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The Ocean Observatories Initiative (OOI) consists of seven research sites collecting ocean, seafloor, and meteorological data in the world's oceans, extending from the Irminger Sea to the Southern Ocean, and the East and West coasts of the United States. The Ocean Observatories Data Evaluation Team is part of the Cyberinfrastructure group at Rutgers University, tasked with reviewing the oceanographic...
A new algorithm for a compact mobile four-hydrophone array “RAPID Array” that displays the dolphin's real-time direction data on a graphical user interface (GUI) was proposed to accurately estimate the number of Ganges river dolphins from acoustic data recorded during the census. By considering the trajectory changes in dolphins' click data depending on the time (t) and azimuth (θ) in the coordinate...
This article develops a geometric framework for detecting targets, in the form of regions of interest, from certain sonar imagery. The main idea is to extract level sets from voxel images and compute local geometric features of the resulting surfaces. Examples include Gaussian and principal curvatures, radial distances, patch areas etc. These features are then compressed into histograms, or estimated...
Convolutional neural networks (CNNs) are revolutionizing machine learning, but they present significant computational challenges. Recently, many FPGA-based accelerators have been proposed to improve the performance and efficiency of CNNs. Current approaches construct a single processor that computes the CNN layers one at a time; the processor is optimized to maximize the throughput at which the collection...
Network centrality reflects node importance in networks, which is a challenging problem in social network analysis. Based on Fuzzy Set and MYCIN theory, this paper proposes a novel node centrality measuring method and models n-monkeys dataset, where n is 20. Initially, we created monkeys relationship graph and generated relationship matrix based on the monkeys' encountering times in a specific time...
Anecdotal evidence suggests that the variety of Big data is one of the most challenging problems in Computer Science research today [Stonebraker, 2012], [Ou et al., 2017], [Guo et al., 2016], [Bai et al., 2016]. First, Big data comes at us from a myriad of data sources, hence its shape and flavor differ. Second, hundreds of data management systems which work with Big data support different APIs and...
We present a novel SIMD multiply array for fixed-point and floating-point multiplication. To be concrete, the array supports one 54 (unsigned), one 32 or four 16 bits (signed/unsigned) operation. Based on the enhanced booth decode algorithm, the time overhead of the multiplications is reduced. The proposed intermediate result reuse strategy can reduce area overhead of the SIMD multiply array. The...
Functional verification is one of the key problems hindering successful design of large and complex hardware. As the base of functional verification, summary and decomposition of function points are leak-prone due to lack of standard language to abstract function points from specification. In this paper, we propose a function abstraction language, FAL, which is used for describing function points...
DNA methylation has been identified to be widely associated to complex diseases. Among biological platforms to profile DNA methylation in human, the Illumina Infinium HumanMethylation450 BeadChip (450K) has been accepted as one of the most efficient technologies. However, challenges exist in analysis of DNA methylation data generated by this technology due to widespread biases. Here we proposed a...
Down syndrome (DS) is a genetic disorder with genome dosage imbalances and micro-duplications of human chromosome 21. It is usually associated with a group of serious diseases, including intellectual disabilities, cardiac diseases, physical abnormalities, and other abnormalities. Currently, since there is no cure for human DS, screening and early detection have become the most efficient way for DS...
Compact online learning architectures could be used to enhance internet of things devices to allow them to learn directly based on data being received instead of having to ship data to a remote server for learning. This saves communications energy and enhances privacy and security as the data is not shared. The learning architectures can also be used in high performance computing and in traditional...
This paper explores the problems of the averaging and interpolation of electricity consumption and distributed photovoltaic energy generation data on higher than hour resolution levels. The paper compares different interpolation methods, like linear and spline interpolation and discusses modelling approaches for the energy data reconstruction on the basis of minutely and hourly data. The results are...
Identifying the most recent heavy hitters, i.e., finding the items with the highest appearances in a high speed data stream is a fundamental problem in real-time stream processing. The requirement of real-time stream applications raises significant challenges to this problem in terms of the processing latency, the space usage and the precision. Traditional schemes leverage the sliding windows based...
The DOA estimation problem for wideband signals has attracted much attention in the past years, and how to utilize and derive the common DOA information among frequency bins is the essential question. We address the wideband DOA estimation problem in this paper, and to solve this problem we propose a joint sparse Bayesian learning algorithm based on the sparse signal representation (SSR) of the covariance...
A detection method of short-time double-Duffing chaotic oscillator array with variable amplitude coefficients (VASTD-Duffing) is proposed for the problem of Duffing chaotic oscillator system's miss detection generated by envelope fluctuation and distribution information in the time domain of the signals. A short-time characteristic window approach and periodic extension method to envelope fluctuation...
Due to waveguide dispersion and array shape distortion, target's energy spreads in the vicinity of direction of arrival in spatial orientation spectrum. To solve this problem, a spatial spectrum estimation method using beam signal spectral decomposition is proposed. Utilizing correlation between signals which spread in multiple beams, beam signal spectrum is decomposed using singular value decomposition...
This paper introduces the basic theory and method of compressed sensing, and its application in DOA estimation. The theory uses a new sampling method through sparse sampling and reconstruction of the signal to break through the limitation of the Nyquist sampling theorem, effectively solving the inherent shortcomings of classic spatial spectrum estimation algorithms.
The speed of the target can be estimated by the sound pressure cross-correlation of the line spectrum of the moving target at different time intervals. However, a slight deviation of the line spectrum has a significant influence on the estimated result. In this paper this phenomenon is analyzed from the theory, then the speed of the target is estimated when the line spectrum is biased. Experimental...
This paper investigates the consistency in phased array element performance by extracting information from the Full Matrix Capture (FMC) of a reflection from a planar interface. The purpose of this work is to generate a robust methodology for tracking phased array performance over time, therefore, ensuring the reliability of measured data. To achieve this, a calibration method has been developed that...
The first modular 4-channel frontend for exploring phased array communications at a carrier frequency of 300 GHz was developed. A metamorphic HEMT process with a gate length of 35 nm was the key enabling technology for the integration of each transmit and receive channel. All channels implemented the quadrature direct conversion architecture. The measured RF bandwidth exceeded 55 GHz for the receive...
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