Design of an automatic escaped animal detection and monitoring system: a case study of Volcanoes National Park (VNP)

A Project Report Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Embedded and Mobile Systems of the Nelson Mandela African Institution of Science and TechnologyThe results have been shown that the people especially farmers living at the edge of Volcanoes National Park (VNP) practiced agricultural business due to the fertile soil found in the region. The rising number of agronomies in the zone, number of tourists, and illegal forest users such as poaching, and deforestation cause wild animals to get out of their habitats. Therefore, their crops are raided by forest animals which present a likely risk to damage crops whenever they get out of the forest. The current systems such as “Buffer Wall also known as wall of stones” was manually operated; electric fence systems resulted in death and pain to wild animals. Due to the development of automatic systems for detecting and monitoring all moving wild animals and intruders, it was stated that using automation at Buffer wall could be helpful for both wild animals and farmers keep safe. Security is an importance in the VNP whereby detection and monitoring wildlife would determine the needs by park officials. The objectives of developing an Automatic Escaped Animal Detection and Monitoring System were to reduce the probability of crop raids, death and injuries between wild animals and farmers, warning the wild animals through the use of buzzer, speaker with a recorder voice of lion and block of LEDs to remain in their habitats and the notifications sent to the park officials related to the forest animals getting out of the forest. Since wild animals and intruders found in buffer zone targeting to pass by the buffer wall for crop raiding and poaching activities; this system should primarily use sensing devices to detect and monitor their presence. On the other hand, for buffer wall security, warning equipment’s such as block of LEDs, Buzzer, SIREN Alarm and speaker should all together be activated. Whenever wild animals and trespassers would search to pass by another part would be activated the same way as the previous. The specialty of this technological system developed was to automate manual and improve the current systems by using Arduino NANO Microcontroller to execute system’s operations, GPS NEO 6M for locating moving wild animal, Ultrasonic sensor for detecting wildlife and calculating its speed, PIR sensor to detect intruders, GSM SIM900 to notify park rangers, reduction of crop raiding, and finally reducing death and pain of wild animals caused by current systems

Perspectives in machine learning for wildlife conservation

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation

GEOFENCING AS APPLIED WITHIN THE FIELD OF CYBERSECURITY: AN OVERVIEW OF POTENTIAL RISKS AND ADVANTAGES

This culminating experience project explores geofencing as a potential risk and advantageous tool within the field of cybersecurity. Geofencing is defined here as a software program feature that allows its users to collect and deliver data within a specific targeted geographical area. Currently used applications are addressed from a cybersecurity mindset by applying the hacker methodology to demonstrate the potential threat. Additionally, geofencing is applied to the NIST Cybersecurity Framework to demonstrate potential benefits for cyber defence. Finally, vulnerabilities associated with applying geofencing to cyber defense, and its potential implications on privacy and cybersecurity laws is discussed and recommendations for further research are suggested. Key findings include: Demonstrating geofencing as an unknown threat in the field of cybersecurity, suggesting attention be dedicated to the type of data that is collected and the safety measures protecting that data. Geofencing can be used as a tool to defend as well as support risk management. By using it as a source of data collection, decisions can be implemented to better manage the risk of devices entering and leaving a specified geographical area. Geofencing can provide data that falls into Personally Identifiable Information (PII) which should make it regulated under most privacy laws. Current privacy policies and laws are insufficient when the scope of geofencing is applied to current methodologies. Geofencing must be regulated in a fashion that ensures data collected is necessary and relevant, and that the data is kept safe from potential threats

Secure Face and Liveness Detection with Criminal Identification for Security Systems

The advancement of computer vision, machine learning, and image processing techniques has opened new avenues for enhancing security systems. In this research work focuses on developing a robust and secure framework for face and liveness detection with criminal identification, specifically designed for security systems. Machine learning algorithms and image processing techniques are employed for accurate face detection and liveness verification. Advanced facial recognition methods are utilized for criminal identification. The framework incorporates ML technology to ensure data integrity and identification techniques for security system. Experimental evaluations demonstrate the system's effectiveness in detecting faces, verifying liveness, and identifying potential criminals. The proposed framework has the potential to enhance security systems, providing reliable and secure face and liveness detection for improved safety and security. The accuracy of the algorithm is 94.30 percent. The accuracy of the model is satisfactory even after the results are acquired by combining our rules inwritten by humans with conventional machine learning classification algorithms. Still, there is scope for improving and accurately classifying the attack precisely

