Excellence in Research and Innovation for Humanity

International Science Index

Commenced in January 1999 Frequency: Monthly Edition: International Paper Count: 11

11
10008750
Surveillance for African Swine Fever and Classical Swine Fever in Benue State, Nigeria
Abstract:
A serosurveillance study was conducted to detect the presence of antibodies to African swine fever virus (ASFV) and Classical swine fever virus in pigs sampled from piggeries and Makurdi central slaughter slab in Benue State, Nigeria. 416 pigs from 74 piggeries across 12 LGAs and 44 pigs at the Makurdi central slaughter slab were sampled for serum. The sera collected were analysed using Indirect Enzyme Linked Immunosorbent Assay (ELISA) test kit to test for antibodies to ASFV, while competitive ELISA test kit was used to test for antibodies to CSFV. Of the 416 pigs from piggeries and 44 pigs sampled from the slaughter slab, seven (1.7%) and six (13.6%), respectively, tested positive to ASFV antibodies and was significantly associated (p < 0.0001). Out of the 12 LGAs sampled, Obi LGA had the highest ASFV antibody detection rate of (4.8%) and was significantly associated (p < 0.0001). None of the samples tested positive to CSFV antibodies. The study concluded that antibodies to CSFV were absent in the sampled pigs in piggeries and at the Makurdi central slaughter slab in Benue State, while antibodies to ASFV were present in both locations; hence, the need to keep an eye open for CSF too since both diseases may pose great risk in the study area. Further studies to characterise the ASFV circulating in Benue State and investigate the possible sources is recommended. Routine surveillance to provide a comprehensive and readily accessible data base to plan for the prevention of any fulminating outbreak is also recommended.
Digital Article Identifier (DAI):
10
10009192
Description of Reported Foodborne Diseases in Selected Communities within the Greater Accra Region-Ghana: Epidemiological Review of Surveillance Data
Abstract:

Background: Acute gastroenteritis is one of the frequently reported Out-Patient Department (OPD) cases. However, the causative pathogens of these cases are rarely identified at the OPD due to delay in laboratory results or failure to obtain specimens before antibiotics is administered. Method: A retrospective review of surveillance data from the Adentan Municipality, Accra, Ghana that were recorded in the National foodborne disease surveillance system of Ghana, was conducted with the main aim of describing the epidemiology and food practice of cases reported from the Adentan Municipality. The study involved a retrospective review of surveillance data kept on patients who visited health facilities that are involved in foodborne disease surveillance in Ghana, from January 2015 to December 2016. Results: A total of 375 cases were reviewed and these were classified as viral hepatitis (hepatitis A and E), cholera (Vibrio cholerae), dysentery (Shigella sp.), typhoid fever (Salmonella sp.) or gastroenteritis. Cases recorded were all suspected case and the average cases recorded per week was 3. Typhoid fever and dysentery were the two main clinically diagnosed foodborne illnesses. The highest number of cases were observed during the late dry season (Feb to April), which marks the end of the dry season and the beginning of the rainy season. Relatively high number of cases was also observed during the late wet seasons (Jul to Oct) when the rainfall is the heaviest. Home-made food and street vended food were the major sources of suspected etiological food, recording 49.01% and 34.87% of the cases respectively. Conclusion: Majority of cases recorded were classified as gastroenteritis due to the absence of laboratory confirmation. Few cases were classified as typhoid fever and dysentery based on clinical symptoms presented. Patients reporting with foodborne diseases were found to consume home meal and street vended foods as their predominant source of food.

Digital Article Identifier (DAI):
9
10005435
Video Based Ambient Smoke Detection By Detecting Directional Contrast Decrease
Abstract:
Fire-related incidents account for extensive loss of life and material damage. Quick and reliable detection of occurring fires has high real world implications. Whereas a major research focus lies on the detection of outdoor fires, indoor camera-based fire detection is still an open issue. Cameras in combination with computer vision helps to detect flames and smoke more quickly than conventional fire detectors. In this work, we present a computer vision-based smoke detection algorithm based on contrast changes and a multi-step classification. This work accelerates computer vision-based fire detection considerably in comparison with classical indoor-fire detection.
Digital Article Identifier (DAI):
8
10003795
Occurrence of Foreign Matter in Food: Applied Identification Method - Association of Official Agricultural Chemists (AOAC) and Food and Drug Administration (FDA)
Abstract:
The aim of this study is to present the results of a retrospective survey on the foreign matter found in foods analyzed at the Adolfo Lutz Institute, from July 2001 to July 2015. All the analyses were conducted according to the official methods described on Association of Official Agricultural Chemists (AOAC) for the micro analytical procedures and Food and Drug Administration (FDA) for the macro analytical procedures. The results showed flours, cereals and derivatives such as baking and pasta products were the types of food where foreign matters were found more frequently followed by condiments and teas. Fragments of stored grains insects, its larvae, nets, excrement, dead mites and rodent excrement were the most foreign matter found in food. Besides, foreign matters that can cause a physical risk to the consumer’s health such as metal, stones, glass, wood were found but rarely. Miscellaneous (shell, sand, dirt and seeds) were also reported. There are a lot of extraneous materials that are considered unavoidable since are something inherent to the product itself, such as insect fragments in grains. In contrast, there are avoidable extraneous materials that are less tolerated because it is preventable with the Good Manufacturing Practice. The conclusion of this work is that although most extraneous materials found in food are considered unavoidable it is necessary to keep the Good Manufacturing Practice throughout the food processing as well as maintaining a constant surveillance of the production process in order to avoid accidents that may lead to occurrence of these extraneous materials in food.
Digital Article Identifier (DAI):
7
10001733
Automatic Motion Trajectory Analysis for Dual Human Interaction Using Video Sequences
Abstract:

Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.). 

