scholarly journals Lossless Image Compression Techniques: A State-of-the-Art Survey

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1274 ◽  
Author(s):  
Md. Atiqur Rahman ◽  
Mohamed Hamada
Keyword(s):  
Image Compression ◽  
High Speed ◽  
Communication Theory ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Daily Basis ◽  
The State ◽  
Huge Amount ◽  
Lossless Image Compression ◽  
Art Techniques

Modern daily life activities result in a huge amount of data, which creates a big challenge for storing and communicating them. As an example, hospitals produce a huge amount of data on a daily basis, which makes a big challenge to store it in a limited storage or to communicate them through the restricted bandwidth over the Internet. Therefore, there is an increasing demand for more research in data compression and communication theory to deal with such challenges. Such research responds to the requirements of data transmission at high speed over networks. In this paper, we focus on deep analysis of the most common techniques in image compression. We present a detailed analysis of run-length, entropy and dictionary based lossless image compression algorithms with a common numeric example for a clear comparison. Following that, the state-of-the-art techniques are discussed based on some bench-marked images. Finally, we use standard metrics such as average code length (ACL), compression ratio (CR), pick signal-to-noise ratio (PSNR), efficiency, encoding time (ET) and decoding time (DT) in order to measure the performance of the state-of-the-art techniques.

scholarly journals Coinductive Algorithms for Büchi Automata

2021 ◽  
Vol 180 (4) ◽  
pp. 351-373
Author(s):  
Denis Kuperberg ◽  
Laureline Pinault ◽  
Damien Pous
Keyword(s):  
State Space ◽  
State Of The Art ◽  
The State ◽  
Specific Structure ◽  
Büchi Automata ◽  
Deterministic Automata ◽  
Buchi Automata ◽  
Language Equivalence ◽  
Art Techniques

We propose a new algorithm for checking language equivalence of non-deterministic Büchi automata. We start from a construction proposed by Calbrix, Nivat and Podelski, which makes it possible to reduce the problem to that of checking equivalence of automata on finite words. Although this construction generates large and highly non-deterministic automata, we show how to exploit their specific structure and apply state-of-the art techniques based on coinduction to reduce the state-space that has to be explored. Doing so, we obtain algorithms which do not require full determinisation or complementation.

scholarly journals A Review and Comparison of the State-of-the-Art Techniques for Atrial Fibrillation Detection and Skin Hydration

2021 ◽  
Vol 2 ◽  
Author(s):  
Sidrah Liaqat ◽  
Kia Dashtipour ◽  
Adnan Zahid ◽  
Kamran Arshad ◽  
Sana Ullah Jan ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
State Of The Art ◽  
The State ◽  
Skin Hydration ◽  
Hydration Level ◽  
Independent Review ◽  
Intelligent Models ◽  
Electrocardiogram Ecg ◽  
Fast Heart Rate ◽  
Art Techniques

Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia, with a prevalence of 1–2% in the community, increasing the risk of stroke and myocardial infarction. Early detection of AF, typically causing an irregular and abnormally fast heart rate, can help reduce the risk of strokes that are more common among older people. Intelligent models capable of automatic detection of AF in its earliest possible stages can improve the early diagnosis and treatment. Luckily, this can be made possible with the information about the heart's rhythm and electrical activity provided through electrocardiogram (ECG) and the decision-making machine learning-based autonomous models. In addition, AF has a direct impact on the skin hydration level and, hence, can be used as a measure for detection. In this paper, we present an independent review along with a comparative analysis of the state-of-the-art techniques proposed for AF detection using ECG and skin hydration levels. This paper also highlights the effects of AF on skin hydration level that is missing in most of the previous studies.

Accelerating Mobile-Cloud Computing

2015 ◽  
pp. 1933-1955
Author(s):  
Tolga Soyata ◽  
He Ba ◽  
Wendi Heinzelman ◽  
Minseok Kwon ◽  
Jiye Shi
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
High Speed ◽  
Mobile Cloud Computing ◽  
Response Times ◽  
State Of The Art ◽  
The State ◽  
Mobile Cloud ◽  
Computational Resources ◽  
Traditional Approaches

With the recent advances in cloud computing and the capabilities of mobile devices, the state-of-the-art of mobile computing is at an inflection point, where compute-intensive applications can now run on today's mobile devices with limited computational capabilities. This is achieved by using the communications capabilities of mobile devices to establish high-speed connections to vast computational resources located in the cloud. While the execution scheme based on this mobile-cloud collaboration opens the door to many applications that can tolerate response times on the order of seconds and minutes, it proves to be an inadequate platform for running applications demanding real-time response within a fraction of a second. In this chapter, the authors describe the state-of-the-art in mobile-cloud computing as well as the challenges faced by traditional approaches in terms of their latency and energy efficiency. They also introduce the use of cloudlets as an approach for extending the utility of mobile-cloud computing by providing compute and storage resources accessible at the edge of the network, both for end processing of applications as well as for managing the distribution of applications to other distributed compute resources.

High-Speed and Area-Efficient Reconfigurable Multiplexer Bank for RAM-Based Finite State Machine Implementations

2015 ◽  
Vol 24 (07) ◽  
pp. 1550101 ◽  
Author(s):  
Raouf Senhadji-Navaro ◽  
Ignacio Garcia-Vargas
Keyword(s):  
High Speed ◽  
Finite State Machine ◽  
State Of The Art ◽  
Behavioral Model ◽  
State Machine ◽  
The State ◽  
Experimental Results ◽  
State Machines ◽  
Finite State ◽  
Area Efficient

This work is focused on the problem of designing efficient reconfigurable multiplexer banks for RAM-based implementations of reconfigurable state machines. We propose a new architecture (called combination-based reconfigurable multiplexer bank, CRMUX) that use multiplexers simpler than that of the state-of-the-art architecture (called variation-based reconfigurable multiplexer bank, VRMUX). The performance (in terms of speed, area and reconfiguration cost) of both architectures is compared. Experimental results from MCNC finite state machine (FSM) benchmarks show that CRMUX is faster and more area-efficient than VRMUX. The reconfiguration cost of both multiplexer banks is studied using a behavioral model of a reconfigurable state machine. The results show that the reconfiguration cost of CRMUX is lower than that of VRMUX in most cases.

