scholarly journals An Event-Driven Multiple Objects Surveillance System

2018 ◽  
Vol 9 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Saeed Mina Qaisar ◽  
Dija Sidiya ◽  
Mohammad Akbar ◽  
Abdulhamit Subasi
Keyword(s):  
Power Consumption ◽  
Surveillance System ◽  
Cost Effective ◽  
Self Organization ◽  
False Alarms ◽  
Sleep Mode ◽  
Surveillance Systems ◽  
Effective Combination ◽  
Event Driven ◽  
Processing Activity

Traditional surveillance systems are constrained because of a fixed and preset pattern of monitoring. It can reduce the reliability of the system and cause an increased generation of false alarms. It results in an increased processing activity of the system, which causes an augmented consumption of system resources and power. Within this framework, a human surveillance system is proposed based on the event-driven awakening and self-organization principle. The proposed system overcomes these downsides up to a certain level. It is achieved by intelligently merging an assembly of sensors with two cameras, actuators, a lighting module and cost-effective embedded processors. With the exception of low-power event detectors, all other system modules remain in the sleep mode. These modules are activated only upon detection of an event and as a function of the sensing environment condition. It reduces power consumption and processing activity of the proposed system. An effective combination of a sensor assembly and a robust classifier suppresses generation of false alarms and improves system reliability. An experimental setup is realized in order to verify the functionality of the proposed system. Results confirm proper functionality of the implemented system. A 62.3-fold system memory utilization and bandwidth consumption reduction compared to traditional counterparts is achieved, i.e. a result of the proposed system self-organization and event-driven awakening features. It confirms that the proposed system outperforms its classical counterparts in terms of processing activity, power consumption and usage of resources

scholarly journals Automatic Fire and Smoke Detection Method for Surveillance Systems Based on Dilated CNNs

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1241
Author(s):  
Yakhyokhuja Valikhujaev ◽  
Akmalbek Abdusalomov ◽  
Young Im Cho
Keyword(s):  
Cost Effective ◽  
Classification Performance ◽  
Detection Methods ◽  
False Alarms ◽  
Surveillance Systems ◽  
Smoke Detection ◽  
Detection Systems ◽  
Number Of False Alarms ◽  
Unseen Data ◽  
Vision Detection

The technologies underlying fire and smoke detection systems play a crucial role in ensuring and delivering optimal performance in modern surveillance environments. In fact, fire can cause significant damage to lives and properties. Considering that the majority of cities have already installed camera-monitoring systems, this encouraged us to take advantage of the availability of these systems to develop cost-effective vision detection methods. However, this is a complex vision detection task from the perspective of deformations, unusual camera angles and viewpoints, and seasonal changes. To overcome these limitations, we propose a new method based on a deep learning approach, which uses a convolutional neural network that employs dilated convolutions. We evaluated our method by training and testing it on our custom-built dataset, which consists of images of fire and smoke that we collected from the internet and labeled manually. The performance of our method was compared with that of methods based on well-known state-of-the-art architectures. Our experimental results indicate that the classification performance and complexity of our method are superior. In addition, our method is designed to be well generalized for unseen data, which offers effective generalization and reduces the number of false alarms.

scholarly journals An Event-Driven Efficient Segmentation and De-Noising of Multi-Channel EEG Signals

2018 ◽  
Author(s):  
S. M. Qaisar ◽  
R. Ramadan ◽  
A. Subasi
Keyword(s):  
Power Consumption ◽  
Eeg Signals ◽  
Local Characteristics ◽  
Consumption Reduction ◽  
Transmission Data ◽  
Event Driven ◽  
Processing Activity ◽  
New Generation ◽  
Time Invariant ◽  
Electroencephalogram Eeg

The segmentation and de-noising are basic operations, required in every signal processing and classification system. The classical segmentation and de-noising approaches are time-invariant. Consequently, it results in the post processing of an unnecessary information and causes an increase in the system processing activity and power consumption. In this context, an efficient event-driven segmentation and de-noising technique is proposed. It is founded on the principles of level crossing and activity selection. Therefore, it can adapt its sampling frequency, segmentation window length and position along with the filter order by analyzing the input signal local characteristics. As a result, the computational complexity and the power consumption of the proposed system is reduced compared to the counter ones. The suggested system performance is compared with the classical one. It is done for the case of a multi-channel Electroencephalogram (EEG) signals. Results show a noticeable compression gain with an effective adaptation of the de-noising filters order. It aptitudes a significant computational gain, transmission data rate reduction and power consumption reduction of the proposed technique, compared to the counter ones. It shows that the proposed solution is an attractive candidate to embed in the new generation EEG wearables.   

scholarly journals WSN-Based Height Estimation of Moving Object in Surveillance Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Jaeseok Shim ◽  
Yujin Lim
Keyword(s):  
Surveillance System ◽  
Human Detection ◽  
Wireless Sensor ◽  
False Alarms ◽  
Surveillance Systems ◽  
Detection Mechanism ◽  
Control Center ◽  
Object Based ◽  
Measured Information ◽  
Physical Height

In the WSN- (wireless sensor network-) based surveillance system to detect undesired intrusion, all detected objects are not intruders. In order to reduce false alarms, human detection mechanism needs to determine if the detected object is a human. For human detection, physical characteristics of human are usually used. In this paper, we use the physical height to differentiate an intruder from detected objects. Using the measured information from sensors, we estimate the height of the detected object. Based on the height, if the detected object is decided as an intruder, an alarm is given to a control center. The experimental results indicate that our mechanism correctly and fast estimates the height of the object without complex computation.

