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This paper presents Artificial Neural Network (ANN) as an optimization tool in tuning Proportional-Integral-Derivative (PID) controller's gain of a multi-joints Lower Limb Exoskeleton (LLE) for gait rehabilitation. The interest in wearable post-stroke and spinal cord injury rehabilitation devices such as LLE has been increasing due to the demand for assistive technologies for paralyze patients and to meet the concerns in the increasing number of ageing society. The dynamic of three degree of freedom LLE was determined using Euler-Lagrange equation, and PID parameters were initially tuned using the Ziegler-Nichols (ZN) method. The paper compares different ANN-based algorithms in tuning PID controller's gain for LLE applications. The method compared and evaluated with other methods and dynamic systems in the literature. ANN-based algorithms, Gradient Descent, Levenberg-Marquardt, and Scaled Conjugate Gradient, are utilized for PID tuning of each joint in the LLE model. The result shows faster convergence and improves step response characteristics for each controlled joint model. The overshoot values found to be 0.3126%, 0.6335%, and 0.2619% compared to the ZN method with 10.5582%, 15.1643%, and 11.8511% for hip, knee, and ankle joints, respectively. It can be ascertained that the PID controlled of LLE has been optimally tuned significantly by different ANN methods, which reduced its steady-state errors.

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Automatic Traffic Sign Detection and Recognition (ATDR) system has been expanded and implemented partly in Intelligent Transportation System (ITS) that is actively used today. As traffic congestion increases, the manufacturing industry may have made and installed the ATDR systems in various types of vehicles, including cars, light commercial vehicles, and heavy trucks that act as driver assistance systems. ATDR system not only helps to minimize the number of traffic accidents but also supports road users through legal and compliant guidance and providing all traffic information, so that road users can be more attentive and solve them immediately in a short duration of time. On the point of the safety of the road users and others will be protected throughout a critical situation with identify the driving scene. In this paper, a deep-architectural neural networks, which is the convolutional neural network (CNN/ConvNet) model are chosen to expand in the ATDR system. With its excellent ability to train, research, and organize data, CNN is becoming one of the most widely used machine learning algorithms in key tasks such as prediction and classification. First, various open source of deep learning libraries will be studied. The library has been tested using the Malaysian Traffic Sign (MTS) dataset, which contains 32891 labelled images with real-world signs. Then the combination of both designed detection model and the CNN classification model will be trained under some parameter settings. Finally, the proposed system will detect and recognize the different types of traffic sign images in real-time display and Graphical User Interface (GUI). The achievable test accuracy is exceeded by 98% on a total of 43 different classes of MTS data that is obtained and for all study cases.

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<p><span>This article introduces a novel approach for identify partial pose estimation using template matching method. The algorithms performs 2D correlation matching on tested image to CAD database by using regional shape representation in order to get the similar object pose in CAD database. The descriptor named outer box method, it is useful for rescale or aligning object size in both different images of tested image and CAD database image and also provide interest point for segmentation in image registration stage. The proposed algorithm were experimentally shown to be robust to apply on scale changes, various complex shape, unstructured CAD database and mixed CAD model database. Last part, the identified pose and its retrieved pose angle was calculated and achieved high accuracyin range ±0.388˚ to ±1.471˚.</span></p>