scholarly journals Joint Sparse and Low-Rank Multi-Task Learning with Extended Multi-Attribute Profile for Hyperspectral Target Detection

2019 ◽  
Vol 11 (2) ◽  
pp. 150 ◽  
Author(s):  
Xing Wu ◽  
Xia Zhang ◽  
Nan Wang ◽  
Yi Cen
Keyword(s):  
Performance Improvement ◽  
Target Detection ◽  
State Of The Art ◽  
Low Rank ◽  
The Past ◽  
Task Learning ◽  
Spatial Features ◽  
The Difference ◽  
Morphological Attribute ◽  
Background Pixel

Target detection is an active area in hyperspectral imagery (HSI) processing. Many algorithms have been proposed for the past decades. However, the conventional detectors mainly benefit from the spectral information without fully exploiting the spatial structures of HSI. Besides, they primarily use all bands information and ignore the inter-band redundancy. Moreover, they do not make full use of the difference between the background and target samples. To alleviate these problems, we proposed a novel joint sparse and low-rank multi-task learning (MTL) with extended multi-attribute profile (EMAP) algorithm (MTJSLR-EMAP). Briefly, the spatial features of HSI were first extracted by morphological attribute filters. Then the MTL was exploited to reduce band redundancy and retain the discriminative information simultaneously. Considering the distribution difference between the background and target samples, the target and background pixels were separately modeled with different regularization terms. In each task, a background pixel can be low-rank represented by the background samples while a target pixel can be sparsely represented by the target samples. Finally, the proposed algorithm was compared with six detectors including constrained energy minimization (CEM), adaptive coherence estimator (ACE), hierarchical CEM (hCEM), sparsity-based detector (STD), joint sparse representation and MTL detector (JSR-MTL), independent encoding JSR-MTL (IEJSR-MTL) on three datasets. Corresponding to each competitor, it has the average detection performance improvement of about 19.94%, 22.53%, 16.92%, 14.87%, 14.73%, 4.21% respectively. Extensive experimental results demonstrated that MTJSLR-EMAP outperforms several state-of-the-art algorithms.

scholarly journals Detecting Moving Target on Ground Based on Its Shadow by Using VideoSAR

2021 ◽  
Vol 13 (16) ◽  
pp. 3291
Author(s):  
Zhihua He ◽  
Zihan Li ◽  
Xing Chen ◽  
Anxi Yu ◽  
Tianzhu Yi ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Detection ◽  
State Of The Art ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Moving Target ◽  
Moving Target Detection ◽  
Frame Difference ◽  
The Difference ◽  
Distinct Features

Video synthetic aperture radar (VideoSAR) can detect and identify a moving target based on its shadow. A slowly moving target has a shadow with distinct features, but it cannot be detected by state-of-the-art difference-based algorithms because of minor variations between adjacent frames. Furthermore, the detection boxes generated by difference-based algorithms often contain such defects as misalignments and fracture. In light of these problems, this study proposed a robust moving target detection (MTD) algorithm for objects on the ground by fusing the background frame detection results and the difference between frames over multiple intervals. We also discuss defects that occur in conventional MTD algorithms. The difference in background frame was introduced to overcome the shortcomings of difference-based algorithms and acquire the shadow regions of objects. This was fused with the multi-interval frame difference to simultaneously extract the moving target at different velocities while identifying false alarms. The results of experiments on empirically acquired VideoSAR data verified the performance of the proposed algorithm in terms of detecting a moving target on the ground based on its shadow.

scholarly journals From Zero-Shot Learning to Cold-Start Recommendation

2019 ◽  
Vol 33 ◽  
pp. 4189-4196 ◽  
Author(s):  
Jingjing Li ◽  
Mengmeng Jing ◽  
Ke Lu ◽  
Lei Zhu ◽  
Yang Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Performance Improvement ◽  
User Behavior ◽  
State Of The Art ◽  
Cold Start ◽  
Feature Representation ◽  
The Other ◽  
Low Rank ◽  
Significant Performance ◽  
First Time

Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are independently investigated in different communities. This paper, however, reveals that ZSL and CSR are two extensions of the same intension. Both of them, for instance, attempt to predict unseen classes and involve two spaces, one for direct feature representation and the other for supplementary description. Yet there is no existing approach which addresses CSR from the ZSL perspective. This work, for the first time, formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR. Specifically, we propose a Lowrank Linear Auto-Encoder (LLAE), which challenges three cruxes, i.e., domain shift, spurious correlations and computing efficiency, in this paper. LLAE consists of two parts, a low-rank encoder maps user behavior into user attributes and a symmetric decoder reconstructs user behavior from user attributes. Extensive experiments on both ZSL and CSR tasks verify that the proposed method is a win-win formulation, i.e., not only can CSR be handled by ZSL models with a significant performance improvement compared with several conventional state-of-the-art methods, but the consideration of CSR can benefit ZSL as well.

