Recognizing human activity plays a significant role in the advancements of human-interaction applications in healthcare, personal fitness, and smart devices. Many papers presented various techniques for human activity representation that resulted in distinguishable progress. In this study, we conduct an extensive literature review on recent, topperforming techniques in human activity recognition based on wearable sensors. Due to the lack of standardized evaluation and to assess and ensure a fair comparison between the state-of-the-art techniques, we applied a standardized evaluation benchmark on the state-of-the-art techniques using six publicly available data-sets: MHealth, USCHAD, UTD-MHAD, WISDM, WHARF, and OPPORTUNITY. Also, we propose an experimental, improved approach that is a hybrid of enhanced handcrafted features and a neural network architecture which outperformed top-performing techniques with the same standardized evaluation benchmark applied concerning MHealth, USCHAD, UTD-MHAD data-sets.

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Multi-object tracking is a vital component in various robotics and computer vision applications. However, existing multi-object tracking techniques trade off computation runtime for tracking accuracy leading to challenges in deploying such pipelines in real-time applications. This paper introduces a novel real-time model, LMOT, i.e., Light-weight Multi-Object Tracker, that performs joint pedestrian detection and tracking. LMOT introduces a simplified DLA-34 encoder network to extract detection features for the current image that are computationally efficient. Furthermore, we generate efficient tracking features using a linear transformer for the prior image frame and its corresponding detection heatmap. After that, LMOT fuses both detection and tracking feature maps in a multi-layer scheme and performs a two-stage online data association relying on the Kalman filter to generate tracklets. We evaluated our model on the challenging real-world MOT16/17/20 datasets, showing LMOT significantly outperforms the state-of-theart trackers concerning runtime while maintaining high robustness. LMOT is approximately ten times faster than state-of-the-art trackers while being only 3.8% behind in performance accuracy on average leading to a much computationally lighter model. INDEX TERMSMulti-object tracking, pedestrian tracking, joint detection and tracking, object detection, deep learning.

Recognizing human activity plays a significant role in the advancements of human-interaction applications in healthcare, personal fitness, and smart devices. Many papers presented various techniques for human activity representation that resulted in distinguishable progress. In this study, we conduct an extensive literature review on recent, top-performing techniques in human activity recognition based on wearable sensors. Due to the lack of standardized evaluation and to assess and ensure a fair comparison between the state-of-the-art techniques, we applied a standardized evaluation benchmark on the state-of-the-art techniques using six publicly available data-sets: MHealth, USCHAD, UTD-MHAD, WISDM, WHARF, and OPPORTUNITY. Also, we propose an experimental, improved approach that is a hybrid of enhanced handcrafted features and a neural network architecture which outperformed top-performing techniques with the same standardized evaluation benchmark applied concerning MHealth, USCHAD, UTD-MHAD data-sets.

The rheological properties of Fig jam puree were studied at the range 40-65 % solid concentration of Fig jam puree within temperature range 20-90°C and spindle speed of 10-50 rpm. Dependence of apparent viscosity on temperature was related through the Arrhenius law. The plot of lnµ versus 1/T exhibits three regions with different temperature dependencies at concentration 65%, a reasonable explanation was presented for this phenomenon. Also, this study includes the dependence of the apparent viscosity of Fig jam puree on solid concentration that follows the power law relationship. An interpretation of the relation between the constants of the power law and shear rate was deducted. KEYWORDSActivation energy, effect of temperature , Rheology of jams, Effect of concentrations.