• Journal of Biomedical Engineering Research
• /
• v.23 no.5
• /
• pp.351-364
• /
• 2002

Conventional 3D ultrasound imaging using mechanical ID arrays suffers from poor elevation resolution due to the limited depth-of-focus (DOF). On the other hand, 3D imaging systems using 2D phased arrays have a large number of active channels and hence require a very expensive and bulky beamforming hardware. To overcome these limitations, a new real-time volumetric imaging method using curved 2-D arrays is presented, in which a small subaperture, consisting of 256 elements, moves across the array surface to scan a volume of interest. For this purpose, a 2-D curved array is designed which consists of 90$\times$46 elements with 1.5λ inter-element spacing and has the same view angles along both the lateral and elevation directions as those of a commercial mechanical 1-D array. In the proposed method, transmit and receive subapertures are constructed by cutting the four corners of a rectangular aperture to obtain a required image qualify with a small number of active channels. In addition the receive subaperture size is increased by using a sparse array scheme that uses every other elements in both directions. To suppress the grating lobes elevated due to the increase in clement spacing, fold-over array scheme is adopted in transmit, which doubles the effective size of a transmit aperture in each direction. Computer simulation results show that the proposed method can provide almost the same and greatly improved resolutions in the lateral and elevation directions, respectively compared with the conventional 3D imaging with a mechanical 1-D array.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.5
• /
• pp.1171-1176
• /
• 2011

This paper describes an analysis on the relationship between elemental image size and object locations in the computational integral imaging reconstruction and in the pickup using a periodically-distributed lenslet array. A sparse sampling effect arises from a periodically-distributed lenslet array in the pickup of 3D objects. The relationship between elemental image size and object location is also reported. Based on the analysis, a method to eliminate the sparse sampling is proposed. To show the effectiveness of the proposed method, experimental results are carried out. It turns out that the theory works.

• Journal of Biomedical Engineering Research
• /
• v.23 no.6
• /
• pp.419-430
• /
• 2002

In this paper. we propose an efficient method for implementing hi-directional pixel-based focusing(BiPBF) based on a sparse array imaging technique. The proposed method can improve spatial resolution and frame rate of ultrasound imaging with reduced hardware complexity by synthesizing transmit apertures with a small number of sparsely distributed subapertures. As the distance between adjacent subapertures increases, however. the image resolution tends to decrease due to the elevation of grating lobes. Such grating lobes can be eliminated in conventional synthetic aperture imaging techniques. On the contrary, grating lobes arisen from employing sparse synthetic transmit apertures can not be eliminated, which has been shown analytically in this paper. We also propose the condition and method for suppressing the grating lobes below -40dB, which is generally required in practical imaging. by placing the transmit focal depth at a near depth and properly selecting the subaperture distance in Proportion to receive aperture size. The results of both the Phantom and in vivo experiments show that the proposed method implements two-wav dynamic focusing using a smaller number of subapertures, resulting in reduced system complexity and increased frame rate.

• Journal of Korea Spatial Information System Society
• /
• v.12 no.1
• /
• pp.49-56
• /
• 2010

Many researches of frequent pattern mining technique for detecting unknown patterns on spatial data have studied actively. Existing data structures have classified into tree-structure and array-structure, and those structures show the weakness of performance on dense or sparse data. Since spatial data have obtained the characteristics of dense and sparse patterns, it is important for us to mine quickly dense and sparse patterns using only single algorithm. In this paper, we propose novel data structure as compressed patricia frequent pattern tree and frequent pattern mining algorithm based on proposed data structure which can detect frequent patterns quickly in terms of both dense and sparse frequent patterns mining. In our experimental result, proposed algorithm proves about 10 times faster than existing FP-Growth algorithm on both dense and sparse data.

• The Journal of the Acoustical Society of Korea
• /
• v.38 no.2
• /
• pp.168-176
• /
• 2019

When an array receives a signal with a frequency higher than the design frequency, there is an ambiguity in beamforming due to spatial aliasing. In order to overcome this problem, Abadi proposed frequency-difference beamforming. However, there is a constraint that the minimum frequency bandwidth is required according to the value of the difference frequency. In this paper, we propose a method to find the direction of the target signal with spatial aliasing based on the frequency-wavenumber spectrum combined with Radon transform. The proposed method can estimate the direction of the target without ambiguities when the signal has nonnegligible bandwidth. We tested the algorithm by simulating a broadband signal and verified the results with the frequency-difference beamforming method using SAVEX15 (Shallow Water Acoustic Variability EXperiment 2015)'s shrimp noise data.