Digital Article Identifier (DAI):
6
12826
The Masquerade of Life, Our Many Selves and Issues of Privacy
Abstract:
This paper explores the importance of privacy in a contemporary online world. Crucial to the discussion is the idea of the Lacanian postmodern fragmented self and the problem of how to ensure that we have room to fully explore various aspects of our personalities in an environment which is–or at least feels--safe and free from observation by others. The paper begins with an exploration of the idea of the self with particular regard to the ways in which contemporary life and technology seems to have multiplied the various faces or masks which we present in different contexts. A brief history of privacy and surveillance follows. Finally, the paper ends with an affirmation of the importance of private space as an essential component of our spiritual and emotional well-being in today-s wired world.
Digital Article Identifier (DAI):
5
1566
Traffic Density Estimation for Multiple Segment Freeways
Abstract:
Traffic density, an indicator of traffic conditions, is one of the most critical characteristics to Intelligent Transport Systems (ITS). This paper investigates recursive traffic density estimation using the information provided from inductive loop detectors. On the basis of the phenomenological relationship between speed and density, the existing studies incorporate a state space model and update the density estimate using vehicular speed observations via the extended Kalman filter, where an approximation is made because of the linearization of the nonlinear observation equation. In practice, this may lead to substantial estimation errors. This paper incorporates a suitable transformation to deal with the nonlinear observation equation so that the approximation is avoided when using Kalman filter to estimate the traffic density. A numerical study is conducted. It is shown that the developed method outperforms the existing methods for traffic density estimation.
Digital Article Identifier (DAI):
4
9689
Efficient Mean Shift Clustering Using Exponential Integral Kernels
Abstract:

This paper presents a highly efficient algorithm for detecting and tracking humans and objects in video surveillance sequences. Mean shift clustering is applied on backgrounddifferenced image sequences. For efficiency, all calculations are performed on integral images. Novel corresponding exponential integral kernels are introduced to allow the application of nonuniform kernels for clustering, which dramatically increases robustness without giving up the efficiency of the integral data structures. Experimental results demonstrating the power of this approach are presented.

Digital Article Identifier (DAI):
3
2654
Hot-Spot Blob Merging for Real-Time Image Segmentation
Abstract:
One of the major, difficult tasks in automated video surveillance is the segmentation of relevant objects in the scene. Current implementations often yield inconsistent results on average from frame to frame when trying to differentiate partly occluding objects. This paper presents an efficient block-based segmentation algorithm which is capable of separating partly occluding objects and detecting shadows. It has been proven to perform in real time with a maximum duration of 47.48 ms per frame (for 8x8 blocks on a 720x576 image) with a true positive rate of 89.2%. The flexible structure of the algorithm enables adaptations and improvements with little effort. Most of the parameters correspond to relative differences between quantities extracted from the image and should therefore not depend on scene and lighting conditions. Thus presenting a performance oriented segmentation algorithm which is applicable in all critical real time scenarios.
Digital Article Identifier (DAI):
2
2902
Implementation of a Motion Detection System
Abstract:

In today-s competitive environment, the security concerns have grown tremendously. In the modern world, possession is known to be 9/10-ths of the law. Hence, it is imperative for one to be able to safeguard one-s property from worldly harms such as thefts, destruction of property, people with malicious intent etc. Due to the advent of technology in the modern world, the methodologies used by thieves and robbers for stealing have been improving exponentially. Therefore, it is necessary for the surveillance techniques to also improve with the changing world. With the improvement in mass media and various forms of communication, it is now possible to monitor and control the environment to the advantage of the owners of the property. The latest technologies used in the fight against thefts and destruction are the video surveillance and monitoring. By using the technologies, it is possible to monitor and capture every inch and second of the area in interest. However, so far the technologies used are passive in nature, i.e., the monitoring systems only help in detecting the crime but do not actively participate in stopping or curbing the crime while it takes place. Therefore, we have developed a methodology to detect the motion in a video stream environment and this is an idea to ensure that the monitoring systems not only actively participate in stopping the crime, but do so while the crime is taking place. Hence, a system is used to detect any motion in a live streaming video and once motion has been detected in the live stream, the software will activate a warning system and capture the live streaming video.

Digital Article Identifier (DAI):
1
8274
Model-Based Person Tracking Through Networked Cameras
Abstract:

This paper proposes a way to track persons by making use of multiple non-overlapping cameras. Tracking persons on multiple non-overlapping cameras enables data communication among cameras through the network connection between a camera and a computer, while at the same time transferring human feature data captured by a camera to another camera that is connected via the network. To track persons with a camera and send the tracking data to another camera, the proposed system uses a hierarchical human model that comprises a head, a torso, and legs. The feature data of the person being modeled are transferred to the server, after which the server sends the feature data of the human model to the cameras connected over the network. This enables a camera that captures a person's movement entering its vision to keep tracking the recognized person with the use of the feature data transferred from the server.

Digital Article Identifier (DAI):
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