Yamini Nimmagadda

Real-time moving object recognition and tracking using computation offloading

Mobile robots are widely used for computation-intensive tasks such as surveillance, moving object recognition and tracking. Existing studies perform the computation entirely on robot processors or on dedicated servers. The robot processors are limited by their computation capability; real-time performance may not be achieved. Even though servers can perform tasks faster, the communication time between robots and servers is affected by variations in wireless bandwidths. In this paper, we present a system for realtime moving object recognition and tracking using computation offloading. Offloading migrates computation to servers to reduce the computation time on the robots. However, the migration consumes additional time, referred as communication time in this paper. The communication time is dependent on data size exchanged and the available wireless bandwidth. We estimate the computation and communication needed for the tasks and choose to execute them on robot processors or servers to minimize the total execution time, in order to satisfy real-time constraints.

Energy conservation by adaptive feature loading for mobile content-based image retrieval

We present an adaptive loading scheme to save energy for content based image retrieval (CBIR) in a mobile system. In CBIR, images are represented and compared by high-dimensional vectors called features. Loading these features into memory and comparing them consumes a significant amount of energy. Our method adaptively reduces the features to be loaded into memory for each query image. The reduction is achieved by estimating the difficulty of the query and by reusing cached features in memory for subsequent queries. We implement our method on a PDA and obtain overall energy reduction of 61.3% compared with an existing CBIR implementation.

Energy Conservation for Image Retrieval on Mobile Systems

Mobile systems such as PDAs and cell phones play an increasing role in handling visual contents such as images. Thousands of images can be stored in a mobile system with the advances in storage technology: this creates the need for better organization and retrieval of these images. Content Based Image Retrieval (CBIR) is a method to retrieve images based on their visual contents. In CBIR, images are compared by matching their numerical representations called features; CBIR is computation and memory intensive and consumes significant amounts of energy. This article examines energy conservation for CBIR on mobile systems. We present three improvements to save energy while performing the computation on the mobile system: selective loading, adaptive loading, and caching features in memory. Using these improvements adaptively reduces the features to be loaded into memory for each search. The reduction is achieved by estimating the difficulty of the search. If the images in the collection are dissimilar, fewer features are sufficient; less computation is performed and energy can be saved. We also consider the effect of consecutive user queries and show how features can be cached in memory to save energy. We implement a CBIR algorithm on an HP iPAQ hw6945 and show that these improvements can save energy and allow CBIR to scale up to 50,000 images on a mobile system. We further investigate if energy can be saved by migrating parts of the computation to a server, called computation offloading. We analyze the impact of the wireless bandwidth, server speed, number of indexed images, and the number of image queries on the energy consumption. Using our scheme, CBIR can be made energy efficient under all conditions.

Adaptation of Multimedia Presentations for Different Display Sizes in the Presence of Preferences and Temporal Constraints

Multimedia content adaptation has become important due to many devices with different amounts of resources like display sizes, memories, and computation capabilities. Existing studies perform content adaptation on web pages and other media files that have the same start times and durations. In this paper, we present a content adaptation method for multimedia presentations constituting media files with different start times and durations. We perform adaptation based on preferences and temporal constraints specified by authors and generate an order of importance among media files. Our method can automatically generate layouts by computing the locations, start times, and durations of the media files. We compare three solutions to generate layouts: 1) exhaustive search, 2) dynamic programming, and 3) greedy algorithms. We analyze the presentations by varying screen resolutions, media files, preferences, and temporal constraints. Our analysis shows that screen utilizations are 92%, 85%, and 80% for the three methods, respectively. The time to generate layouts for a presentation with 100 media files is 1200, 17, and 10 s, respectively, for the three methods.

Energy-efficient image compression in mobile devices for wireless transmission

This paper presents an adaptive compression algorithm for mobile devices to reduce the transmission energy of images through wireless networks. Our algorithm selects different compression schemes based on the amount of details in the images and the available transmission bandwidths. At low bandwidths, significant amounts of energy can be saved because the reduction in transmission energy outweighs the additional processing energy. We implement this adaptive algorithm on an HP iPAQ hw6945 Personal Digital Assistant (PDA). The algorithm is invoked before transmitting images and is transparent to users. The average energy reduction is 20% with an average delay of 1.1 seconds.respectively.

Preference-based adaptation of multimedia presentations for different display sizes

The increased availability of electronic devices with multimedia capabilities has resulted in a dramatic growth in the usage of multimedia presentations. In this paper, we present a framework for automatic generation of layouts for multimedia presentations for different screen resolutions. We develop a heuristic to find the layout for a screen of given resolution in polynomial time. The heuristic achieves 92–l98% performance compared with the exhaustive search for different screen sizes. The execution time of the heuristic is as low as 15 seconds for a presentation of 100 media files whereas the exhaustive search takes an average of 6 hours.

Establishing Trust for Computation Offloading

Computation offloading can extend the battery life- time of a portable device by migrating computation to grid- powered servers. The server may charge the portable device for the computation performed, thus providing offloading as a service. The portable device has to trust that the server has indeed performed the computation as claimed. We propose a protocol to establish trust for computation offloading. The protocol uses two keys to establish trust. The first key is part of the input for the computation; executing the program with the first key generates the second key. Without executing the program, it is difficult to generate the correct second key. We implement an algorithm that meets the requirements of the protocol on an HP iPAQ PDA and a server. We demonstrate through experiments how the algorithm establishes trust and quantify the overhead required to establish trust.

Non-photorealistic rendering for energy conservation

As images are increasingly used in wireless communication, such as mobile phones and PDAs, it is important to reduce the energy consumption for transmitting and receiving images. The energy is approximately proportional to the sizes (numbers of bytes) of the images. Many existing techniques aim to improve compression ratios while preserving the image fidelity without perceivable differences. In this paper, we propose a new approach by allowing visible distortion in the images. Our method eliminates or reduces the fine details (textures) so that new images have smaller file sizes and require less energy to transmit or receive. Even though new images may be visually different, the essential information is preserved. Our experiment uses 400 images and achieves up to 40.1% and average 32.2% reduction in file sizes.