Alignment control using visual servoing and mobilenet single-shot multi-box detection (SSD): a review

The concept is highly critical for robotic technologies that rely on visual feedback. In this context, robot systems tend to be unresponsive due to reliance on pre-programmed trajectory and path, meaning the occurrence of a change in the environment or the absence of an object. This review paper aims to provide comprehensive studies on the recent application of visual servoing and DNN. PBVS and Mobilenet-SSD were chosen algorithms for alignment control of the film handler mechanism of the portable x-ray system. It also discussed the theoretical framework features extraction and description, visual servoing, and Mobilenet-SSD. Likewise, the latest applications of visual servoing and DNN was summarized, including the comparison of Mobilenet-SSD with other sophisticated models. As a result of a previous study presented, visual servoing and MobileNet-SSD provide reliable tools and models for manipulating robotics systems, including where occlusion is present. Furthermore, effective alignment control relies significantly on visual servoing and deep neural reliability, shaped by different parameters such as the type of visual servoing, feature extraction and description, and DNNs used to construct a robust state estimator. Therefore, visual servoing and MobileNet-SSD are parameterized concepts that require enhanced optimization to achieve a specific purpose with distinct tools

A New Algorithm for Shared Simultaneous Learning of Alzheimer's Disease Progression

Alzheimer’s Disease (AD), a progressive neurodegenerative disease, is the most common form of dementia in older adults. It is preceded by stages of subtle cognitive decline called as Mild Cognitive Impairment (MCI), which is further stratified into Early (EMCI) and Late (LMCI) stages. Several imaging biomarkers are being investigated for early and accurate diagnosis as well as prognosis, and traditional approaches have generally focused on training multiple independent binary classifiers for distinguishing between Normal Controls (NC), EMCI, LMCI and AD subjects. However, these multiple one vs one classifiers could hold complementary information and sharing this information during the training may improve predictive performance. We introduce a new framework to perform Shared Simultaneous Learning (SSL) of sparse logistic regression classifiers for NC vs EMCI, EMCI vs LMCI, and LMCI vs AD classification. We achieve this by adding a new term to the logistic loss function to enforce the weight vectors to be similar to each other. We introduce a constraint to minimize the squared Euclidean distance between the three weight vectors. A smooth approximation for the absolute value function is used and the model is optimized using gradient descent with line search. For each classifier, at the current gradient descent step, weights from the other two classifiers are shared. We evaluated this algorithm on Structural Brain Connectome Networks generated from diffusion MRI of 202 subjects from the multicenter Alzheimer’s Disease Neuroimaging Initiative 2 (ADNI2) dataset. The normalized adjacency matrices were vectorized and passed as input for training along with the corresponding class labels. SSL outperformed independently trained multiple linear binary classifiers and achieved an average AUC of 0.53 for NC vs EMCI, 0.68 for EMCI vs LMCI, and 0.73 for LMCI vs AD classification. We also analyzed the brain connectivity patterns associated with highest odds ratio and show that abnormal inter-hemispheric connectivity patterns are indicative of EMCI vs LMCI whereas the right hemisphere of the brain is involved in the later stages