2,682 research outputs found
Performance-oriented Cloud Provisioning: Taxonomy and Survey
Cloud computing is being viewed as the technology of today and the future.
Through this paradigm, the customers gain access to shared computing resources
located in remote data centers that are hosted by cloud providers (CP). This
technology allows for provisioning of various resources such as virtual
machines (VM), physical machines, processors, memory, network, storage and
software as per the needs of customers. Application providers (AP), who are
customers of the CP, deploy applications on the cloud infrastructure and then
these applications are used by the end-users. To meet the fluctuating
application workload demands, dynamic provisioning is essential and this
article provides a detailed literature survey of dynamic provisioning within
cloud systems with focus on application performance. The well-known types of
provisioning and the associated problems are clearly and pictorially explained
and the provisioning terminology is clarified. A very detailed and general
cloud provisioning classification is presented, which views provisioning from
different perspectives, aiding in understanding the process inside-out. Cloud
dynamic provisioning is explained by considering resources, stakeholders,
techniques, technologies, algorithms, problems, goals and more.Comment: 14 pages, 3 figures, 3 table
Intelligent visual media processing: when graphics meets vision
The computer graphics and computer vision communities have been working closely together in recent
years, and a variety of algorithms and applications have been developed to analyze and manipulate the visual media
around us. There are three major driving forces behind this phenomenon: i) the availability of big data from the
Internet has created a demand for dealing with the ever increasing, vast amount of resources; ii) powerful processing
tools, such as deep neural networks, provide e�ective ways for learning how to deal with heterogeneous visual data;
iii) new data capture devices, such as the Kinect, bridge between algorithms for 2D image understanding and
3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics
and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey
recent research on how computer vision techniques bene�t computer graphics techniques and vice versa, and cover
research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest
possible further research directions
A note on brain actuated spelling with the Berlin brain-computer interface
Brain-Computer Interfaces (BCIs) are systems capable of decoding neural activity in real time, thereby allowing a computer application to be directly controlled by the brain. Since the characteristics of such direct brain-tocomputer interaction are limited in several aspects, one major challenge in BCI research is intelligent front-end design. Here we present the mental text entry application ‘Hex-o-Spell’ which incorporates principles of Human-Computer Interaction research into BCI feedback design. The system utilises the high visual display bandwidth to help compensate for the extremely limited control bandwidth which operates with only two mental states, where the timing of the state changes encodes most of the information. The display is visually appealing, and control is robust. The effectiveness and robustness of the interface was demonstrated at the CeBIT 2006 (world’s largest IT fair) where two subjects operated the mental text entry system at a speed of up to 7.6 char/min
Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection
Pedestrian detection is an important component for safety of autonomous
vehicles, as well as for traffic and street surveillance. There are extensive
benchmarks on this topic and it has been shown to be a challenging problem when
applied on real use-case scenarios. In purely image-based pedestrian detection
approaches, the state-of-the-art results have been achieved with convolutional
neural networks (CNN) and surprisingly few detection frameworks have been built
upon multi-cue approaches. In this work, we develop a new pedestrian detector
for autonomous vehicles that exploits LiDAR data, in addition to visual
information. In the proposed approach, LiDAR data is utilized to generate
region proposals by processing the three dimensional point cloud that it
provides. These candidate regions are then further processed by a
state-of-the-art CNN classifier that we have fine-tuned for pedestrian
detection. We have extensively evaluated the proposed detection process on the
KITTI dataset. The experimental results show that the proposed LiDAR space
clustering approach provides a very efficient way of generating region
proposals leading to higher recall rates and fewer misses for pedestrian
detection. This indicates that LiDAR data can provide auxiliary information for
CNN-based approaches
Cloud-based or On-device: An Empirical Study of Mobile Deep Inference
Modern mobile applications are benefiting significantly from the advancement
in deep learning, e.g., implementing real-time image recognition and
conversational system. Given a trained deep learning model, applications
usually need to perform a series of matrix operations based on the input data,
in order to infer possible output values. Because of computational complexity
and size constraints, these trained models are often hosted in the cloud. To
utilize these cloud-based models, mobile apps will have to send input data over
the network. While cloud-based deep learning can provide reasonable response
time for mobile apps, it restricts the use case scenarios, e.g. mobile apps
need to have network access. With mobile specific deep learning optimizations,
it is now possible to employ on-device inference. However, because mobile
hardware, such as GPU and memory size, can be very limited when compared to its
desktop counterpart, it is important to understand the feasibility of this new
on-device deep learning inference architecture. In this paper, we empirically
evaluate the inference performance of three Convolutional Neural Networks
(CNNs) using a benchmark Android application we developed. Our measurement and
analysis suggest that on-device inference can cost up to two orders of
magnitude greater response time and energy when compared to cloud-based
inference, and that loading model and computing probability are two performance
bottlenecks for on-device deep inferences.Comment: Accepted at The IEEE International Conference on Cloud Engineering
(IC2E) conference 201
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