In the proposed work, initially, the YOLO algorithm is used to extract and classify objects in a frame. In the sequence of frames, due to various reasons the confidence measure suddenly drops. This changes the class of an object in consecutive frames which affects the object tracking and counting process severely. To overcome this limitation of the YOLO algorithm, it is modified to enable and track the same object efficiently in the sequence of frames. This will in turn increase object tracking and counting accuracy. In the proposed work drastic change in confidence scores and class change of an object in consecutive frames are identified by tracking the confidence of a particular object in the sequence of frames. These outliers are detected and removed using the RANSAC algorithm. After the removal of the outliers, interpolation is applied to get the new confidence score at that point. By applying the proposed method a smooth confidence measure variation is obtained across the frames. Using this, average counting accuracy has been increased from 66 % to 87 % and overall average object classification accuracy is in the range of 94 - 96 % for various standard dataset.

An efficient product packing system through a simplified automatic counting and collecting process is a critical aspect of the manufacturing industry. In the present paper, we propose a real-time tool for simultaneous counting and collection of the objects into different bins using machine vision. The system employs a minimum distance classifier for object detection, Counting on the conveyor belt is done by tracking the Euclidian distances between the centroids of the objects in successive frames. Furthermore, the required number of object collection is carried out in two steps. In the first step, multiple objects nearest to the required count are collected. In the second step, the difference of objects that remained from the previous step are added one by one. Object collection is carried out by integrating software output to the hardware, which collects them in required numbers in the different bins. To meet the fast counting requirements, a high-speed conveyor system constituting vision sensor with pixel resolution of 1920x1200 and 41 frames per second frame rate is employed. The high computational power requirements for implementing the algorithm is accomplished by employing a dedicated graphics microcontroller, whose GPIO pins are used for interfacing the hardware for the object collection. The testing results of the proposed device with 30 variants of different plumbing industry objects confirm an accuracy of 100% for a period of 3 hours. Moreover, the proposed module is scalable and could boost the productivity of the company in a complex environment without affecting the accuracy of the system.

Pathological conditions in the knee joint can be caused by vibrations emitted in knee joint while bending or extending the leg. To detect this vibrations or to know the inner condition of the joint like softening, coarseness, breakage or it's state of lubrication of articular cartilage surface, vibroarthrographic (VAG) signal is useful. The VAG signal is used because of fluctuating and nonlinear property which is helpful to extract the condition of knee joint. In present work on VAG signals by using the TQWT decomposition. VAG signals are partitioned into sub-band signals of distinct frequencies. There are different features of VAG Signals, fluctuating in nature like Kraskov Entropy (KE) and Signal Fractal Dimension (SFD). The performance of feature selection (FS) techniques can be validated through using the Least square support Vector machine (LS-SVM). By using LS-SVM classifier we have achieved an accuracy of 86.91%, sensitivity of 88.33%, specificity of 86.66% based on the input of 89 VAG signals.