Spatial Coding Technique Research Articles

Collaborative Layout Optimization for Ship Pipes Based on Spatial Vector Coding Technique

Collaborative Layout Optimization for Ship Pipes Based on Spatial Vector Coding Technique

InFocus: A Spatial Coding Technique to Mitigate Misfocus in Near-Field LoS Beamforming

Phased arrays, commonly used in IEEE 802.11ad and 5G radios, are capable of focusing radio frequency signals in a specific direction or a spatial region. Beamforming achieves such directional or spatial concentration of signals and enables phased array-based radios to achieve high data rates. Designing beams for millimeter wave and terahertz communication using massive phased arrays, however, is challenging due to hardware constraints and the wide bandwidth in these systems. For example, beams which are optimal at the center frequency may perform poor in wideband communication systems where the radio frequencies differ substantially from the center frequency. The poor performance in such systems is due to differences in the optimal beamformers corresponding to distinct radio frequencies within the wide bandwidth. Such a mismatch leads to a misfocus effect in near-field systems and the beam squint effect in far-field systems. In this paper, we investigate the misfocus effect and propose InFocus, a low complexity technique to construct beams that are well suited for massive wideband phased arrays. The beams are constructed using a carefully designed frequency modulated waveform in the spatial dimension. InFocus mitigates beam misfocus and beam squint when applied to near-field and far-field systems.

A novel sector mass spectrograph design for high-order coded aperture Mass Spectrometry with stigmatic aberration correction

Previous works experimentally demonstrated that the application of spatially-coded apertures to simple sector mass spectrographs can result in increased signal intensity while maintaining spectral resolution comparable to that of a conventional slit aperture.[Chen, E.X., et al.: J Am Soc Mass Spectr. 26, 1633 (2015)][Russell, Z.E., et al.: J Am Soc Mass Spectr. 26, 248 (2015)] This spatial coding technique was later applied to a double-focusing Mattauch-Herzog mass spectrograph, and shown to be generally compatible with the architecture.[Russell, Z.E., et al.: J Am Soc Mass Spectr. 27, 1 (2016)] However, the achieved signal gain was limited by the spatial size of the electric sector gap. In this work, we introduce a modified version of the Mattauch-Herzog mass spectrograph with a wide-gap electric sector that is enabled by a novel, segmented-electrode approach, and show via simulation that this modified architecture alone (without aperture coding) achieves a 1.8x improvement in mass resolving power compared to a conventional Mattauch-Herzog spectrograph of equivalent size. Further, simulation of an aperture-coded version of the instrument supports a predicted signal strength increase of 50x with minimal loss of aperture clarity on the sensor due to the increased open area of the high-order coded aperture which can be passed.

A trajectory feature extraction approach based on spatial coding technique

GPS data often have position deviations in precision and are apt to be affected by noise; hence, it is essential to extract features from trajectories before performing large-scale data mining. A GeoHash-based spatial coding technique called GeoHashTree was used to index spatiotemporal trajectory points in order to improve the efficiency of nearest-neighbor search. The GeoHashTree was applied in trajectory clustering and an improved density-based clustering algorithm was proposed to reduce the time complexity of nearest-neighbor search from $O(n^2)$ to $O(n\\log{n})$. After extracting trajectory points with changing angles, the proposed clustering approach was employed to achieve deep-level feature extraction on trajectory points with changing angles, which aims to accurately identify feature points. Extensive experiments are conducted on real GPS data and the results demonstrate that the proposed trajectory-clustering algorithm based on the GeoHashTree spatial index structure can improve time performance by an average of 90.89% as well as guarantee the accuracy of clustering compared with the traditional clustering method. The visualization results show that the trajectory feature extraction approach can effectively find trajectory points with changing angles and discover a varying types of feature points from large-scale data sets. In addition, the proposed approach does not depend on road network data and can dynamically update with new incoming trajectory data as road networks change in real time.

Spatial coding-based approach for partitioning big spatial data in Hadoop

Spatial data partitioning (SDP) plays a powerful role in distributed storage and parallel computing for spatial data. However, due to skew distribution of spatial data and varying volume of spatial vector objects, it leads to a significant challenge to ensure both optimal performance of spatial operation and data balance in the cluster. To tackle this problem, we proposed a spatial coding-based approach for partitioning big spatial data in Hadoop. This approach, firstly, compressed the whole big spatial data based on spatial coding matrix to create a sensing information set (SIS), including spatial code, size, count and other information. SIS was then employed to build spatial partitioning matrix, which was used to spilt all spatial objects into different partitions in the cluster finally. Based on our approach, the neighbouring spatial objects can be partitioned into the same block. At the same time, it also can minimize the data skew in Hadoop distributed file system (HDFS). The presented approach with a case study in this paper is compared against random sampling based partitioning, with three measurement standards, namely, the spatial index quality, data skew in HDFS, and range query performance. The experimental results show that our method based on spatial coding technique can improve the query performance of big spatial data, as well as the data balance in HDFS. We implemented and deployed this approach in Hadoop, and it is also able to support efficiently any other distributed big spatial data systems.

Frame differencing-based segmentation for low bit rate video codec using H.264

In video sequence coding, a combination of temporal and spatial coding technique is used in order to remove the predictable or redundant image content and encode only the unpredictable information. The objective of video compression technique is to increase the coding efficiency and to increase the data rate savings. A segmentation-based compression method is proposed to achieve this goal. In this paper, static portions are identified using frame differencing method and segmented during the encoding process. This encoded information is passed to the synthesiser. In synthesis, information which is passed during segmentation process is added to reconstruct the video. This method is integrated with the conventional video codec H.264/AVC video codec. Experimental results substantially proved the data rate is reduced by as much as 25%.

