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Despite sign language being the primary medium of conversation with deaf and dumb (DD) individuals, it is hard to grasp for any class of people, making communication immensely challenging. Bengali being one of the most spoken languages globally, significant research works on Bangla Sign Language (BdSL) have come to light to deal with this challenge. In recent years, researchers from diverse backgrounds have proposed solutions to automate the process of BdSL recognition. In this literature review, we have explored the trends of research on BdSL by comparing incorporated features and evaluation results of systems between 2002-2021 and techniques applied on different existing and new datasets. We have also collected and combined metadata from datasets of all BdSL Alphabets and numerics used so far. The literature findings of this paper indicate that most proposed models perform well on images containing static and single-handed signs but, performance falls significantly in complex backgrounds. Moreover, we focused on identifying insights and similarities of the existing system, finding research gaps and propose possible future directions. We expect this article will draw the attention of more researchers into this domain and this is the first identifiable academic literature review of BdSL recognition systems to the best of our knowledge.

The use of Mobile Robots in diverse sectors are increasing day by day. Nowadays, mobile robots are widely used in industrial sectors, but real-time path planning and collision free path tracking raises more challenging issues. A good path planning process for these mobile robots is needed in order for them to perform better. In path planning problems, the $A^{\ast}$ algorithm has been widely investigated and applied, but the cost and efficiency of the path is not completely taken into consideration. This algorithm has been tweaked and improved in a number of ways to facilitate path planning. Taking into account the variable-step-length $A^{\ast}$ search algorithm, which can take steps longer than one. This method uses a fixed step length and produces a better path than the $A^{\ast}$ Search algorithm. However, if the step length can be modified in the process as required, a more optimal direction can be obtained. As a result, this paper proposes an improved updated variable-step-length based $A^{\ast}$ algorithm that can update or change step length. This algorithm will efficiently find a path with a shorter length. In order to check the feasibility and efficiency of the proposed system, simulation and practical experiments are carried out. The findings show that the improved $A^{\ast}$ algorithm will increase the planned route’s safety and efficiency while also reducing the robot’s movement time in difficult terrain.

Paralyzed people face a lot of challenges and difficulties in their regular activities. Even a simple task such as controlling electrical devices also very difficult for them. This study's primary purpose is to develop a cost-effective home automation system based on the human-centered design (HCD) that could reduce some people's difficulties. The proposed approach takes voice or gesture as input and performs necessary actions according to the information. The proposed system helps all paralyzed people except quadriplegia operate the room appliances without regular attention from a caretaker or nurse.

Wind power capacity around the world is increasing day by day, but the production of wind energy greatly depends on the wind speed, where the wind speed has stochastic nature over time. In this paper, an artificial neural network (ANN) technique to forecast wind speed for the next hour in Newfoundland, Canada is proposed. As, deep learning models are combined with different hyperparameters, in our study, the selection of important hyperparameters are conducted by applying the Bayesian optimization algorithm. The wind speed forecasting performance of the proposed model is compared with other recognized models like support vector machine (SVM), random forest (R.F.) and decision tree (D.T.), where it is observed that our proposed model performs better than the other models in terms of mean absolute error (M.A.E.) and root mean squared error (R.M.S.E.). The proposed Bayesian optimized artificial neural network is fed with five input features and delivers M.A.E. and R.M.S.E. of 1.09 and 1.45.

Irrigation is the process of applying appropriate amount of water to crop fields at needed interim. Irrigation is an exigent part of cultivation of rice. Although, the overall yield of rice paddy predominantly depends on proper irrigation, irrigation process in developing countries like Bangladesh is still backdated. This study proposes an Arduino/GSM based remotely controlled power efficient smart irrigation system for crops that need to be immersed in water during its growing period. It will ensure the proper irrigation of a field by monitoring water level of the paddy field, providing feedback to farmers and giving farmers option to control the water motor via SMS. This study is expected to improve the overall production of AMAN and BORO rice of Barishal region by automating the traditional irrigation system. Also it will provide a more sophisticated irrigation system for similar types of crops.