Compressed SAR Interferometry in the Big Data Era

Modern Synthetic Aperture Radar (SAR) missions provide an unprecedented massive interferometric SAR (InSAR) time series. The processing of the Big InSAR Data is challenging for long-term monitoring. Indeed, as most deformation phenomena develop slowly, a strategy of a processing scheme can be worked on reduced volume data sets. This paper introduces a novel ComSAR algorithm based on a compression technique for reducing computational efforts while maintaining the performance robustly. The algorithm divides the massive data into many mini-stacks and then compresses them. The compressed estimator is close to the theoretical Cramer–Rao lower bound under a realistic C-band Sentinel-1 decorrelation scenario. Both persistent and distributed scatterers (PSDS) are exploited in the ComSAR algorithm. The ComSAR performance is validated via simulation and application to Sentinel-1 data to map land subsidence of the salt mine Vauvert area, France. The proposed ComSAR yields consistently better performance when compared with the state-of-the-art PSDS technique. We make our PSDS and ComSAR algorithms as an open-source TomoSAR package. To make it more practical, we exploit other open-source projects so that people can apply our PSDS and ComSAR methods for an end-to-end processing chain. To our knowledge, TomoSAR is the first public domain tool available to jointly handle PS and DS targets.

Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery

In low-resolution wide-area aerial imagery, object detection algorithms are categorized as feature extraction and machine learning approaches, where the former often requires a post-processing scheme to reduce false detections and the latter demands multi-stage learning followed by post-processing. In this paper, we present an approach on how to select post-processing schemes for aerial object detection. We evaluated combinations of each of ten vehicle detection algorithms with any of seven post-processing schemes, where the best three schemes for each algorithm were determined using average F-score metric. The performance improvement is quantified using basic information retrieval metrics as well as the classification of events, activities and relationships (CLEAR) metrics. We also implemented a two-stage learning algorithm using a hundred-layer densely connected convolutional neural network for small object detection and evaluated its degree of improvement when combined with the various post-processing schemes. The highest average F-scores after post-processing are 0.902, 0.704 and 0.891 for the Tucson, Phoenix and online VEDAI datasets, respectively. The combined results prove that our enhanced three-stage post-processing scheme achieves a mean average precision (mAP) of 63.9% for feature extraction methods and 82.8% for the machine learning approach.

Speech recognition with a hearing-aid processing scheme combining beamforming with mask-informed speech enhancement

A signal processing approach combining beamforming with mask-informed speech enhancement was assessed by measuring sentence recognition in listeners with mild-to-moderate hearing impairment in adverse listening conditions that simulated the output of behind-the-ear hearing aids in a noisy classroom. Two types of beamforming were compared: binaural, with the two microphones of each aid treated as a single array, and bilateral, where independent left and right beamformers were derived. Binaural beamforming produces a narrower beam, maximising improvement in signal-to-noise ratio (SNR), but eliminates the spatial diversity that is preserved in bilateral beamforming. Each beamformer type was optimised for the true target position and implemented with and without additional speech enhancement in which spectral features extracted from the beamformer output were passed to a deep neural network trained to identify time-frequency regions dominated by target speech. Additional conditions comprising binaural beamforming combined with speech enhancement implemented using Wiener filtering or modulation-domain Kalman filtering were tested in normally-hearing (NH) listeners. Both beamformer types gave substantial improvements relative to no processing, with significantly greater benefit for binaural beamforming. Performance with additional mask-informed enhancement was poorer than with beamforming alone, for both beamformer types and both listener groups. In NH listeners the addition of mask-informed enhancement produced significantly poorer performance than both other forms of enhancement, neither of which differed from the beamformer alone. In summary, the additional improvement in SNR provided by binaural beamforming appeared to outweigh loss of spatial information, while speech understanding was not further improved by the mask-informed enhancement method implemented here.

