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Pathogenic bacterial growth detection and monitoring is an important scientific process in the field of quality control in the food, water, and medical industries. Very-large-scale process of such bacteria growth monitoring is possible only with an automated process. The mechanism must make sure that the sample is continuously monitored, and detected, data is communicated to supervisors and managers, and data is stored historically retrievable for quality control and analysis. A manual bacteria inspection among the Petri dishes incubated of such bacterial growth in food processing was attempted for automation. The manual inspection in a microbiological industry involves; an operator inspecting the input petri discs to check if there are bacteria, writing down the barcode of the corresponding petri dish, and then sorting the Petri discs depending on the bacterial growth. In this automation attempt of automatizing this petri-disc inspection, the project was split into two phases. 1. Building a vision system to detect bacteria, developing of an algorithm to quantify the growth, and registering the barcode in a registry. 2. The second phase is to design a robot system with programming and define the layout of the station. The development of an intelligent robotized machine vision automated system proves the concept of a major industrial practice that has the potential to significantly increase the quality and productivity of bacterial growth, with increased throughput.

This study is an overview of autonomous cars and also the challenges faced in the field of autonomous cars. Recent advances are being made in the field of planning, perception, and decision-making for autonomous vehicles. This has led to great improvements in functional capabilities; several prototypes are already on roads. The challenge is to obtain the safe execution of vehicles in all driving situations. In this study, we will see recent methodologies for vehicle control using kinematic and dynamic model, collaborative autonomy, decision-making system for autonomous vehicles using convolutional neural network, path planning for autonomous vehicles using model predictive control, real-time decision-making for autonomous city vehicle using world model and driving maneuver system, and intention-aware autonomous driving decision-making using hidden Markov model and partially observable Markov decision process.

The electricity demand is increasing day by day. On the other side, fossil fuels are depleting at a higher rate. The abundantly available renewable energy sources like solar, wind, and fuel cells are becoming popular sources of energy. But due to the intermittent availability of these renewable energy sources, two or more energy sources are integrated together. Power electronic converters are used for integrating different energy sources. Various power electronic converter (PEC) topologies have been proposed and analyzed in detail to control maximum power point, voltage, frequency, and harmonic distortions. Each of these configurations will differ in its topology and operating principle. The objective of this paper is to present a comprehensive review of various aspects of PEC configurations available for integration of renewable energy sources by systematization into three groups (AC shunt coupled, DC shunt coupled, and hybrid coupled systems). Emphasis is also given to coordination power control, maximum power point, and grid integration challenges related to the hybrid energy system. Furthermore, a general, system modeling of solar, wind, and grid integration is presented here to give an overall picture of a hybrid renewable energy system. ©2020 Taylor & Francis Group, LLC.