This research paper contains the work done on the ‘Design and development of an Autonomous quadruped robot’ – a system engineered to complete tasks like carrying weaponry and materials for military applications. The primary aim of the project was to design a quadruped that works successfully in harsh conditions and irregular terrain were human intervention is impossible or hard. Initially, individual components of the design were designed and modifications were made according to the requirements. The development was carried out in five major steps including frame assignment, DH parameter analysis, Kinematic and dynamic analysis, Multibody simulation, Stability analysis and execution of gaits. The average time taken for the device to complete on cycle was found to be 10 seconds in crawl gait and around 8 seconds in trot gait. Further tests on the final device also directed a consistent results in its functionality and time taken for the quadruped to complete one gait cycle. As such, the performance of the quadruped is found to be successful and also open to further modification for better performance and varied usage requirements since the research on autonomous quadruped is at its early stages of development.

The data's dimensionality had already risen sharply in the last several decades. The "Dimensionality Curse" (DC) is a problem for conventional learning techniques when dealing with "Big Data (BD)" with a higher level of dimensionality. A learning model's performance degrades when there is a numerous range of features present. "Dimensionality Reduction (DR)" approaches are used to solve the DC issue, and the field of "Machine Learning (ML)" research is significant in this regard. It is a prominent procedure to use "Feature Selection (FS)" to reduce dimensions. Improved learning effectiveness such as greater classification precision, cheaper processing costs, and improved model comprehensibility are all typical outcomes of this approach that selects an optimal portion of the original features based on some relevant assessment criteria. An "Adaptive Firefly Optimization (AFO)" technique based on the "Map Reduce (MR)" platform is developed in this research. During the initial phase (mapping stage) the whole large "DataSet (DS)" is first subdivided into blocks of contexts. The AFO technique is then used to choose features from its large DS. In the final phase (reduction stage), every one of the fragmentary findings is combined into a single feature vector. Then the "Multi Kernel Support Vector Machine (MKSVM)" classifier is used as classification in this research to classify the data for appropriate class from the optimal features obtained from AFO for DR purposes. We found that the suggested algorithm AFO combined with MKSVM (AFO-MKSVM) scales very well to high-dimensional DSs which outperforms the existing approach "Linear Discriminant Analysis-Support Vector Machine (LDA-SVM)" in terms of performance. The evaluation metrics such as Information-Ratio for Dimension-Reduction, Accuracy, and Recall, indicate that the AFO-MKSVM method established a better outcome than the LDA-SVM method.

Analytics of Big data research has been entering the latest processes of "fast-data", in which every second many Giga Bytes of data arriving towards a massive structure of data. Based on the number, speed, importance, variation, uncertainty and veracity of the collected data, current Big data applications gather dynamic data sources and thus create massive unstructured Big Data. Data sources that are decreased and significant are deemed more valuable than raw, repetitive, unreliable, and noisy data set. A further prospect for reducing the big data whereas the thousands of attributes in large data sets are the cause of the dimensionality which takes infinite computing resources to expose working patterns of information. Not each feature in the generated datasets is essential for the training of computer algorithms. Any characteristics do not influence the effects of the forecast and some may be negligible. The ignorance of this trivial or less important characteristics lowers the pressure on the algorithms of Machine Learning (ML). The MapReduce technology in existing has also been used to decrease dimensionality, but without decreasing irrelevant features it takes all data for a direct reduction, which contributes to lower classification precision.

COVID-19 is declared as a pandemic by WHO (world health organization) which has led to many deaths all over the world. This study deals with the fluid motion in the isolation rooms with 12 or more ACH (air changes per hour) and maintaining a minimum pressure difference of 2.5 Pascal that can help in reducing the transmission of the virus from affected people. ANSI/ASHRAE guidelines are considered for the analysis. These Isolation rooms help in eradicating the spread of the contaminated particles to the surroundings by creating a pressure less than the atmospheric pressure in in the room. CFD simulations are carried to study the fluid motion of the particles emitted by the patient inside the room. The Analysis is carried out with various human cough velocities of different particle diameters and we observed from the results that the time taken by the particles to reach the exhaust increases with increase in particle diameter, and the flow inside the room increases with increase in human cough velocity.

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