Study on Particle Manipulation in a Metal Internal Channel under Acoustic Levitation

In order to study the acoustic levitation and manipulation of micro-particles in the heterogeneous structures inside metal, a test system for internal levitation in three-dimensional space is designed, establishing the 3D motion model of ultrasonic levitation and manipulation of micro-particles. The relationship between levitation force, particle diameter, internal channel size, and transmission thickness is established through the motion manipulation tests of multi-configuration channel levitation micro-particles in components. The results show that the proposed method can realize the following movement of levitation micro-particles at a higher speed and the control of motion accuracy in three-dimensional space. The micro-particles can be reliably suspended and continuously moved inside the components along a predesigned motion trajectory. The results provide an effective and feasible processing scheme for direct processing through the internal spatial structure.

An Efficient Distributed SPARQL Query Processing Scheme Considering Communication Costs in Spark Environments

Various distributed processing schemes were studied to efficiently utilize a large scale of RDF graph in semantic web services. This paper proposes a new distributed SPARQL query processing scheme considering communication costs in Spark environments to reduce I/O costs during SPARQL query processing. We divide a SPARQL query into several subqueries using a WHERE clause to process a query of an RDF graph stored in a distributed environment. The proposed scheme reduces data communication costs by grouping the divided subqueries in related nodes through the index and processing them, and the grouped subqueries calculate the cost of all possible query execution paths to select an efficient query execution path. The efficient query execution path is selected through the algorithm considering the data parsing cost of all possible query execution paths, amount of data communication, and queue time per node. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

Surface and Subsurface Quality Assessment of Polished Lu2O3 Single Crystal Using Quasi-Brewster Angle Technique

The sesquioxide Lu2O3 single crystal has attracted tremendous attention as potential host material for high-power solid-state lasers. As polishing is the terminal process of conventional ultra-precision machining, the quality of polished crystal directly impacts the crucial performance indicators of optics. The high melting point of Lu2O3 single crystal makes crystal preparation difficult. Therefore, investigations on the surface/subsurface quality inspection of polished Lu2O3 single crystal are scarce. In this paper, we utilize the quasi-Brewster angle technique (qBAT) based on ellipsometry to inspect the quality of polished Lu2O3 single crystal, achieving fast, non-destructive, and high-sensitive surface/subsurface damage assessment. A systematic crystal processing scheme is designed and polished Lu2O3 crystal samples are obtained. To verify the results of qBAT, the surface and subsurface quality are tested using optical profilometer and transmission electron microscope, respectively. The consistency of the test results demonstrates the feasibility, high sensitivity, and accuracy of the qBAT. To our knowledge, this is the first time that the qBAT is applied to investigate the polished surface/subsurface quality of Lu2O3 single crystal. In conclusion, this method provides a powerful approach to the high-precision characterization of the surface/subsurface quality of Lu2O3 single crystal, and has significant potential for material property study and process optimization during ultra-precision machining.

Enhanced Image Compression and Processing Scheme

Image compression refers to the process of encoding image using fewer number of bits. The major aim of lossless image compression is to reduce the redundancy and irreverence of image data for better storage and transmission of data in the better form. The lossy compression scheme leads to high compression ratio while the image experiences lost in quality. However, there are many cases where the loss of image quality or information due to compression needs to be avoided, such as medical, artistic and scientific images. Efficient lossless compression become paramount, although the lossy compressed images are usually satisfactory in divers’ cases. This paper titled Enhanced Lossless Image Compression Scheme is aimed at providing an enhanced lossless image compression scheme based on Bose, Chaudhuri Hocquenghem- Lempel Ziv Welch (BCH-LZW) lossless image compression scheme using Gaussian filter for image enhancement and noise reduction. In this paper, an efficient and effective lossless image compression technique based on LZW- BCH lossless image compression to reduce redundancies in the image was presented and image enhancement using Gaussian filter algorithm was demonstrated. Secondary method of data collection was used to collect the data. Standard research images were used to validate the new scheme. To achieve these, an object approach using Java net beans was used to develop the compression scheme. From the findings, it was revealed that the average compression ratio of the enhanced lossless image compression scheme was 1.6489 and the average bit per pixel was 5.416667. Gaussian filter image enhancement was used for noise reduction and the image was enhanced eight times the original.

BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration

The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.