655 research outputs found
DCT Implementation on GPU
There has been a great progress in the field of graphics processors. Since, there is no rise in the speed of the normal CPU processors; Designers are coming up with multi-core, parallel processors. Because of their popularity in parallel processing, GPUs are becoming more and more attractive for many applications. With the increasing demand in utilizing GPUs, there is a great need to develop operating systems that handle the GPU to full capacity. GPUs offer a very efficient environment for many image processing applications. This thesis explores the processing power of GPUs for digital image compression using Discrete cosine transform
Optimal Resource Allocation Using Deep Learning-Based Adaptive Compression For Mhealth Applications
In the last few years the number of patients with chronic diseases that require constant monitoring increases rapidly; which motivates the researchers to develop scalable remote health applications. Nevertheless, transmitting big real-time data through a dynamic network limited by the bandwidth, end-to-end delay and transmission energy; will be an obstacle against having an efficient transmission of the data. The problem can be resolved by applying data reduction techniques on the vital signs at the transmitter side and reconstructing the data at the receiver side (i.e. the m-Health center). However, a new problem will be introduced which is the ability to receive the vital signs at the server side with an acceptable distortion rate (i.e. deformation of vital signs because of inefficient data reduction).
In this thesis, we integrate efficient data reduction with wireless networking to deliver an adaptive compression with an acceptable distortion, while reacting to the wireless network dynamics such as channel fading and user mobility. A Deep Learning (DL) approach was used to implement an adaptive compression technique to compress and reconstruct the vital signs in general and specifically the Electroencephalogram Signal (EEG) with the minimum distortion. Then, a resource allocation framework was introduced to minimize the transmission energy along with the distortion of the reconstructed signa
Point cloud data compression
The rapid growth in the popularity of Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) experiences have resulted in an exponential surge of three-dimensional data. Point clouds have emerged as a commonly employed representation for capturing and visualizing three-dimensional data in these environments. Consequently, there has been a substantial research effort dedicated to developing efficient compression algorithms for point cloud data. This Master's thesis aims to investigate the current state-of-the-art lossless point cloud geometry compression techniques, explore some of these techniques in more detail and then propose improvements and/or extensions to enhance them and provide directions for future work on this topic
Edge Intelligence : Empowering Intelligence to the Edge of Network
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe
Edge Intelligence : Empowering Intelligence to the Edge of Network
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe
Fast Compressed Segmentation Volumes for Scientific Visualization
Voxel-based segmentation volumes often store a large number of labels and
voxels, and the resulting amount of data can make storage, transfer, and
interactive visualization difficult. We present a lossless compression
technique which addresses these challenges. It processes individual small
bricks of a segmentation volume and compactly encodes the labelled regions and
their boundaries by an iterative refinement scheme. The result for each brick
is a list of labels, and a sequence of operations to reconstruct the brick
which is further compressed using rANS-entropy coding. As the relative
frequencies of operations are very similar across bricks, the entropy coding
can use global frequency tables for an entire data set which enables efficient
and effective parallel (de)compression. Our technique achieves high throughput
(up to gigabytes per second both for compression and decompression) and strong
compression ratios of about 1% to 3% of the original data set size while being
applicable to GPU-based rendering. We evaluate our method for various data sets
from different fields and demonstrate GPU-based volume visualization with
on-the-fly decompression, level-of-detail rendering (with optional on-demand
streaming of detail coefficients to the GPU), and a caching strategy for
decompressed bricks for further performance improvement.Comment: IEEE Vis 202
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