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Depth-image-based rendering (DIBR) produces multiple views efficiently. However, its process lacks some viewpoint information. There will be holes, which influences the 3D video quality. Previous DIBR techniques were mainly applied to 3D images, so it only relied on the single view and the depth map to fill the holes, but insufficient repair information resulted in incorrect repair. In this paper,...
With the emerging increase of diabetes, that recently affects around 346 million people, of which more than one-third go undetected in early stage, a strong need for supporting the medical decision-making process is generated. A number of researches have focused either in using one of the algorithms or in the comparisons of the performances of algorithms on a given, usually predefined and static datasets...
The growth of energy demand and decentralised renewable energy generation (e.g. photovoltaic, eolic) can lead to electric grid imbalances requiring extra investments in the electric grid infrastructure. One of the goals of Smart-Home and Smart-Grid solutions is to solve this issue. The majority of the solutions are focused on centralised load management. Furthermore most of the Smart-Home and Smart-Grid...
Fast Fourier Transform (FFT) is used to obtain the Fourier spectrum of a periodic signal, but it is well known that special care must be taken to avoid severe distortions introduced by the sampling process. The major errors associated with this algorithm are spectral leakage and picket fence effect. This paper discusses the advancement in FFT algorithms for harmonic analysis in power system by applying...
In this paper, a Neural Network Deployment (NND) algorithm is presented to realize and synthesize Multi-Valued Logic (MVL) functions. The algorithm is combined with back-propagation learning capability and MVL operators. The operators are used to synthesize the functions. Consequently the synthesized expressions are applied by the MVL neural operators. The advantages of NND-MVL algorithm are demonstrated...
We develop a version of spectral clustering and empirically study its performance when applied to behavior-based malware clustering. In 2011, a behavior-based malware clustering algorithm was reported by Rieck et al. We hypothesize that, owing to the more complex nature of our algorithm, it will exhibit higher accuracy than Rieck's but will require greater run-time. Through experiments using three...
Histopathological specimens are prepped through a process called staining prior to analysis by the pathologist. Staining of a pathological specimen is a standard procedure used to increase the contrast between the cell and tissue structures against the background. Unfortunately, staining is a lengthy process that requires hours of preparation. Moreover, the chemicals used to perform the procedure...
We consider the problem of spatiotemporal sampling in an evolutionary process xn = Anx where an unknown operator A driving an unknown initial state x is to be recovered from a combined set of coarse spatial samples {χ|Ωο, x(1)|Ωι,· · ·, x(N)|ΩN}. In this paper, we will study the case of infinite dimensional spatially invariant evolutionary process, where the unknown initial signals x are modeled as...
Astrology has started around 4000 years back and has significantly developed over a period of time. Till date no unified rules or standards for astrological prediction exist in the world. Astrologers concentrate on providing quality services to persons rather than defining universal rules and standards for astrological prediction. Advances in artificial intelligence resulted in large number of applications...
Performing signal processing tasks on compressive measurements of data has received great attention in recent years. In this paper, we extend previous work on compressive dictionary learning by showing that more general random projections may be used, including sparse ones. More precisely, we examine compressive K-means clustering as a special case of compressive dictionary learning and give theoretical...
Pedestrian detection is paramount for advanced driver assistance systems (ADAS) and autonomous driving. As a key technology in computer vision, it also finds many other applications, such as security and surveillance etc. Generally, pedestrian detection is conducted for images in visible spectrum, which are not suitable for night time detection. Infrared (IR) or thermal imaging is often adopted for...
As vertigo is common disease, it causes by problem with Nystagmus. It is difficult to diagnosis by observation. In this paper, we propose a method to detect nystagmus for vertigo diagnosis system using eye movement velocity. This method consists of three main steps: pupil extraction, velocity of eye movement computation, and nystagmus detection. An infrared camera is used to record eye movement in...
Stack Overflow (SO) is a question and answers (Q&A) web platform on software development that is gaining in popularity. With increasing popularity often comes a very unwelcome side effect: A decrease in the average quality of a post. To keep Q&A websites like SO useful it is vital that this side effect is countered. Previous research proved to be reasonably successful in using properties...
In this paper, we present a novel moving object detection algorithm for H.264/AVC-compressed video streams. The algorithm does not require full decoding up to the pixel domain but only parsing the compressed bit streams. Thereby, only syntax elements for reconstructing (sub-)macroblock types and quantization parameters are extracted. These features are used to segment the video frames into foreground...
Software effort estimation is crucial task in software development life cycle. There are many model exists for estimating the software project effort like regression model, expert judgment, algorithmic and non-algorithmic. But, still software industries are lacking to have more accurate effort estimation method due to which they will lead to failure. In this work, an enhanced Use Case Point model...
The data mining applications such as bioinformatics, risk management, forensics etc., involves very high dimensional dataset. Due to large number of dimensions, a well known problem of “Curse of Dimensionality” occurs. This problem leads to lower accuracy of machine learning classifiers due to involvement of many insignificant and irrelevant dimensions or features in the dataset. There are many methodologies...
The real-world big data can be clustered along desired dimensions but it is limited in its applicability to large-scale problems due to its high computational complexity, user's desire, number of dimensions etc. Recently, many approaches have been proposed to accelerate the large scale data clustering. Unfortunately, these methods usually sacrifice quite a lot of information of the original data;...
It is well-known that using floating-point numbers may inevitably result in inaccurate results and sometimes even cause serious software failures. Safety-critical software often has strict requirements on the upper bound of inaccuracy, and a crucial task in testing is to check whether significant inaccuracies may be produced. The main existing approach to the floating-point inaccuracy problem is...
User interaction with web sites generates a large amount of web access data stored in the web access logs. Those data can be used for e-commerce to conduct an evaluation of possessed website pages as one of the efforts to understand the desires of the user. Through classification techniques in web usage mining, we conducted an experiment to categorize a number of data obtained from the client log...
This paper investigates the accuracy of predictive auto-scaling systems in the Infrastructure as a Service (IaaS) layer of cloud computing. The hypothesis in this research is that prediction accuracy of auto-scaling systems can be increased by choosing an appropriate time-series prediction algorithm based on the performance pattern over time. To prove this hypothesis, an experiment has been conducted...
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