Cotton is the natural fiber produced, and the commercial crop grown in monoculture on 2.5% of total agricultural land. Cotton is a drought-resistant crop that provides a reliable income to the farmers that grow under the area with a threat from climatic change. These cotton crops are being affected by bacterial, fungal, viral, and other parasitic diseases that may vary due to the climatic conditions resulting in the crop’s low productivity. The most prone to diseases is the leaf that results in the damage of the plant and sometimes the whole crop. Most of the diseases occur only on leaf parts of the cotton plant. The primary purpose of disease detection has always been to identify the diseases affecting the plant in the early stages using traditional techniques for better production. To detect these cotton leaf diseases appropriately, the prior knowledge and utilization of several image processing methods and machine learning techniques are helpful.

The advancement in deep learning techniques has helped researchers acquire and process multimodal data signals from different healthcare domains. Now, the focus has shifted towards providing end-to-end solutions, i.e., processing these data and developing models that can be directly implemented on edge devices. To achieve this, the researchers try to solve two problems: (I) reduce the complex feature dependencies and (II) reduce the complexity of the deep learning model without compromising accuracy. In this paper, we focus on the later part of reducing the complexity of the model by using the knowledge distillation framework. We have introduced knowledge distillation on the Vision Transformer model to study the MIT-BIH Arrhythmia Database. A tenfold crossvalidation technique was used to validate the model, and we obtained a 99.7% F1 score and 99.3% accuracy. The model was further tested on the Xilinx Alveo U50 FPGA accelerator, and it is found fit for any low-powered wearable device implementation.

Acquisition of digital image and preprocessing methods plays a vital role in clinical diagnosis. The ultrasound medical images are more popular than other imaging modalities, due to portable, adequate, harmless and cheaper nature of it. Because of intrinsic nature of speckle noise (signal based noise), ultrasound medical image leads to degradation of the resolution and contrast of the image. Reduction of this signal based noise is helpful for the purpose of visualization of the ultrasound images. The low quality of image is considered as a barrier for the better extraction of features, recognition, analysis and detection of edges. Because of which inappropriate diagnosis may be done by doctor. Thus, speckle noise reduction is essential and preprocessing step of ultrasound images. Analysts survey manifold reduction methods of speckle noise, yet there is no exact method that takes all the limitations into account. In this review paper, we compare filters that are Lee, Frost, Median, SRAD, PMAD, SRBF, Bilateral, Adaptive Bilateral and Multiresolution on medical ultrasound images. The results are compared with parameter PSNR along with the visual inspection. The conclusion is illustrated by filtered images and data tables.

This paper presents, a new approach to track the specified path of mobile robot by using PID controller. Cuckoo optimization method is used to design the PID controller which update the linear & angular velocities to track the path. The objective function is evaluated by calculating individual errors in three (x, y and theta) directions and effective error is calculated by taking under-root of summation of the squared errors. The proposed method is validated by analysing the simulations for different paths.

The cellular automaton paradigm is very\ud appealing and its inherent simplicity belies its potential\ud complexity. Two dimensional cellular automata are\ud significantly applying to image processing operations.\ud This paper describes the application of cellular automata\ud (CA) to various morphological operations such as\ud thinning and thickening of binary images. The description\ud about the selection of the optimum rule set of two\ud dimensions cellular automata for thinning and thickening\ud of binary images is illustrated by this paper. The selection\ud of the optimum rule set from large search space has been\ud performed on the basis of sequential floating forward\ud search method. The misclassification error between the\ud images obtained by the standard function and the one\ud obtained by cellular automata rule is used as the fitness\ud function. The proposed method is also compared with\ud some standard methods and found suitable for the\ud purpose of morphological operations