This paper presents the fusion of swarm behavior in multi robotic system specifically the quadrotors unmanned aerial vehicle (QUAV) operations. This study directed on using robot swarms because of its key feature of decentralized processing amongst its member. This characteristic leads to advantages of robot operations because an individual robot failure will not affect the group performance. The algorithm emulating the animal or insect swarm behaviors is presented in this paper and implemented into an artificial robotic agent (QUAV) in computer simulations. The simulation results concluded that for increasing number of QUAV the aggregation accuracy increases with an accuracy of 90.62%. The experiment for foraging revealed that the number of QUAV does not affect the accuracy of the swarm instead the iterations needed are greatly improved with an average of 160.53 iterations from 50 to 500 QUAV. For swarm tracking, the average accuracy is 89.23%. The accuracy of the swarm formation is 84.65%. These results clearly defined that the swarm system is accurate enough to perform the tasks and robust in any QUAV number.

Unmanned underwater vehicles (UUVs) have become an integral part in helping humans do underwater explorations more efficiently and safely since these vehicles can stay underwater much longer than any human can possibly do and they require little or almost no human interaction. These vehicles are subject to dynamic and unpredictable nature of the underwater environment resulting to complexities in their navigation. This paper proposes a fuzzy logic-based controller to allow the vehicle to navigate autonomously while avoiding obstacles. The said controller is implemented in an actual lowcost underwater vehicle equipped with magnetometer and ultrasonic sensors. The intelligence of the UUV includes a two fuzzy logic block, namely Motion Control block and Heading Correction block. The fuzzy logic controller takes in target positions in X, Y and Z axes. Also, the heading error and rate of heading error are included as inputs in order to correct the bearing or direction of the vehicle. A heuristic and integration stage is also included after these fuzzy logic blocks for vehicle's collision avoidance. The controller output parameters are the adjusted thrusters' speeds which dictate the six thrusters speed and direction. With the proper output commands from this controller, the vehicle is able to navigate in its predefined destination.

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Breast cancer is the most common cancer among women worldwide and breast self-examination (BSE) is considered as the most cost-effective approach for early breast cancer detection. The general objective of this paper is to design and develop a computer vision algorithm to evaluate the BSE performance in real-time. The first stage of the algorithm presents a method for detecting and tracking the nipples in frames while a woman performs BSE; the second stage presents a method for localizing the breast region and blocks of pixels related to palpation of the breast, and the third stage focuses on detecting the palpated blocks in the breast region. The palpated blocks are highlighted at the time of BSE performance. In a correct BSE performance, all blocks must be palpated, checked, and highlighted, respectively. If any abnormality, such as masses, is detected, then this must be reported to a doctor to confirm the presence of this abnormality and proceed to perform other confirmatory tests. The experimental results have shown that the BSE evaluation algorithm presented in this paper provides robust performance.