Review of Magnetron Developments

Frequenz ◽  
2016 ◽  
Vol 70 (9-10) ◽  
Author(s):  
Sandeep Kumar Vyas ◽  
Rajendra Kumar Verma ◽  
Shivendra Maurya ◽  
V.V.P. Singh
Keyword(s):  
Performance Improvement ◽  
Performance Enhancement ◽  
State Of The Art ◽  
First Century ◽  
Foreseeable Future ◽  
High Power Microwave ◽  
The Past ◽  
Different Types ◽  
Microwave Sources ◽  
Power Microwave

AbstractMagnetrons have been the most efficient high power microwave sources for decades. In the twenty-first century, many of the development works are headed towards the performance improvement of CW industrial magnetrons. In this review article, the development works and techniques, used on different types of magnetrons, for the performance enhancement in the past two decades have been discussed. The article focuses on the state of the art of CW magnetron and the direction it will take in foreseeable future. In addition it also glimpses some of the major variants of magnetron which have further opened up scope in mm-THz spectrum of electromagnetism.

scholarly journals Application of Microfluidics in Immunoassay: Recent Advancements

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Yuxing Shi ◽  
Peng Ye ◽  
Kuojun Yang ◽  
Jie Meng ◽  
Jiuchuan Guo ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Biochemical Analysis ◽  
Driving Forces ◽  
State Of The Art ◽  
Point Of Care ◽  
Digital Microfluidics ◽  
Microfluidic Chips ◽  
Point Of Care Testing ◽  
Microfluidic Platform ◽  
The Past

In recent years, point-of-care testing has played an important role in immunoassay, biochemical analysis, and molecular diagnosis, especially in low-resource settings. Among various point-of-care-testing platforms, microfluidic chips have many outstanding advantages. Microfluidic chip applies the technology of miniaturizing conventional laboratory which enables the whole biochemical process including reagent loading, reaction, separation, and detection on the microchip. As a result, microfluidic platform has become a hotspot of research in the fields of food safety, health care, and environmental monitoring in the past few decades. Here, the state-of-the-art application of microfluidics in immunoassay in the past decade will be reviewed. According to different driving forces of fluid, microfluidic platform is divided into two parts: passive manipulation and active manipulation. In passive manipulation, we focus on the capillary-driven microfluidics, while in active manipulation, we introduce pressure microfluidics, centrifugal microfluidics, electric microfluidics, optofluidics, magnetic microfluidics, and digital microfluidics. Additionally, within the introduction of each platform, innovation of the methods used and their corresponding performance improvement will be discussed. Ultimately, the shortcomings of different platforms and approaches for improvement will be proposed.

scholarly journals FE-YOLO: A Feature Enhancement Network for Remote Sensing Target Detection

2021 ◽  
Vol 13 (7) ◽  
pp. 1311
Author(s):  
Danqing Xu ◽  
Yiquan Wu
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
State Of The Art ◽  
Computing Power ◽  
Fast Detection ◽  
Feature Enhancement ◽  
The Past ◽  
Small Targets ◽  
Better Than ◽  
Great Development

In the past few decades, target detection from remote sensing images gained from aircraft or satellites has become one of the hottest topics. However, the existing algorithms are still limited by the detection of small remote sensing targets. Benefiting from the great development of computing power, deep learning has also made great breakthroughs. Due to a large number of small targets and complexity of background, the task of remote sensing target detection is still a challenge. In this work, we establish a series of feature enhancement modules for the network based on YOLO (You Only Look Once -V3 to improve the performance of feature extraction. Therefore, we term our proposed network as FE-YOLO. In addition, to realize fast detection, the original Darknet-53 was simplified. Experimental results on remote sensing datasets show that our proposed FE-YOLO performs better than other state-of-the-art target detection models.

