A pipeline framework for robot maze navigation using computer vision, path planning and communication protocols.

Maze navigation is a recurring challenge in robotics competitions, where the aim is to design a strategy for one or several entities to traverse the optimal path in a fast and efficient way. To do so, numerous alternatives exist, relying on different sensing systems. Recently, camera-based approaches are becoming increasingly popular to address this scenario due to their reliability and given the possibility of migrating the resulting technologies to other application areas, mostly related to human-robot interaction. The aim of this paper is to present a pipeline methodology towards enabling a robot solving maze autonomously, by means of computer vision and path planning. Afterwards, the robot is capable of communicating the learned experience to a second robot, which then will solve the same challenge considering its own mechanical characteristics which may differ from the first robot. The pipeline is divided into four steps: (1) camera calibration (2) maze mapping (3) path planning and (4) communication. Experimental validation shows the efficiency of each step towards building this pipeline

GluNet: A Deep Learning Framework For Accurate Glucose Forecasting

For people with Type 1 diabetes (T1D), forecasting of \red{blood glucose (BG)} can be used to effectively avoid hyperglycemia, hypoglycemia and associated complications. The latest continuous glucose monitoring (CGM) technology allows people to observe glucose in real-time. However, an accurate glucose forecast remains a challenge. In this work, we introduce GluNet, a framework that leverages on a personalized deep neural network to predict the probabilistic distribution of short-term (30-60 minutes) future CGM measurements for subjects with T1D based on their historical data including glucose measurements, meal information, insulin doses, and other factors. It adopts the latest deep learning techniques consisting of four components: data pre-processing, label transform/recover, multi-layers of dilated convolution neural network (CNN), and post-processing. The method is evaluated in−silico for both adult and adolescent subjects. The results show significant improvements over existing methods in the literature through a comprehensive comparison in terms of root mean square error (RMSE) (8.88 ± 0.77 mg/dL) with short time lag (0.83 ± 0.40 minutes) for prediction horizons (PH) = 30 mins (minutes), and RMSE (19.90 ± 3.17 mg/dL) with time lag (16.43 ± 4.07 mins) for PH = 60 mins for virtual adult subjects. In addition, GluNet is also tested on two clinical data sets. Results show that it achieves an RMSE (19.28 ± 2.76 mg/dL) with time lag (8.03 ± 4.07 mins) for PH = 30 mins and an RMSE (31.83 ± 3.49 mg/dL) with time lag (17.78 ± 8.00 mins) for PH = 60 mins. These are the best reported results for glucose forecasting when compared with other methods including the neural network for predicting glucose (NNPG), the support vector regression (SVR), the latent variable with exogenous input (LVX), and the auto regression with exogenous input (ARX) algorithm

A Wave Variable Approach with Multiple Channel Architecture for Teleoperated System

© 2013 IEEE. Performance of teleoperation can be greatly influenced by time delay in the process of tele-manipulation with respect to accuracy and transparency. Wave variable is an effective algorithm to achieve a good stable capability. However, some traditional wave variable methods may decrease the performance of transparency and suffer the impacts of wave reflection. To deal with the problem of stability and transparency in teleoperation, in this paper, a novel wave variable method with four channel is presented to achieve stable tracking in position and force. In addition, the proposed method can achieve the distortion compensation and reduce the impacts of wave reflection. The simulation experimental results verified the tracking performance of the proposed method

A framework for measuring quality in the emergency department

There is increasing concern that medical care is of variable quality, with variable outcomes, safety, costs and experience for patients. Despite substantial efforts to improve patient safety, some studies suggest little evidence of reductions in adverse events. Furthermore, there is limited agreement about what outcomes are expected and whether increased expenditure results in a real improvement in outcome or experience. In emergency medicine, many countries have developed specific indicators to help drive improvements in patient care. Most of these are time based and there is a lack of consensus regarding which indicators are high priority and what an appropriate framework for measuring quality should look like

A Conditional Variational Framework for Dialog Generation

Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems. However, these latent variables are highly randomized, leading to uncontrollable generated responses. In this paper, we propose a framework allowing conditional response generation based on specific attributes. These attributes can be either manually assigned or automatically detected. Moreover, the dialog states for both speakers are modeled separately in order to reflect personal features. We validate this framework on two different scenarios, where the attribute refers to genericness and sentiment states respectively. The experiment result testified the potential of our model, where meaningful responses can be generated in accordance with the specified attributes.Comment: Accepted by ACL201

A Model of Management Strategy for a Quality Learning in Islamic Higher Education (Ihe)

The quality of Islamic education is generally influenced by several factors, among other things: leadership, organizational culture, lecturercompetence versus faculty student ratio, dynamic curriculum, library collections and learning facilities. The factors above are most likely to influence and impact the quality of education process in general. Developing a model of management strategy for quality learning is a minimal effort to improve quality graduates of a university. The model was developed on the basis of the following theories: (1) transformative leadership (Tichy and Devana (1997), (2) strategy of learning organization, (Peter (2002), and (3) a quality-based management (Griffin, 2004). Furthermore, the model shares the following characteristics: (1) a quality learning emerges from an effective and efficient management of academic service; (2) developing management of a quality learning is continuous lecture development; (3) lecture plays an important role in developing a quality learning; (4) a quality learning stipulates that a leader be loyal and committed to their job, wise and have a sense of democracy

A method to provide accessibility for visual components to vision impaired

Non-textual graphical information (line graphs, bar charts, pie charts, etc.) are increasingly pervasive in digital scientific literature and business reports which enabling readers to easily acquire the nature of the underlying information . These graphical components are commonly used to present data in an easy-to interpret way. Graphs are frequently used in economics, mathematics and other scientific subjects. In general term data visualization techniques are useless for blind people. Being unable to access graphical information easily is a major obstacle to blind people in pursuing a scientific study and careers .This paper suggests a method to extract implicit information of Bar chart, Pie chart, Line chart and math’s graph components of an electronic document and present them to vision impaired users in audio format. The goal is to provide simple to use, efficient, and available presentation schemes for non textual which can help vision impaired users in comprehending form without needing any further devices or equipments. A software application has been developed based on this research. The output of application is a textual summary of the graphic including the core content of the hypothesized intended message of the graphic designer. The textual summary of the graphic is then conveyed to the user by Text to Speech software .The benefit of this approach is automatic providing the user with the message and knowledge that one would gain from viewing t

A framework for automatic semantic video annotation

The rapidly increasing quantity of publicly available videos has driven research into developing automatic tools for indexing, rating, searching and retrieval. Textual semantic representations, such as tagging, labelling and annotation, are often important factors in the process of indexing any video, because of their user-friendly way of representing the semantics appropriate for search and retrieval. Ideally, this annotation should be inspired by the human cognitive way of perceiving and of describing videos. The difference between the low-level visual contents and the corresponding human perception is referred to as the ‘semantic gap’. Tackling this gap is even harder in the case of unconstrained videos, mainly due to the lack of any previous information about the analyzed video on the one hand, and the huge amount of generic knowledge required on the other. This paper introduces a framework for the Automatic Semantic Annotation of unconstrained videos. The proposed framework utilizes two non-domain-specific layers: low-level visual similarity matching, and an annotation analysis that employs commonsense knowledgebases. Commonsense ontology is created by incorporating multiple-structured semantic relationships. Experiments and black-box tests are carried out on standard video databases for action recognition and video information retrieval. White-box tests examine the performance of the individual intermediate layers of the framework, and the evaluation of the results and the statistical analysis show that integrating visual similarity matching with commonsense semantic relationships provides an effective approach to automated video annotation