Using image morphing for memory-efficient impostor rendering on GPU

Real-time rendering of large animated crowds consisting thousands of virtual humans is important for several applications including simulations, games and interactive walkthroughs; but cannot be performed using complex polygonal models at interactive frame rates. For that reason, several methods using large numbers of pre-computed image-based representations, which are called as impostors, have been proposed. These methods take the advantage of existing programmable graphics hardware to compensate the computational expense while maintaining the visual fidelity. Making the number of different virtual humans, which can be rendered in real-time, not restricted anymore by the required computational power but by the texture memory consumed for the variety and discretization of their animations. In this work, we proposed an alternative method that reduces the memory consumption by generating compelling intermediate textures using image-morphing techniques. In order to demonstrate the preserved perceptual quality of animations, where half of the key-frames were rendered using the proposed methodology, we have implemented the system using the graphical processing unit and obtained promising results at interactive frame rates

Digital sound synthesis via parallel evolutionary optimization (Paralel evrimsel eniyileme ile sayısal ses sentezleme)

In this research, we propose a novel parallelizable architecture for the optimization of various sound synthesis parameters. The architecture employs genetic algorithms to match the parameters of different sound synthesizer topologies to target sounds. The fitness function is evaluated in parallel to decrease its convergence time. Based on the proposed architecture, we have implemented a framework using the SuperCollider audio synthesis and programming environment and conducted several experiments. The results of the experiments have shown that the framework can be utilized for accurate estimation of the sound synthesis parameters at promising speeds

Augmenting conversations through context-aware multimedia retrieval based on speech recognition

Future’s environments will be sensitive and responsive to the presence of people to support them carrying out their everyday life activities, tasks and rituals, in an easy and natural way. Such interactive spaces will use the information and communication technologies to bring the computation into the physical world, in order to enhance ordinary activities of their users. This paper describes a speech-based spoken multimedia retrieval system that can be used to present relevant video-podcast (vodcast) footage, in response to spontaneous speech and conversations during daily life activities. The proposed system allows users to search the spoken content of multimedia files rather than their associated meta-information and let them navigate to the right portion where queried words are spoken by facilitating within-medium searches of multimedia content through a bag-of-words approach. Finally, we have studied the proposed system on different scenarios by using vodcasts in English from various categories, as the targeted multimedia, and discussed how it would enhance people’s everyday life activities by different scenarios including education, entertainment, marketing, news and workplace

A decision forest based feature selection framework for action recognition from RGB-Depth cameras

In this paper, we present an action recognition framework leveraging data mining capabilities of random decision forests trained on kinematic features. We describe human motion via a rich collection of kinematic feature time-series computed from the skeletal representation of the body in motion. We discriminatively optimize a random decision forest model over this collection to identify the most effective subset of features, localized both in time and space. Later, we train a support vector machine classifier on the selected features. This approach improves upon the baseline performance obtained using the whole feature set with a significantly less number of features (one tenth of the original). On MSRC-12 dataset (12 classes), our method achieves 94% accuracy. On the WorkoutSU-10 dataset, collected by our group (10 physical exercise classes), the accuracy is 98%. The approach can also be used to provide insights on the spatiotemporal dynamics of human actions

Smart Ring: Controlling Call Alert Functionality Based on Audio and Movement Analysis

ABSTRACT In this work, we present a method for controlling call alert functionality in mobile phones. It has happened for almost everybody experiencing a situation that call alert functionality is not proper for actual ambient context, leading to missing a phone call or disturbing others by a loud ring. In this work, we use audio and physical movement analysis to distinguish between different situations in which a mobile phone may ring, and adjust the call alert functionality accordingly. Considering the fact that mobile phones are usually carried in a pocket or bag, capturing ambient audio is not usually practically perfect. The novelty in our work is using information about physical movements of user of mobile device in addition to analysis of ambient audio. Analysis of user movements is based on information captured by acceleration sensors integrated in mobile phone. The call alert functionality is then adjusted based on a combination of ambient audio level and physical activities of user

Digital music performance for mobile devices based on magnetic interaction

Digital music performance requires a high degree of interaction with input controllers that can provide fast feedback on the user's action. One of the primary considerations of professional artists is a powerful and creative tool that minimizes the number of steps required for the speed-demanding processes. Nowadays, mobile devices have become popular digital instruments for musical performance. Most of the applications designed for mobile devices use touch screen, keypad, or accelerometer as interaction modalities. In this paper, we present a novel interface for musical performance that is based on a magnetic interaction between a user and a device. The proposed method constitutes a touchless interaction modality that is based on the mutual effect between the magnetic field surrounding a device and that of a properly shaped magnet. Extending the interaction space beyond the physical boundary of a device provides the user with higher degree of flexibility for musical performance which, in turn, can open doors to a wide spectrum of new functionalities in digital music performance and production

Enhancing security of linux-based android devices

Our daily lives become more and more dependent upon smartphones due to their increased capabilities. Smartphones are used in various ways from payment systems to assisting the lives of elderly or disabled people. Security threats for these devices become increasingly dangerous since there is still a lack of proper security tools for protection