Performance and trends in recent opinion retrieval techniques

2013 ◽  
Vol 30 (1) ◽  
pp. 76-105 ◽  
Author(s):  
Sylvester O. Orimaye ◽  
Saadat M. Alhashmi ◽  
Eu-Gene Siew
Keyword(s):  
State Of The Art ◽  
Performance Comparison ◽  
The State ◽  
Future Research ◽  
Research Directions ◽  
Opinion Retrieval ◽  
Future Research Directions ◽  
And Performance ◽  
Comparison Table ◽  
Art Techniques

AbstractThis paper presents trends and performance of opinion retrieval techniques proposed within the last 8 years. We identify major techniques in opinion retrieval and group them into four popular categories. We describe the state-of-the-art techniques for each category and emphasize on their performance and limitations. We then summarize with a performance comparison table for the techniques on different datasets. Finally, we highlight possible future research directions that can help solve existing challenges in opinion retrieval.

scholarly journals Spoofing Speaker Verification System by Adversarial Examples Leveraging the Generalized Speaker Difference

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hongwei Luo ◽  
Yijie Shen ◽  
Feng Lin ◽  
Guoai Xu
Keyword(s):  
Neural Networks ◽  
Loss Function ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Speaker Verification ◽  
Signal To Noise Ratio ◽  
The State ◽  
Verification System ◽  
Adversarial Examples ◽  
Human Hearing

Speaker verification system has gained great popularity in recent years, especially with the development of deep neural networks and Internet of Things. However, the security of speaker verification system based on deep neural networks has not been well investigated. In this paper, we propose an attack to spoof the state-of-the-art speaker verification system based on generalized end-to-end (GE2E) loss function for misclassifying illegal users into the authentic user. Specifically, we design a novel loss function to deploy a generator for generating effective adversarial examples with slight perturbation and then spoof the system with these adversarial examples to achieve our goals. The success rate of our attack can reach 82% when cosine similarity is adopted to deploy the deep-learning-based speaker verification system. Beyond that, our experiments also reported the signal-to-noise ratio at 76 dB, which proves that our attack has higher imperceptibility than previous works. In summary, the results show that our attack not only can spoof the state-of-the-art neural-network-based speaker verification system but also more importantly has the ability to hide from human hearing or machine discrimination.

scholarly journals EmbedMask: Embedding Coupling for Instance Segmentation

2021 ◽  
Author(s):  
Hui Ying ◽  
Zhaojin Huang ◽  
Shu Liu ◽  
Tianjia Shao ◽  
Kun Zhou
Keyword(s):  
High Resolution ◽  
High Speed ◽  
State Of The Art ◽  
The State ◽  
Single Stage ◽  
Segmentation Method ◽  
Two Stage ◽  
Object Proposal ◽  
Segmentation Methods ◽  
Instance Segmentation

Current instance segmentation methods can be categorized into segmentation-based methods and proposal-based methods. The former performs segmentation first and then does clustering, while the latter detects objects first and then predicts the mask for each object proposal. In this work, we propose a single-stage method, named EmbedMask, that unifies both methods by taking their advantages, so it can achieve good performance in instance segmentation and produce high-resolution masks in a high speed. EmbedMask introduces two newly defined embeddings for mask prediction, which are pixel embedding and proposal embedding. During training, we enforce the pixel embedding to be close to its coupled proposal embedding if they belong to the same instance. During inference, pixels are assigned to the mask of the proposal if their embeddings are similar. This mechanism brings several benefits. First, the pixel-level clustering enables EmbedMask to generate high-resolution masks and avoids the complicated two-stage mask prediction. Second, the existence of proposal embedding simplifies and strengthens the clustering procedure, so our method can achieve high speed and better performance than segmentation-based methods. Without any bell or whistle, EmbedMask outperforms the state-of-the-art instance segmentation method Mask R-CNN on the challenging COCO dataset, obtaining more detailed masks at a higher speed.

scholarly journals A FRAMEWORK FOR COMMUNITY DETECTION IN HETEROGENEOUS MULTI-RELATIONAL NETWORKS

2014 ◽  
Vol 17 (06) ◽  
pp. 1450018 ◽  
Author(s):  
XIN LIU ◽  
WEICHU LIU ◽  
TSUYOSHI MURATA ◽  
KEN WAKITA
Keyword(s):  
Community Detection ◽  
Real World ◽  
State Of The Art ◽  
General Structure ◽  
The State ◽  
New Method ◽  
World Systems ◽  
Relational Networks ◽  
Relational Network ◽  
Art Techniques

There has been a surge of interest in community detection in homogeneous single-relational networks which contain only one type of nodes and edges. However, many real-world systems are naturally described as heterogeneous multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose a new method for detecting communities in such networks. Our method is based on optimizing the composite modularity, which is a new modularity proposed for evaluating partitions of a heterogeneous multi-relational network into communities. Our method is parameter-free, scalable, and suitable for various networks with general structure. We demonstrate that it outperforms the state-of-the-art techniques in detecting pre-planted communities in synthetic networks. Applied to a real-world Digg network, it successfully detects meaningful communities.

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