Fish and Ships: Impacts of Boat Noise on the Singing Fish, Porichthys notatus

As anthropogenic ocean noise rises, research into its impacts on marine life is intensifying. Recent studies show concerning effects of noise on a variety of taxa, including fish. However currently lacking are in situ studies: the majority of fish studies have been lab-based, which lack the natural conditions and interconnections that put results in context. Further, the dearth of baseline information on natural fish sounds, communication and behaviours, limits predictions of noise impacts. Here I investigated the highly vocal plainfin midshipman (Porichthys notatus) in its natural habitat to determine the effects of boat noise on wild fish. Porichthys notatus uses sound to communicate during courtship and aggression, and depends on paternal care to safeguard nests in intertidal zones over several months. I first described acoustic communication features of P. notatus in situ by quantifying its vocalizations from longterm audio recordings gathered via hydrophones near a nesting site. I then characterized behaviours associated with acoustic signals by analyzing audio and video data of nest-guarding P. notatus. Finally, I determined the response of P. notatus to live motor-boat noise by examining behavioural and vocal activity of P. notatus in boat noise, ambient and control conditions. In addition to the manual analysis, I used an automated approach to determine overall movement of P. notatus under boat noise, ambient and control conditions. Findings reveal that when exposed to boat noise, fewer P. notatus predators were documented in the vicinity of P. notatus nests, while P. notatus increased overall time spent moving inside nests. Thus, noise benefits P. notatus indirectly by decreasing predator pressure, yet has direct negative impacts on P. notatus by increasing stress and metabolic costs. Such results reveal fitness consequences at both species and ecosystem scales, and indicate the importance of accounting for ecological relationships when predicting noise effects

Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences

In this survey, we first briefly review the current state of cyber attacks, highlighting significant recent changes in how and why such attacks are performed. We then investigate the mechanics of malware command and control (C2) establishment: we provide a comprehensive review of the techniques used by attackers to set up such a channel and to hide its presence from the attacked parties and the security tools they use. We then switch to the defensive side of the problem, and review approaches that have been proposed for the detection and disruption of C2 channels. We also map such techniques to widely-adopted security controls, emphasizing gaps or limitations (and success stories) in current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages. Listing abstract compressed from version appearing in repor

Southwest Research Institute assistance to NASA in biomedical areas of the technology utilization program

The activities are reported of the NASA Biomedical Applications Team at Southwest Research Institute between 25 August, 1972 and 15 November, 1973. The program background and methodology are discussed along with the technology applications, and biomedical community impacts

Divergent Mating Behaviors and the Evolution of Reproductive Isolation

Sexual selection can cause rapid co-divergence of mating traits and mate preferences, generate reproductive barriers among individuals bearing divergent mating traits, and potentially lead to speciation. In my dissertation, I focused on two emerging topics that challenge this traditional speciation-by-mate-choice paradigm. First, sexual selection encompasses both mate preferences and intrasexual competition, yet speciation research disproportionally focused on the role of the former. Second, sexual behaviors are usually assumed to be genetically inherited, but they may often be shaped by learning instead, which can generate very different evolutionary trajectories for traits and preferences. Using studies of the highly polymorphic strawberry poison frogs (Oophaga pumilio), I demonstrated how incorporating (i) male-male competition and (ii) behavioral learning can enhance our understanding of the potential for speciation to be driven by sexual selection. I first characterized behavioral patterns across a natural contact zone between color morphs and showed that coloration (the divergent mating trait) mediates both female choice and male-male competition. Females often prefer males of their own (local) color over a novel color, and males, when defending territories, are more aggressive against their own color morph. I then tested how these color-mediated female preferences and male aggression biases interact to determine mating patterns. I conducted a controlled breeding experiment in which male-male competition and female mate choice act either in same or in opposing directions. In this study, females reproduced more often with the territorial male over the non-territorial male, regardless of the males’ coloration. This challenges the common assumption that knowledge of female preferences for male mating traits is sufficient to predict mating patterns. Finally, I discovered that learning from mothers during the tadpole stage shapes both female mate preferences and male aggression biases in O. pumilio. Based on this finding, I built a population genetic model and used it to demonstrate a simple and elegant mechanism by which sexual selection alone has the potential to initiate speciation. My research highlights the importance of considering interactions between mate choice, intrasexual competition, and behavioral learning, for studies of mating trait evolution and sexual selection’s role in speciation

Detecting cyber threats through social network analysis: short survey

This article considers a short survey of basic methods of social networks analysis, which are used for detecting cyber threats. The main types of social network threats are presented. Basic methods of graph theory and data mining, that deals with social networks analysis are described. Typical security tasks of social network analysis, such as community detection in network, detection of leaders in communities, detection experts in networks, clustering text information and others are considered