In recent years, the Pterois Volitans, also known as the red lionfish, has become a serious threat by rapidly invading US coastal waters. Being a fierce predator, having no natural predator, being adaptive to different habitats, and being with high reproduction rates, the red lionfish has enervated current endeavors to control their population . This paper focuses on the first steps to reinforce these efforts by employing autonomous vehicles. To that end, an assistive underwater robotic scheme is designed to aid spear - hunting divers to locate and more efficiently hunt the lionfish. A small - sized, open source ROV with an integrated camera is programmed using Deep Learning methods to detect red lionfish in real time. Dives are restricted to a certain depth range, time, and air supply. The ROV program is designed to allow the divers to locate the red lionfish before each dive, so that they can plan their hunt to maximize their catch. Lightweight, portability, user - friendly interface, energy efficiency, and low cost of maintenance are some advantages of the proposed scheme. The developed system’s performance is examined in areas currently invaded by the red lionfish in the Gulf of Mexico. The ROV has shown success in detecting the red lionfish with high confidence in real time.

Consider a dynamic task allocation problem, where tasks are unknowingly distributed over an environment. This paper considers each task comprising two sequential subtasks: detection and completion, where each subtask can only be carried out by a certain type of agent. We address this problem using a novel nature-inspired approach called “hunter and gatherer.” The proposed method employs two complementary teams of agents: one agile in detecting (hunters) and another skillful in completing (gatherers) the tasks. To minimize the collective cost of task accomplishments in a distributed manner, a game-theoretic solution is introduced to couple agents from complementary teams. We utilize market-based negotiation models to develop incentive-based decision-making algorithms relying on innovative notions of “certainty and uncertainty profit margins.” The simulation results demonstrate that employing two complementary teams of hunters and gatherers can effectually improve the number of tasks completed by agents compared to conventional methods, while the collective cost of accomplishments is minimized. In addition, the stability and efficacy of the proposed solutions are studied using Nash equilibrium analysis and statistical analysis, respectively. It is also numerically shown that the proposed solutions function fairly; that is, for each type of agent, the overall workload is distributed equally.

Fundamentals of Engineering Programming with C and Fortran, first published in 1998, is a beginner's guide to problem solving with computers which shows how to quickly prototype a program for a particular engineering application. The book's side by side coverage of C and Fortran, the predominant computer languages in engineering, is unique. It emphasizes the importance of developing programming skills in C while carefully presenting the importance of maintaining a good reading knowledge of Fortran. Beginning with a brief description of computer architecture, the book then covers the fundamentals of computer programming for problem solving. It devotes separate chapters to data types and operators, control flow, type conversion, arrays, and file operations. The final chapter contains case studies that illustrate particular elements of modeling and visualization. Also included are a number of appendices covering C and Fortran language summaries and other useful topics. This concise and accessible book can be used as a text for introductory-level undergraduate courses on engineering programming or as a self-study guide for practising engineers.

Background clutter characterization in IR imagery has become an actively researched field and several clutter models have been reported. These models attempt to evaluate the target detection/recognition probabilities that are characteristic to a certain scene when specific target and human visual perception features are known. The prior knowledge assumed and required by these models is a severe limitation. Furthermore, the attempt to model subjective and intricate mechanisms such as human perception with simple mathematical formulae is controversial. In this paper, we introduce the idea of adaptive models that are dynamically derived from a set of examples by a supervised evolutionary learning scheme. A set of characteristic scene and target features with a demonstrated influence on the human visual perception mechanism is first extracted from the original images. Then, the correlation between these features and the results obtained by visual observer tests on the same set of images are captured into a model by the learning scheme. The effectiveness of the adaptive modeling principle is discussed in the final part of the paper.

DNA sequencing is a critical functionality in biomedical research, and technical advances that improve it have important implications for human health. Novel methods by which sequencing can be accomplished in more accurate, high-throughput, and faster ways are in development. Here, we review VLSI biosensors for nucleotide detection and DNA sequencing. Implementation strategies are discussed and split into function-specific architectures that are presented for reported design examples from the literature. Lastly, we briefly introduce a new approach to sequencing using Gate All-Around (GAA) nanowire Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) that has significant implications for the field.

Global thresholding is widely used in image processing to generate binary images, which are used by various pattern recognition systems. Typically, many features that are present in the original gray-level image are lost in the resulting binary image. This paper presents an adaptive thresholding algorithm, that maximizes the edge features within the gray-level image. The Gaussian pyramid algorithm is used to find the local gray-level variations that are present in the original gray-level image. The resulting Gaussian pyramid image is then subtracted from the original gray-level image removing the local variations in illumination. This new image is then adaptively thresholded using the adaptive contour entropy algorithm. The resulting binary images have been shown to contain more edge features than the binary images generated using global thresholding techniques.