Model for simulating scorpion substrate vibration and detection system

Scorpion stings are vital health issues which requires prompt attention to minimize the pain inflicted on victims and avert death. A possible solution in averting the sting is the capability of detecting its presence earlier before it stings. Scorpion like other arthropods have a specific kind of movement pattern called substrate vibration, which generates a specific signal that is used in recognizing and locating mates and preys. This paper aims at developing an intelligent scorpion detection system using vibration frequency detection technique. A six step model for simulating scorpion substrate vibration and detection has been proposed. The surrounding vibrating signal is acquired and passed through a band pass filter. The resulting signal is model using autoregressive modeling technique. Resulting co-efficients are further analyzed for activity detection. The frequency response of scorpion activities for mating behaviou

Development of an intelligent scorpion detection technique using vibration analysis

A possible solution to address the problem of Scorpion stings is the capability of detecting its presence earlier before it stings. This paper presents efforts in Scorpion detection using substrate vibration modelling approach. An eight stage approach has been presented in this work. Using sinusoidal signal, signal representing Scorpion behaviour was firstly sampled and then amplified before transmitting to a nearby receiving module. The received signal undergoes filtering for noise removal before being modelled for coefficients determination. The computed coefficients were then clustered for analysis of behavioural determination. Results obtained in this work show that the proposed technique can be used for Scorpion detection

Scorpion image segmentation system

Death as a result of scorpion sting has been a major public health problem in developing countries. Despite the high rate of death as a result of scorpion sting, little report exists in literature of intelligent device and system for automatic detection of scorpion. This paper proposed a digital image processing approach based on the floresencing characteristics of Scorpion under Ultra-violet (UV) light for automatic detection and identification of scorpion. The acquired UV-based images undergo pre-processing to equalize uneven illumination and colour space channel separation. The extracted channels are then segmented into two nonoverlapping classes. It has been observed that simple thresholding of the green channel of the acquired RGB UV-based image is sufficient for segmenting Scorpion from other background components in the acquired image. Two approaches to image segmentation have also been proposed in this work, namely, the simple average segmentation technique and K-means image segmentation. The proposed algorithm has been tested on over 40 UV scorpion images obtained from different part of the world and results obtained show an average accuracy of 97.7% in correctly classifying the pixel into two non-overlapping clusters. The proposed 1 system will eliminate the problem associated with some of the existing manual approaches presently in use for scorpion detection

PEER-TO-PEER BOTNETS: A SURVEY ON PROPAGATION, DETECTION AND DETECTION EVASION TECHNIQUES

Botnets have been identified as one of the major threats to users in the internet space, nowadays. Unlike other categories of malware, botnets use Command and Control channels to launch and propagate their attacks. These botnets have been classified as centralised and decentralised (Peer-to-Peer). Due to the structure, Peerto-Peer botnets have different behavioural characteristics from centralised botnets. Past researches have equally identified that Peer-to-Peer botnets are more difficult to detect and shutdown compared to centralised botnets. This work provides a survey on the propagation, detection and detection evasion techniques of Peerto-Peer botnets. The study was able to identify various machine learning-based classifiers that have been proposed to detect Peer-to-Peer botnets in the cyber space. It is believed that any identified gap in the detection mechanisms will bring better insights into P2P botnet researches. The work concluded that identifying some of the Peer-to-Peer botnet propagation mechanisms and their detection evasion techniques will enable security researchers and experts to come up with improved botnet identification and mitigation approaches

An adaptive wavelet transformation filtering algorithm for improving road anomaly detection and characterization in vehicular technology

Accelerometers are widely used in modern vehicular technologies to automatically detect and characterize road anomalies such as potholes and bumps. However, measurements from an accelerometer are usually plagued by high noise levels, which typically increase the false alarm and misdetection rates of an anomaly detection system. To address this problem, we have developed in this paper an adaptive threshold estimation technique to filter accelerometer measurements effectively to improve road anomaly detection and characterization in vehicular technologies. Our algorithm decomposes the output signal of an accelerometer into multiple scales using wavelet transformation (WT). Then, it correlates the wavelet coefficients across adjacent scales and classifies them using a newly proposed adaptive threshold technique. Furthermore, our algorithm uses a spatial filter to smoothen further the correlated coefficients before using these coefficients to detect road anomalies. Our algorithm then characterizes the detected road anomalies using two unique features obtained from the filtered wavelet coefficients to differentiate potholes from bumps. The findings from several comparative tests suggest that our algorithm successfully detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates as compared to other known methods

Evolution of 5G Network: A Precursor towards the Realtime Implementation of VANET for Safety Applications in Nigeria

  A crucial requirement for the successful real-time design and deployment of Vehicular Adhoc Networks (VANET) is to ensure high speed data rates, low latency, information security, and a wide coverage area without sacrificing the required Quality of Service (QoS) in VANET. These requirements must be met for flawless communication on the VANET. This study examines the generational patterns in mobile wireless communication and looks into the possibilities of adopting fifth generation (5G) network technology for real-time communication of road abnormalities in VANET. The current paper addresses the second phase of a project that is now underway to develop real-time road anomaly detection, characterization, and communication systems for VANET. The major goal is to reduce the amount of traffic accidents on Nigerian roadways. It will also serve as a platform for the real-time deployment and testing of various road anomaly detection algorithms, as well as schemes for communicating such detected anomalies in the VANET.   &nbsp

Utility of crime surveys for Sustainable Development Goals monitoring and violence prevention using a public health approach

The Sustainable Development Goals (SDGs) have highlighted interpersonal violence and violence against women and girls as impediments to development globally. South Africa is adversely affected by violence and injury. The annual Victims of Crime Survey (VoCS) provides a potentially useful source of complementary data to bolster vital registration and police crime statistics, but it may not provide data that are sufficiently accurate and reliable to inform prevention efforts. We conducted a critical assessment of the VoCS’s methodological robustness and strength as a data source for high-level analyses, adopting a public health and SDGs monitoring perspective that was based on expert opinion and comparison with other data sources. We concluded that either the survey methods should be improved to provide findings that are better aligned with the SDGs agenda and are robust enough to inform high-quality research and prevention, or the funds used to conduct the VoCS should be redirected to other more suitable instruments

Tearing Out the Income Tax by the (Grass)Roots

Landscapes are increasingly fragmented, and conservation programs have started to look at network approaches for maintaining populations at a larger scale. We present an agent-based model of predator–prey dynamics where the agents (i.e. the individuals of either the predator or prey population) are able to move between different patches in a landscaped network. We then analyze population level and coexistence probability given node-centrality measures that characterize specific patches. We show that both predator and prey species benefit from living in globally well-connected patches (i.e. with high closeness centrality). However, the maximum number of prey species is reached, on average, at lower closeness centrality levels than for predator species. Hence, prey species benefit from constraints imposed on species movement in fragmented landscapes since they can reproduce with a lesser risk of predation, and their need for using anti-predatory strategies decreases.authorCount :