In the present era, the applications of computer vision is increasing day by day. Computer vision is related to the automatic recognition, exploration and extraction of the necessary information from a particular image or a group of image sets. This paper addresses the method to detect the desired object from an image. Usually, a template of the desired object is used in detection through a matching technique named Template Matching. But it works well when the template image is cropped from the original one, which is not always invariant due to various transformations in the test images. To cope with this difficulty and to develop a generalized approach, we investigate in detail another technique which is known as HOG (Histogram of Oriented Gradient) approach. In HOG, the image is divided into overlapping blocks of template size and then compare each block's normalized HOG with the normalized HOG of the template to find the best match of the object. We perform experiments with a large number of images and have found satisfactory performance.

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The wireless communication system is one of the most favorable fields of communication in computer science because of its mobility and dynamicity. But it is very important to use some efficient techniques in terms of bandwidth and speed to cope with the wide range of user's demands. In order to fulfill these demands, Orthogonal Frequency Division Multiplexing (OFDM) multicarrier technique holds some magnificent features namely multipath fading, delay spread, frequency selective fading, and inter channel interference (ICI). In this paper, the performance of OFDM system is analyzed to lessen the multipath fading and the inter symbol interference (ISI) over an Additive White Gaussian Noise (AWGN). We find out the variation of the output signal after removing unnecessary noise and bit error rate (BER) of the input signal using AWGN, and it shows better results for BER curve for Binary Phase Shift Keying (BPSK) in the OFDM system.

Ever since the medieval era, the preponderance of our concentration has been concentrated upon agriculture, which is typically recognized to be one of the vital aspects of the economy in contemporary society. This focus on agriculture can be traced back to the advent of the industrial revolution. Wheat is still another type of grain that, in the same way as other types of harvests, satisfies the necessity for the essential nutrients that are required for our bodies to perform their functions correctly. On the other hand, the supply of this harvest is being limited by a variety of rather frequent ailments. This is making it difficult to meet demand. The vast majority of people who work in agriculture are illiterate, which hinders them from being able to take appropriate preventative measures whenever they are necessary to do so. As a direct consequence of this factor, there has been a reduction in the total amount of wheat that has been produced. It can be quite difficult to diagnose wheat illnesses in their early stages because there are so many various forms of environmental variables and other factors. This is because there are numerous distinct sorts of agricultural products, illiteracy of agricultural workers, and other factors. In the past, a variety of distinct models have been proposed as potential solutions for identifying illnesses in wheat harvests. This study demonstrates a two-dimensional CNN model that can identify and categorize diseases that affect wheat harvests. To identify significant aspects of the photos, the software employs models that have previously undergone training. The suggested method can then identify and categorize disease-affected wheat crops as distinct from healthy wheat crops by employing the major criteria described above. The reliability of the findings was assessed to be 98.84 percent after the collection of a total of 4800 images for this study. These images included eleven image classes of images depicting diseased crops and one image class of images depicting healthy crops. To offer the suggested model the capability to identify and classify diseases from a variety of angles, the photographs that help compensate for the collection were flipped at a variety of different perspectives. These findings provide evidence that CNN can be applied to increase the precision with which diseases in wheat crops are identified.

Cancer is now one of the leading causes of death globally and in Bangladesh. This research is focused on the blood cancer patients because there are only a few studies in Bangladesh on blood cancer. Initially, we collected 340 data (blood cancer and non-blood cancer) from BSMMU hospital. Then we use data mining to rank 30 factors associated with blood cancer. Our data mining methods include classification, chi-square (χ2) test, P-value, and association rule mining. The most potent predictors (P-value < .001) were muscle pull, inability to control the bladder, unusual bleeding, fever/raised temperature, and weakness in the legs. To predict an association between these significant elements, popular rule mining algorithms such as Apriori or Tertius are used. The results of the experiment show that weakness in the legs, fever and inability to control the bladder are common rules of blood cancer. Again, unusual bleeding, rapid illness, and muscle pull are likely to be associated. A fair skin tone with rapid breathing and leg weakness may also be a danger. Journal of Engineering Science 13(1), 2022, 9-20