From Subsurface to Riser: An Integrated Approach to Subsea Surveillance

2010 ◽  
Author(s):  
Jon Machin
Keyword(s):  
Surveillance System ◽  
Production Control ◽  
High Reliability ◽  
Cost Effective ◽  
Integrated Approach ◽  
Production Optimization ◽  
Surveillance Systems ◽  
Physical Layers ◽  
Key Enabling Technologies ◽  
Reliability Performance

The high reliability performance of a subsea surveillance system, from subsurface to riser, is of the utmost importance for maximizing production availability. In designing such a surveillance system, there are a multitude of considerations that need to be addressed. These have traditionally focused on safe and cost effective production control system availability. However, they are now being extended to also address enablers for secondary recovery, production optimization, and increased recovery activities. This paper addresses the idea that latest-generation surveillance systems must operate seamlessly from the subsurface to the seabed and in turn from seabed to riser. In doing so they must integrate a number of key enabling technologies over different physical layers and predefined technical interfaces. They must also serve to integrate these technologies over the project management interfaces which arise from the selection of the different proprietary technologies, and the commercial and contractual barriers which can result.

Object detection and tracking for low-cost video surveillance system

2013 ◽  
Vol 18 (2-3) ◽  
pp. 91-99
Author(s):  
Jaromir Przybylo
Keyword(s):  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Low Cost ◽  
Elderly Person ◽  
Video Stream ◽  
Modern World ◽  
False Alarms ◽  
Surveillance Systems ◽  
Unusual Behavior

Abstract Automated and intelligent video surveillance systems play important role in the modern world. Since the amount of various video streams that must be analyzed grows, such artificial intelligence systems can assist humans in performing tiresome tasks. As a result, the effectiveness of response to a dangerous situations is increasing (detect unexpected movement or unusual behavior that may pose a threat to people, property and infrastructure). Video surveillance systems have to meet several requirements: must be accurate and not produce too many false alarms, moreover it must be able to process the received video stream in real-time to provide a sufficient response time. The work presented here focuses on the selected challenges of scene analysis in video surveillance systems (object detection/tracking, effectiveness of the whole system). The aim of the research is to design a low-budget surveillance system, that can be used for example in a home security monitoring. Such solution can be use not only to surveillance but also to monitor elderly person at home or provide new ways of interacting in human-computer interaction systems.

Quality Assessment of Hospital Discharge Database for Routine Surveillance of Hip and Knee Arthroplasty–Related Infections

2014 ◽  
Vol 35 (6) ◽  
pp. 646-651 ◽  
Author(s):  
Leslie Grammatico-Guillon ◽  
Sabine Baron ◽  
Christophe Gaborit ◽  
Emmanuel Rusch ◽  
Pascal Astagneau
Keyword(s):  
Hospital Discharge ◽  
Knee Arthroplasty ◽  
Predictive Value ◽  
Surveillance System ◽  
Cost Effective ◽  
International Classification Of Diseases ◽  
Case Definition ◽  
Surveillance Systems ◽  
Routine Surveillance ◽  
Hospital Stays

Objective.Surgical site infection (SSI) surveillance represents a key method of nosocomial infection control programs worldwide. However, most SSI surveillance systems are considered to be poorly cost effective regarding human and economic resources required for data collection and patient follow up. This study aims to assess the efficacy of using hospital discharge databases (HDDs) as a routine surveillance system for detecting hip or knee arthroplasty–related infections (HKAIs).Methods.A case-control study was conducted among patients hospitalized in the Centre region of France between 2008 and 2010. HKAI cases were extracted from the HDD with various algorithms based on the International Classification of Diseases, Tenth Revision, and procedure codes. The control subjects were patients with hip or knee arthroplasty (HKA) without infection selected at random from the HDD during the study period. The gold standard was medical chart review. Sensitivity (Se), specificity (Spe), positive predictive value (PPV), and negative predictive value (NPV) were calculated to evaluate the efficacy of the surveillance system.Results.Among 18,265 hospital stays for HKA, corresponding to 17,388 patients, medical reports were checked for 1,010 hospital stays (989 patients). We identified 530 cases in total (incidence rate, 1% [95% confidence interval (CI), 0.4%–1.6%), and 333 cases were detected by routine surveillance. As compared with 480 controls, Se was 98%, Spe was 71%, PPV was 63%, and NPV was 99%. Using a more specific case definition, based on a sample of 681 hospital stays, Se was 97%, Spe was 95%, PPV was 87%, and NPV was 98%.Conclusions.This study demonstrates the potential of HDD as a tool for routine SSI surveillance after low-risk surgery, under conditions of having an appropriate algorithm for selecting infections.Infect Control Hosp Epidemiol 2014;35(6):646–651

scholarly journals Evaluation of an Electronic Smart-Card Based School Absenteeism Surveillance System