• ETRI Journal
• /
• v.44 no.6
• /
• pp.925-935
• /
• 2022

In this study, we propose a limited feedback-based frequency divided group beamforming with sparse space-frequency transmit diversity coded orthogonal frequency division multiplexing (OFDM) system for ultrareliable low latency communication (URLLC) scenario. The proposed scheme has several advantages over the traditional hybrid beamforming approach, including not requiring downlink channel state information for baseband precoding, supporting distributed multipoint transmission structures for diversity, and reducing beam sweeping latency with little uplink overhead. These are all positive aspects of physical layer characteristics intended for URLLC. It is suggested in the system to manage the multipoint transmission structure realized by distributed panels using a power allocation method based on cooperative game theory. Link-level simulations demonstrate that the proposed scheme offers reliability by achieving both higher diversity order and array gain in a nonline-of-sight channel of selectivity and limited spatial scattering.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.3
• /
• pp.191-199
• /
• 2020

Currently, studies on the estimation algorithm based on compressive sensing are actively underway, but to the best of our knowledge, no study on the performance of the Sparse Spectrum Fitting (SpSF) algorithm in nonuniform sensor arrays has been made. This paper deals with the derivation of the compressive sensing based covariance fitting algorithm extended to the frequency domain. In addition, it shows the performance of directon-of-arrival estimation of the frequency domain SpSF algorithm in non-uniform linear sensor array system and the sensor array failure situation.

• Journal of the Institute of Convergence Signal Processing
• /
• v.4 no.3
• /
• pp.1-6
• /
• 2003

It is difficult to detect and to track the moving targets like tanks and diesel vehicles due to the variety of terrain and moving of targets. It is possible to be happened the aliasing conditions as the difficulty of antenna deployment in the complex environment like the battle fields. In this paper, we study the problem of detecting and tracking of moving targets which are emitting wideband signals under severe spatial aliasing conditions because of the sparse arrays. We developed a direction of arrival(DOA) estimation algorithm based on subband MUSIC(Multiple Signal Classification) method which produces high-resolution estimation. In this algorithm, the true bearings are invariant regardless of changes of frequency bands while the aliased false bearings vary. As a result, the proposed algorithm overcomes the aliasing effects and improves the localization performance in sparse passive arrays.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2000.06a
• /
• pp.1493-1510
• /
• 2000

Theoretical models that can be used to predict the range of main lobe widths and the probability distribution of the peak sidelobe levels of two-dimensionally sparse arrays are presented here. The arrays are considered to comprise microphones that are randomly positioned on a segmented grid of a given size. First, approximate expressions for the expected squared magnitude of the aperture smoothing function and the variance of the squared magnitude of the aperture smoothing function about this mean are formulated for the random arrays considered in the present study. By using the variance function, the mean value and the lower end of the range i.e., the first I percent of the mainlobe distribution can be predicted with reasonable accuracy. To predict the probability distribution of the peak sidelobe levels, distributions of levels are modeled by a Weibull distribution at each peak in the sidelobe region of the expected squared magnitude of the aperture smoothing function. The two parameters of the Weibull distribution are estimated from the means and variances of the levels at the corresponding locations. Next, the probability distribution of the peak sidelobe levels are assumed to be determined by a procedure in which the peak sidelobe level is determined as the maximum among a finite number of independent random sidelobe levels. It is found that the model obtained from the above approach predicts the probability density function of the peak sidelobe level distribution reasonably well for the various combinations of two different numbers of microphones and grid sizes tested in the present study. The application of these models to the design of random, sparse arrays having specified performance levels is also discussed.

• Journal of KIISE
• /
• v.43 no.8
• /
• pp.910-918
• /
• 2016

A tensor is a multi-dimensional array that represents many data such as (user, user, time) in the social network system. A tensor generator is an important tool for multi-dimensional data mining research with various applications including simulation, multi-dimensional data modeling/understanding, and sampling/extrapolation. However, existing tensor generators cannot generate sparse tensors like real-world tensors that obey power law. In addition, they have limitations such as tensor sizes that can be processed and additional time required to upload generated tensor to distributed systems for further analysis. In this study, we propose TeT, a distributed tera-scale tensor generator to solve these problems. TeT generates sparse random tensor as well as sparse R-MAT and Kronecker tensor without any limitation on tensor sizes. In addition, a TeT-generated tensor is immediately ready for further tensor analysis on the same distributed system. The careful design of TeT facilitates nearly linear scalability on the number of machines.