time corresponding to the construction phase is recorded. Second, the temporal association rule model of worker’s unsafe behavior is constructed, and the rule “construction phase→unsafe behavior” is determined by the Apriori algorithm to identify target behaviors necessary for critical control in different construction phases. Finally, statistical process control is used to find the trends of violations with frequency and mass characteristics through the dynamic monitoring of target behavior. In addition, real-time alerts of these unsafe acts are produced simultaneously. A pilot study is conducted on the cross-river tunnel project in Wuhan city, Hubei, China, and the violations related to construction machineries is proven to be controllable. Thus, the proposed approach promotes behavioral safety management on construction since it effectively controls workers’ violations by real-time monitoring and analysis.

An Energy-Aware Real-Time VM-Provisioning Framework For Heterogeneous Data Centres

2021 ◽  
Author(s):  
Salam Ismaeel
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Power Efficiency ◽  
Large Scale ◽  
Virtual Machines ◽  
Heterogeneous Data ◽  
Energy Aware ◽  
Time Dynamic ◽  
Data Centre ◽  
Data Centres

<div>Increasing power efficiency is one of the most important operational factors for any data centre providers. In this context, one of the most useful approaches is to reduce the number of utilized Physical Machines (PMs) through optimal distribution and re-allocation of Virtual Machines (VMs) without affecting the Quality of Service (QoS). Dynamic VMs provisioning makes use of monitoring tools, historical data, prediction techniques, as well as placement algorithms to improve VMs allocation and migration. Consequently, the efficiency of the data centre energy consumption increases.</div><div>In this thesis, we propose an efficient real-time dynamic provisioning framework to reduce energy in heterogeneous data centres. This framework consists of an efficient workload preprocessing, systematic VMs clustering, a multivariate prediction, and an optimal Virtual Machine Placement (VMP) algorithm. Additionally, it takes into consideration VM and user behaviours along with the existing state of PMs. The proposed framework consists of a pipeline successive subsystems. These subsystems could be used separately or combined to improve accuracy, efficiency, and speed of workload clustering, prediction and provisioning purposes.<br></div><div>The pre-processing and clustering subsystems uses current state and historical workload data to create efficient VMs clusters. Efficient VMs clustering include less consumption resources, faster computing and improved accuracy. A modified multivariate Extreme Learning Machine (ELM)-based predictor is used to forecast the number of VMs in each cluster for the subsequent period. The prediction subsystem takes users’ behaviour into consideration to exclude unpredictable VMs requests.<br></div><div>The placement subsystem is a multi-objective placement algorithm based on a novel Machine Condition Index (MCI). MCI represents a group of weighted components that is inclusive of data centre network, PMs, storage, power system and facilities used in any data centre. In this study it will be used to measure the extent to which PM is deemed suitable for handling the new and/or consolidated VM in large scale heterogeneous data centres. It is an efficient tool for comparing server energy consumption used to augment the efficiency and manageability of data centre resources.</div><div> The proposed framework components separately are tested and evaluated with both synthetic and realistic data traces. Simulation results show that proposed subsystems can achieve efficient results as compared to existing algorithms. <br></div>

scholarly journals Advances and Trends in Real Time Visual Crowd Analysis

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5073
Author(s):  
Khalil Khan ◽  
Waleed Albattah ◽  
Rehan Ullah Khan ◽  
Ali Mustafa Qamar ◽  
Durre Nayab
Keyword(s):  
Real Time ◽  
Large Scale ◽  
State Of The Art ◽  
Research Work ◽  
Future Research ◽  
Crowd Analysis ◽  
Crowd Management ◽  
Public Events ◽  
Advantages And Disadvantages ◽  
Art Methods

Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large scale applications in people counting, public events management, disaster management, safety monitoring an so on. Although many sophisticated algorithms have been developed to address the task; crowd management in real time conditions is still a challenging problem being completely solved, particularly in wild and unconstrained conditions. In the proposed paper, we present a detailed review of crowd analysis and management, focusing on state-of-the-art methods for both controlled and unconstrained conditions. The paper illustrates both the advantages and disadvantages of state-of-the-art methods. The methods presented comprise the seminal research works on crowd management, and monitoring and then culminating state-of-the-art methods of the newly introduced deep learning methods. Comparison of the previous methods is presented, with a detailed discussion of the direction for future research work. We believe this review article will contribute to various application domains and will also augment the knowledge of the crowd analysis within the research community.

Interference mitigation in point to point wireless sensor networks using LSP protocol and time division multiplexing approach

2020 ◽  
Vol 39 (4) ◽  
pp. 5449-5458
Author(s):  
A. Arokiaraj Jovith ◽  
S.V. Kasmir Raja ◽  
A. Razia Sulthana
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Control Parameter ◽  
Interference Mitigation ◽  
Wireless Sensor ◽  
Point To Point ◽  
A Minor ◽  
Two Stages ◽  
Time Division ◽  
And Control

Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission.

Real-time Worker Safety Management System Using Deep Learning-based Video Analysis Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 25-30
Author(s):  
So Yeon Jeon ◽  
Jong Hwa Park ◽  
Sang Byung Youn ◽  
Young Soo Kim ◽  
Yong Sung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Analysis ◽  
Management System ◽  
Safety Management ◽  
Worker Safety ◽  
Safety Management System ◽  
Analysis Algorithm
Sign in / Sign up

Export Citation Format

Share Document