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Networked Occupancy Sensor System

Energy is often wasted on systems that are used to provide services such as light, heating, air conditioning and ventilation. If these services were intelligently controlled, there is potential for significant improvements in energy conservation. A system including room sensors, database, and webserver was designed, constructed, and implemented over the course of this project. Sensors report occupancy and light status and temperature. Real-time room data is available via the webserver and is archived in the database. The system is networked via Ethernet and powered using the power over Ethernet (802.3af) standard

Staying at the Edge of Privacy: Edge Computing and Impersonal Extraction

From self-driving cars to smart city sensors, billions of devices will be connected to networks in the next few years. These devices will collect vast amounts of data which needs to be processed in real-time, overwhelming centralized cloud architectures. To address this need, the industry seeks to process data closer to the source, driving a major shift from the cloud to the ‘edge.’ This article critically investigates the privacy implications of edge computing. It outlines the abilities introduced by the edge by drawing on two recently published scenarios, an automated license plate reader and an ethnic facial detection model. Based on these affordances, three key questions arise: what kind of data will be collected, how will this data be processed at the edge, and how will this data be ‘completed’ in the cloud? As a site of intermediation between user and cloud, the edge allows data to be extracted from individuals, acted on in real-time, and then abstracted or sterilized, removing identifying information before being stored in conventional data centers. The article thus argues that edge affordances establish a fundamental new ‘privacy condition’ while sidestepping the safeguards associated with the ‘privacy proper’ of personal data use. Responding effectively to these challenges will mean rethinking person-based approaches to privacy at both regulatory and citizen-led levels

DOES: A Deep Learning-based approach to estimate roll and pitch at sea

The use of Attitude and Heading Reference Systems (AHRS) for orientation estimation is now common practice in a wide range of applications, e.g., robotics and human motion tracking, aerial vehicles and aerospace, gaming and virtual reality, indoor pedestrian navigation and maritime navigation. The integration of the high-rate measurements can provide very accurate estimates, but these can suffer from errors accumulation due to the sensors drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and techniques. As an example, camera-based solutions have drawn a large attention by the community, thanks to their low-costs and easy hardware setup; moreover, impressive results have been demonstrated in the context of Deep Learning. This work presents the preliminary results obtained by DOES, a supportive Deep Learning method specifically designed for maritime navigation, which aims at improving the roll and pitch estimations obtained by common AHRS. DOES recovers these estimations through the analysis of the frames acquired by a low-cost camera pointing the horizon at sea. The training has been performed on the novel ROPIS dataset, presented in the context of this work, acquired using the FrameWO application developed for the scope. Promising results encourage to test other network backbones and to further expand the dataset, improving the accuracy of the results and the range of applications of the method as a valid support to visual-based odometry techniques

N-Screen Application Framework

Smartphones and tablets with advanced computing ability and connectivity have already become indispensable in our daily lives. As operating systems of these computer-like handheld devices are getting more mature and stable, many users want physically separated devices to interact with one another and with shared resources in real time. Those devices may have the same type of operating systems, such as sharing between android smartphone and tablets. However, sometimes the sharing occurs among different operating systems. A user may want to use a smartphone to control the menu while the image presentation is displaying on the Internet Protocol television (IPTV), as well as the audio on a personal computer. This scenario brings about the motivation of this thesis. This thesis proposes an architecture that allows for sharing resources among many devices with separated screens at real-time. Compared with traditional mobile application framework, instead of the user experience on a specific device, the consistent user experience across multiple devices becomes the key concern. This research introduces a novel approach to implement the classical Model-View-Controller (MVC) framework in a distributed manner with a multi-layered distributed controller. To ensure consistent user experiences across multiple devices with di erent platforms, this research also adopts a channel-based Publish/Subscribe with effective server push state synchronization. The experiments evaluate the portability, message travelling latency improvement and bandwidth optimization. The results of those experiments prove the advantages of the n-Screen Application Framework (NSAF) both in portability that allows deployment on multiple devices from different manufacturers and performance improvement (both in latency and bandwidth consumption) while comparing with traditional data dissemination scenarios