Computational cell analysis for label-free detection of cell properties in a microfluidic laminar flow.

Although a flow cytometer, being one of the most popular research and clinical tools for biomedicine, can analyze cells based on the cell size, internal structures such as granularity, and molecular markers, it provides little information about the physical properties of cells such as cell stiffness and physical interactions between the cell membrane and fluid. In this paper, we propose a computational cell analysis technique using cells' different equilibrium positions in a laminar flow. This method utilizes a spatial coding technique to acquire the spatial position of the cell in a microfluidic channel and then uses mathematical algorithms to calculate the ratio of cell mixtures. Most uniquely, the invented computational cell analysis technique can unequivocally detect the subpopulation of each cell type without labeling even when the cell type shows a substantial overlap in the distribution plot with other cell types, a scenario limiting the use of conventional flow cytometers and machine learning techniques. To prove this concept, we have applied the computation method to distinguish live and fixed cancer cells without labeling, count neutrophils from human blood, and distinguish drug treated cells from untreated cells. Our work paves the way for using computation algorithms and fluidic dynamic properties for cell classification, a label-free method that can potentially classify over 200 types of human cells. Being a highly cost-effective cell analysis method complementary to flow cytometers, our method can offer orthogonal tests in companion with flow cytometers to provide crucial information for biomedical samples.

One-shot monochromatic symbol pattern for 3D reconstruction using perfect submap coding

Monochromatic perfect submap coding is a technique which utilizes structured light to reconstruct three-dimensional shapes. This coding strategy is robust against occlusions, suitable for dynamic scene detection, and less sensitive to colorful surface when compared to color coding. However, perfect submap patterns are often poor in terms of resolution and accuracy. In this work, the symbol density spectrum (SDS) and its features are used to choose symbols for improving resolution and decreasing decoding error of perfect submap symbol patterns. The proposed SDS provides a distribution of symbol points for reconstruction after which 10 symbol features are extracted. Following a comparative analysis of the symbol features and scene testing of symbols damage rate, a new density pattern is formed using nine symbols chosen from one group. A 3D reconstruction experiment showed that results obtained by this method are of a higher resolution compared with other existing monochromatic spatial coding techniques and are robust against color surface.

Analysis by synthesis spatial audio coding

This study presents a novel spatial audio coding (SAC) technique, called analysis by synthesis SAC (AbS-SAC), with a capability of minimising signal distortion introduced during the encoding processes. The reverse one-to-two (R-OTT), a module applied in the MPEG Surround to down-mix two channels as a single channel, is first configured as a closed-loop system. This closed-loop module offers a capability to reduce the quantisation errors of the spatial parameters, leading to an improved quality of the synthesised audio signals. Moreover, a sub-optimal AbS optimisation, based on the closed-loop R-OTT module, is proposed. This algorithm addresses a problem of practicality in implementing an optimal AbS optimisation while it is still capable of improving further the quality of the reconstructed audio signals. In terms of algorithm complexity, the proposed sub-optimal algorithm provides scalability. The results of objective and subjective tests are presented. It is shown that significant improvement of the objective performance, when compared to the conventional open-loop approach, is achieved. On the other hand, subjective test show that the proposed technique achieves higher subjective difference grade scores than the tested advanced audio coding multichannel.

Correlations between spatial compatibility effects: are arrows more like locations or words?

Responses are more efficient when their spatial mappings with features of targets are compatible compared to when they are incompatible, even when those features are irrelevant to task performance. Currently, a debate exists as to whether spatial information conveyed by different stimulus modes leads to qualitatively different spatial representations. We investigated the relations between three of the most commonly used spatial stimulus modes-arrows, locations, and location words-using correlations of compatibility effects between each of these modes as well as compatibility effects at different segments of their response time distributions. Our results show that when spatial information is irrelevant to task performance (the Simon task), the compatibility effects elicited by arrows and words are more strongly related with each other than with those of locations. However, when spatial information is task relevant (the stimulus-response compatibility SRC task), the compatibility effects elicited by arrows and locations are more related, and both are less related to the effect elicited by words. We suggest that these changing relations between stimulus modes are strategically determined based on which spatial coding technique for arrows is most advantageous to task performance. Furthermore, the varying relations between these spatial compatibility effects indicate that the compatibility effect with one stimulus mode is not always predictive of the compatibility effect in another mode.

Non-quantum implementation of quantum computation algorithm using a spatial coding technique

Non-quantum implementation of quantum information processing is studied. A spatial coding technique, which is one effective digital optical computing technique, is utilized to implement quantum teleportation efficiently. In the coding, quantum information is represented by the intensity and the phase of elemental cells. Correct operation is confirmed within the proposed scheme, which indicates the effectiveness of the proposed approach and a motive for further investigation.

One-operation image algebra and optoelectronic cellular two-layer logic array

A parallel binary image algebra with a logic operation followed by a dilation is developed in this paper. A cellular two-layer logic image processor architecture is proposed to execute such a one-operation image algebra, which consists of a parallel logic layer followed by a morphological layer. All the possible image processing functions can be written by the algebra. We construct an optoelectronic array based on optical logic operation using spatial coding techniques, optical neighborhood interconnection in a defocused system, and electronic thresholding and feedback. Any image transformation can be performed through iterative programming of the logic operations. Experimental results are also given.

Pattern recognition with spatially modulated X-rays

We present a method which may be used to identify X-ray absorbing and emitting patterns. The decision process is performed by a spatial coding technique of the X-ray detector, thereby the measured intensity value describes a code word which is assigned to the unknown pattern. We perform and experiment with eight patters which are generated by different orientations of a test mask. An algorithm for calculating for calculating and a simple technique for manufacturing the detector code masks are described.