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Dennis Ip ◽  
Eric H.Y. Lau ◽  
Yat-hung Tam ◽  
Teresa So ◽  
Chi-kin Lam ◽  
...  
Keyword(s):  
Smart Card ◽  
Surveillance System ◽  
Data Stream ◽  
Potential Benefit ◽  
Cost Effective ◽  
Emerging Diseases ◽  
Surveillance Systems ◽  
School Absenteeism ◽  
Community Settings ◽  
Card System

We evaluated the performance of an electronic smart-card based school absenteeism surveillance system which was initiated in 2008 in Hong Kong. The result demonstrated the feasibility and potential benefit of employing electronic school absenteeism data as captured automatically by a smart card system as an alternative data stream for monitoring influenza activities, and flexibility in establishing surveillance for emerging diseases. The increasing popularity of usage of smart card technology in various community settings might also represent potentially timely and cost-effective opportunities for innovative surveillance systems.

Situational Strategic Awareness Monitoring Surveillance System–Microcomputer and Microcomputer Clustering used for Intelligent, Economical, Scalable, and Deployable Approach for Safeguarding Materials

2020 ◽  
Vol 2020 (3) ◽  
pp. 60408-1-60408-10
Author(s):  
Kenly Maldonado ◽  
Steve Simske
Keyword(s):  
Surveillance System ◽  
Cost Effective ◽  
Government Agencies ◽  
Electronic Systems ◽  
Time Data ◽  
Potential Threat ◽  
Intelligent Surveillance ◽  
World Markets ◽  
Programming Software ◽  
And Control

The principal objective of this research is to create a system that is quickly deployable, scalable, adaptable, and intelligent and provides cost-effective surveillance, both locally and globally. The intelligent surveillance system should be capable of rapid implementation to track (monitor) sensitive materials, i.e., radioactive or weapons stockpiles and person(s) within rooms, buildings, and/or areas in order to predict potential incidents proactively (versus reactively) through intelligence, locally and globally. The system will incorporate a combination of electronic systems that include commercial and modifiable off-the-shelf microcomputers to create a microcomputer cluster which acts as a mini supercomputer which leverages real-time data feed if a potential threat is present. Through programming, software, and intelligence (artificial intelligence, machine learning, and neural networks), the system should be capable of monitoring, tracking, and warning (communicating) the system observer operations (command and control) within a few minutes when sensitive materials are at potential risk for loss. The potential customer is government agencies looking to control sensitive materials and/or items in developing world markets intelligently, economically, and quickly.

Derivation of New Surveillance System for Influenza-like Illness Using Claims Data 2014-2018 in Korea (Preprint)

2020 ◽  
Author(s):  
HeeKyung Choi ◽  
Won Suk Choi ◽  
Euna Han
Keyword(s):  
Surveillance System ◽  
Claims Data ◽  
Health Concern ◽  
Influenza Surveillance ◽  
Utilization Review ◽  
Surveillance Systems ◽  
Influenza Like Illness ◽  
System P ◽  
National Patient ◽  
Antitussive Agents

BACKGROUND Influenza is an important public health concern. A national surveillance system that easily and rapidly detects influenza epidemics is lacking. OBJECTIVE We assumed that the rate of influenza-like illness (ILI) related-claims is similar to the current ILI surveillance system. METHODS We used the Health Insurance Review and Assessment Service-National Patient Samples (HIRA-NPS), 2014-2018. We defined ILI-related claims as outpatient claims that contain both antipyretic and antitussive agents and calculated the weekly rate of ILI-related claims. We compared ILI-related claims and weekly ILI rates from clinical sentinel surveillance data. RESULTS We observed a strong correlation between the two surveillance systems each season. The absolute thresholds for the four-years were 84.64 and 86.19 cases claims per 1,000 claims for claims data and 12.27 and 16.82 per 1,000 patients for sentinel data (Figure 5). Both the claims and sentinel data surpassed the epidemic thresholds each season. The peak epidemic in the claims data was reached one to two weeks later than in the sentinel data. The epidemic patterns were more similar in the 2016-2017 and 2017-2018 seasons than the 2014-2015 and 2015-2016 seasons. CONCLUSIONS Based on hospital reports, ILI-related claims rates were similar to the ILI surveillance system. ILI claims data can be loaded to a drug utilization review system in Korea to make an influenza surveillance system.

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