Secure peer-to-peer trading in small- and large-scale multiplayer games

A fundamental aspect of many multiplayer online games is the ability to trade items between players. Trading may occur with items that were found in the virtual environment, included as pieces of the game, or purchased by the player as assets. Regardless of the types of items, any multiplayer game that supports trading or exchanging items in the game must do so in a secure manner. We have developed a protocol to solve the problem of secure peer-to-peer trading in games in which the primary concern is that items are exchanged fairly, and additionally that items are not duplicated. Our protocol enables one-way and two-way trades and can be extended to multi-item trades in both small- and large-scale games where trades last only for the duration of a session or are persistent. We show that our protocol addresses the security threats which it might encounter, and then provide an analysis to demonstrate its scalability.     

Physicochemical aspects of allanite flotation

This paper investigated the physicochemical properties of allanite, a RE-silicate, by measuring zeta potential in the absence and presence of three different flotation collectors （benzohydroxamic acid, sodium oleate and dodecylamine）. This data was then verified by microflotation experiments and with bubble-particle attachment pictures. The investigated properties of allanite were com- pared to those of quartz, a common gangue mineral in many RE deposits. The results of this work indicated that only dodecylamine was able to achieve a selective separation of allanite and quartz. This was accomplished by lowering the dodecylamine dosage so that only quartz was recovered by microflotation.     

Real-time occlusion tolerant detection of illegally parked vehicles

Illegally parked vehicle detection systems are considered crucial elements in the development of any video-surveillance based traffic-management system. The major challenges in this task lie in making the end solution real time, illumination invariant and occlusion tolerant. A two-stage application framework is presented which efficiently identifies vehicles parked illegally in restricted parking-zones. A real-time approach has been followed and an improved foreground segmentation method based on Segmentation History Images (SHI) is developed to identify stationary objects. A three step pixel based classification method is applied on the background segmentation output to segment adjacent moving pixels that become stationary for certain periods of time. The process then locks on to all identified stationary pixel patches, parts of which overlap with the regions of interest marked interactively a priori. The second stage of the process is applied subsequently to track all the stationary pixel patches detected during the first stage using an adaptive edge orientation based tracking method. Experimental results show that the tracking technique gives more than a 90% detection success rate, even if objects are partially occluded. The technique has been tested on the UK Home Office i-LIDS Parked Vehicle video sequences along with the University of Sussex Traffic Dataset and results are compared with other available state of the art methods.     

Design and Analysis of a Gyroscopically Controlled Micro Air Vehicle

Micro air vehicles have emerged as a popular option for diverse robotic and teleoperated applications in both open terrain and urban environments because of their inherent stealth and portability. To perform many of the tasks envisioned for micro air vehicles, agility is essential. To date, research efforts to improve agility have focused primarily on constructing complex controllers to enable existing vertical-take-off- and-landing vehicles, such as remote-controlled helicopters and quadrotors, to perform aerobatic maneuvers autonomously. In this work, we adopt a system-level perspective and analyze a new design for a rotary-wing micro air vehicle that utilizes gyroscopic dynamics for attitude control. Unlike traditional vehicles where attitude control moments are generated by aerodynamic control surfaces, the proposed vehicle will leverage the existing angular momentum of its counter rotating components. This paradigm has the potential to yield significant increases in agility when compared to state-of-the-art micro vertical take-off and landing vehicles. The proposed design reduces mechanical complexity by precluding the use of complex mechanisms, such as the swashplate. The capacity to rapidly generate large gyroscopic control moments, coupled with the precision gained from eliminating the need for complex and restrictive aerodynamic models, improves both agility and adaptability. We present the development of a gyroscopically controlled micro air vehicle including comprehensive models of the dynamics and the aerodynamics with an emphasis on the design and analysis of such systems. A dynamics simulator that incorporates these models and mechanical hardware solutions to challenges that arose during prototyping will also be presented.     

Estimating size fraction categories of coal particles on conveyor belts using image texture modeling methods

Motivation: Physical properties of coal such as particle size distribution have a large influence on the stability and operational behavior of fluidized bed reactors and metallurgical furnaces. In particular, the presence of large amounts of “fine” particles invariably has a drastic effect on plant performance as a result of impaired gas permeability characteristics of the coal or ore burden. Therefore, monitoring and control of particle size distribution profiles of such aggregate material on reactor feed streams, such as moving conveyor belts, is critical for predictable operation of these processes. Traditionally, the method of sieve analysis using stock or belt cut samples has been widely used in industry. Unfortunately, the reliability and usefulness of belt cut techniques are constrained by frequency of sampling as well as laboratory analysis turnaround times. For real-time monitoring and control purposes, automated sampling and analysis methods are more desirable. Methods: In this study, the problem of estimating the particle size distribution profile of material on a moving conveyor belt is formulated within a texture classification framework, which has its basis in machine vision and incorporates elements from statistical pattern recognition. Using exemplar images of coal particles taken on a process stream, a set of local features that compactly describes the textural properties of each image are expressed in terms of localized nonlinear features called textons. Representation of image information using textons is primarily motivated by insights from neuroscience research on the optimality of linear oriented basis functions as models of perception in early processing of visual information in the cortex regions of the human brain. Using these representations for different textures, nearest neighbor and support vector machine classification models are subsequently used to classify test images. Results: Using a comprehensive evaluation, it is shown that the use of texton representation obtained from decomposing images with linear oriented basis functions can be sufficiently discriminative compared to the use of the widely used second-order statistical features or features from other baseline models. In particular, model performance obtained with appropriately tuned filters suggest the importance of including shape and spatial structure information in an image representation for texture classification of coal particles. Furthermore, using nonlinear support vector machines rather than nearest neighbor classifiers significantly improved classification performance. A texture classification approach to particle size profile estimation has potential applications in the online monitoring of the proportion of “fines” in coal material on moving conveyor belts.