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A general methodology of device array mismatch characterization is introduced, analyzed and verified. Instead of measuring each device's parameter individually, the device array is configured as a data converter and the mismatch information is extracted from the differential linearity (DNL) of the converter. Systematic and random mismatch are characterized separately using the proposed decomposition...
With the development of cyber threats on the Internet, the number of malware, especially unknown malware, is also dramatically increasing. Since all of malware cannot be analyzed by analysts, it is very important to find out new malware that should be analyzed by them. In order to cope with this issue, the existing approaches focused on malware classification using static or dynamic analysis results...
Identifying the technology trends, key patents to be aware of, the key players active in the field, the product areas that are heavily patented or scarcely addressed, and the focus of a company's competitors represent crucial aspects of developing a successful strategic plan, determining a company's freedom to make and sell their product, and focusing future research & product development efforts...
A method to extract an equivalent array of dipole-moments model from only the magnitude data of near-field scan is proposed. The method can extract both magnitude and phases of the equivalent dipole-moments by using the nonlinear least square (NLS) method, although the information used for source modeling is limited to only the field magnitudes. The truncated singular-value decomposition (SVD) method...
Fast downscaling of technology features in CMOS fabrication processes have resulted in numerous insurmountablechallenges, which prompted researchers to explore alternativestorage and computing technologies. Resistive RAM (ReRAM) isa promising non-volatile storage technology with high endurance, high retention capacity and compatibility with CMOS manufacturing flow. More importantly, ReRAM devices...
Periodicity mining is used for predicting trends intime series data. There are many applications data includingtemperature, stock prices depicted in the financial market, gene expression data analysis, etc. In general, there are threetypes of periodic patterns which can be detected in the timeseries data: (1) symbol periodicity, (2) sequence periodicityor partial periodic patterns, and (3) segment...
Key foundational components of Big Data frameworks include efficient large-scale storage and high-performance linear algebra. This paper discusses efficient implementations that utilize compression techniques inspired by columnar relational databases for improving space and time profiles for vector and matrix operations. In addition, linear algebra operations are integrated with columnar relational...
With the rapid development of computer technology, web services has been widely used. In these applications, the uncertain data is in the form of streams. In view of this kind of situation, present a new generalized data structure, that is, PSUF - tree, to store uncertain data streams, all itemsets in recent window are contained in global PStree in a condensed format, establish a header table in which...
Sorting is applied in daily life from ordering simple lists to real world applications. Sorting presents the data in an ordered fashion which helps in analysis or allows computing data faster. Radix sort is a non-comparative integer sorting algorithm that sorts in a linear time complexity. Radix sort performs modulus operation on each data to extract the digits at a specific position and maintain...
This paper proposes a novel data mining framework for object detection in video surveillance system using cellular logic array for replacing contemporary, slow and unreliable techniques of video processing with faster and robust techniques, making surveillance system effective for real time video analytic.
Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) is a meridian reflecting Schmidt telescope. For each observation, it will produce tens of thousands of spectra. The spectra obtained from LAMOST pilot survey and the first two years of its regular survey, LMOST data release 2 (DR2) was released online in December 2014. This data set contains about more than four million spectra, which...
This paper presents the implementation of a Service Oriented Mobile AR Architecture focused on museum learning activities. The service-orientated architecture presented is a collaboration between a mobile AR platform and a web service framework that consumes third party service APIs or linked open data from participating museum collections. The architecture and associated codebase is used to implement...
Ever increasing data size and new requirements in data processing has fostered the development of many new database systems. The result is that many data-intensive applications are underpinned by different engines. To enable data mobility there is a need to transfer data between systems easily and efficiently. We analyze the state-of-the-art of data migration and outline research opportunities for...
In this paper we propose a new machine learning concept called randomized machine learning, in which model parameters are assumed random and data are assumed to contain random errors. Distinction of this approach from “classical” machine learning is that optimal estimation deals with the probability density functions of random parameters and the “worst” probability density of random data errors. As...
Graphs are ubiquitous and are the best data structure for representing linked data because of their flexibility, scalability, and power to deal with complexity. Storing big graphs in graph databases leads to difficult computation and increased time complexity. The best alternative is to use inmemory representations such as compact data structures. They compress the graph sufficiently such that it...
Then Open Government Data movement increases in recent years. The data has become an important part of Big Data. Data generated by different government agencies have different time and space coverages and are often focusing on various aspect of cities. These data could be co-registered in a unified spatial reference system for correlated analysis. This paper proposes a spatial grid based approach...
In this paper, we consider the problem of increasing the efficiency of heavy oil production by a qualitative enhanced oil recovery (EOR) application. A novel approach, based on the analysis of data related to successful events, is proposed. In particular, a synthesised model, targeted to automated search of wells for EOR application, is developed. For data processing, we present a novel approach based...
Read only memories (ROMs) serve as an important non-volatile memory in various hardware systems to store predefined data and programs. Its pre-determined layout pattern makes unauthorized data extraction through chip-level reverse engineering easy to carry out. This paper proposes a new transformable via-programming ROM design to address the vulnerability to reverse energy attacks. Irreversible via...
There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to index and mine very large collections of sequences, or data series. Examples of such applications come from biology, astronomy, entomology, the web, and other domains. It is not unusual for these applications to involve numbers of data series in the order of hundreds of millions to...
This paper describes the design and implementation of a tool to extract the IMDb dataset files and import them into a database. This approach differs from other published tools or research in that the previous work used relational databases. This tool uses document oriented data structures, and allows others to augment the code to change structures based on their needs. The project development required...
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