Self-Selective Correlation Ship Tracking Method for Smart Ocean System

In recent years, with the development of the marine industry, navigation environment becomes more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count the sailing ships to ensure the maritime security and facilitates the management for Smart Ocean System. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly include: 1) A self-selective model with negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of classifier at the same time; 2) A bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were higher than Discriminative Scale Space Tracking (DSST) by over 8 percentage points on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 Frames Per Second (FPS)

Adaptive Motor Resistance Video Game Exercise Apparatus and Method of Use Thereof

The invention comprises a method and/or an apparatus using computer configured exercise equipment and an electric motor provided physical resistance in conjunction with a game system, such as a video game system, where the exercise system provides real physical resistance to a user interface. Results of user interaction with the user interface are integrated into a video game, such as running on a game console. The resistance system comprises: a subject interface, software control, a controller, an electric servo assist/resist motor, an actuator, and/or a subject sensor. The system provides actual physical interaction with a resistance device as input to the game console and game run thereon

HUC-HISF: A Hybrid Intelligent Security Framework for Human-centric Ubiquitous Computing

制度:新 ; 報告番号:乙2336号 ; 学位の種類:博士(人間科学) ; 授与年月日:2012/1/18 ; 早大学位記番号:新584

Remote Adaptive Motor Resistance Training Exercise Apparatus and Method of Use Thereof

The invention comprises a method and/or an apparatus using a computer configured exercise system equipped with an electric motor to provide physical resistance to user motion in conjunction with means for sharing exercise system related data and/or user performance data with a secondary user, such as a medical professional, a physical therapist, a trainer, a computer generated competitor, and/or a human competitor. For example, the exercise system is used with a remote trainer to enhance exercise performance, with a remote medical professional for rehabilitation, and/or with a competitor in a competition, such as in a power/weightlifting competition or in a video game. The exercise system is optionally configured with an intelligent software assistant and knowledge navigator functioning as a personal assistant application

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Progetto di reti Sensori Wireless e tecniche di Fusione Sensoriale

Ambient Intelligence (AmI) envisions a world where smart, electronic environments are aware and responsive to their context. People moving into these settings engage many computational devices and systems simultaneously even if they are not aware of their presence. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. The dependence on a large amount of fixed and mobile sensors embedded into the environment makes of Wireless Sensor Networks one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes, simple devices that typically embed a low power computational unit (microcontrollers, FPGAs etc.), a wireless communication unit, one or more sensors and a some form of energy supply (either batteries or energy scavenger modules). Low-cost, low-computational power, low energy consumption and small size are characteristics that must be taken into consideration when designing and dealing with WSNs. In order to handle the large amount of data generated by a WSN several multi sensor data fusion techniques have been developed. The aim of multisensor data fusion is to combine data to achieve better accuracy and inferences than could be achieved by the use of a single sensor alone. In this dissertation we present our results in building several AmI applications suitable for a WSN implementation. The work can be divided into two main areas: Multimodal Surveillance and Activity Recognition. Novel techniques to handle data from a network of low-cost, low-power Pyroelectric InfraRed (PIR) sensors are presented. Such techniques allow the detection of the number of people moving in the environment, their direction of movement and their position. We discuss how a mesh of PIR sensors can be integrated with a video surveillance system to increase its performance in people tracking. Furthermore we embed a PIR sensor within the design of a Wireless Video Sensor Node (WVSN) to extend its lifetime. Activity recognition is a fundamental block in natural interfaces. A challenging objective is to design an activity recognition system that is able to exploit a redundant but unreliable WSN. We present our activity in building a novel activity recognition architecture for such a dynamic system. The architecture has a hierarchical structure where simple nodes performs gesture classification and a high level meta classifiers fuses a changing number of classifier outputs. We demonstrate the benefit of such architecture in terms of increased recognition performance, and fault and noise robustness. Furthermore we show how we can extend network lifetime by performing a performance-power trade-off. Smart objects can enhance user experience within smart environments. We present our work in extending the capabilities of the Smart Micrel Cube (SMCube), a smart object used as tangible interface within a tangible computing framework, through the development of a gesture recognition algorithm suitable for this limited computational power device. Finally the development of activity recognition techniques can greatly benefit from the availability of shared dataset. We report our experience in building a dataset for activity recognition. Such dataset is freely available to the scientific community for research purposes and can be used as a testbench for developing, testing and comparing different activity recognition techniques

Sailbot 2017-2018

The goal of this MQP was to build and program a robot capable of competing in the 2018 International Robotic Sailing Competition (IRSC), also known as Sailbot. This project utilized existing research on control and design of autonomous sailboats, and built on lessons learned from the last two years of WPI’s Sailbot entries. The final product of this MQP was a more reliable, easier to control, and more innovative design than last year’s event-winning boat

Sailbot 2017-2018

The goal of this MQP was to build and program a robot capable of competing in the 2018 International Robotic Sailing Competition (IRSC), also known as Sailbot. This project utilized existing research on control and design of autonomous sailboats, and built on lessons learned from the last two years of WPIs Sailbot entries. The final product of this MQP was a more reliable, easier to control, and more innovative design than last years event-winning boat

Sailbot 2017-2018

The goal of this MQP was to build and program a robot capable of competing in the 2018 International Robotic Sailing Competition (IRSC), also known as Sailbot. This project utilized existing research on control and design of autonomous sailboats, and built on lessons learned from the last two years of WPIs Sailbot entries. The final product of this MQP was a more reliable, easier to control, and more innovative design than last years event-winning boat