A Macintosh Interface for the Cameca Camebax-Micro Electron Microprobe

1990 ◽  
Vol 48 (1) ◽  
pp. 438-439
Author(s):  
Carl E. Henderson
Keyword(s):  
Electron Microprobe ◽  
Processing Speed ◽  
Word Processing ◽  
State Of The Art ◽  
Computer Control ◽  
Third Party ◽  
Disk Storage ◽  
The Past ◽  
User Facility ◽  
Instrument Control

Over the past few years it has become apparent in our multi-user facility that the computer system and software supplied in 1985 with our CAMECA CAMEBAX-MICRO electron microprobe analyzer has the greatest potential for improvement and updating of any component of the instrument. While the standard CAMECA software running on a DEC PDP-11/23+ computer under the RSX-11M operating system can perform almost any task required of the instrument, the commands are not always intuitive and can be difficult to remember for the casual user (of which our laboratory has many). Given the widespread and growing use of other microcomputers (such as PC’s and Macintoshes) by users of the microprobe, the PDP has become the “oddball” and has also fallen behind the state-of-the-art in terms of processing speed and disk storage capabilities. Upgrade paths within products available from DEC are considered to be too expensive for the benefits received. After using a Macintosh for other tasks in the laboratory, such as instrument use and billing records, word processing, and graphics display, its unique and “friendly” user interface suggested an easier-to-use system for computer control of the electron microprobe automation. Specifically a Macintosh IIx was chosen for its capacity for third-party add-on cards used in instrument control.

Smart Estimation Ver-H.1.0

2018 ◽  
Author(s):  
Sigit Haryadi
Keyword(s):  
Sample Size ◽  
Confidence Level ◽  
Time Span ◽  
Regression Equation ◽  
Weight Factor ◽  
The Past ◽  
Level Of Confidence ◽  
The Future ◽  
Future Value ◽  
The Difference

We cannot be sure exactly what will happen, we can only estimate by using a particular method, where each method must have the formula to create a regression equation and a formula to calculate the confidence level of the estimated value. This paper conveys a method of estimating the future values, in which the formula for creating a regression equation is based on the assumption that the future value will depend on the difference of the past values divided by a weight factor which corresponding to the time span to the present, and the formula for calculating the level of confidence is to use "the Haryadi Index". The advantage of this method is to remain accurate regardless of the sample size and may ignore the past value that is considered irrelevant.

scholarly journals Smoke recognition network based on dynamic characteristics

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092566
Author(s):  
Dahan Wang ◽  
Sheng Luo ◽  
Li Zhao ◽  
Xiaoming Pan ◽  
Muchou Wang ◽  
...  
Keyword(s):  
Dynamic Characteristics ◽  
State Of The Art ◽  
The State ◽  
Detection Accuracy ◽  
Static Characteristics ◽  
Good Tool ◽  
Early Signal ◽  
Fuzzy Objects ◽  
The Difference ◽  
Smoke Recognition

Fire is a fierce disaster, and smoke is the early signal of fire. Since such features as chrominance, texture, and shape of smoke are very special, a lot of methods based on these features have been developed. But these static characteristics vary widely, so there are some exceptions leading to low detection accuracy. On the other side, the motion of smoke is much more discriminating than the aforementioned features, so a time-domain neural network is proposed to extract its dynamic characteristics. This smoke recognition network has these advantages:(1) extract the spatiotemporal with the 3D filters which work on dynamic and static characteristics synchronously; (2) high accuracy, 87.31% samples being classified rightly, which is the state of the art even in a chaotic environments, and the fuzzy objects for other methods, such as haze, fog, and climbing cars, are distinguished distinctly; (3) high sensitiveness, smoke being detected averagely at the 23rd frame, which is also the state of the art, which is meaningful to alarm early fire as soon as possible; and (4) it is not been based on any hypothesis, which guarantee the method compatible. Finally, a new metric, the difference between the first frame in which smoke is detected and the first frame in which smoke happens, is proposed to compare the algorithms sensitivity in videos. The experiments confirm that the dynamic characteristics are more discriminating than the aforementioned static characteristics, and smoke recognition network is a good tool to extract compound feature.

Critical Technical Issues in High Voltage SiC Power Devices

2008 ◽  
Vol 600-603 ◽  
pp. 895-900 ◽  
Author(s):  
Anant K. Agarwal ◽  
Albert A. Burk ◽  
Robert Callanan ◽  
Craig Capell ◽  
Mrinal K. Das ◽  
...  
Keyword(s):  
Threshold Voltage ◽  
Carrier Mobility ◽  
Negative Charge ◽  
State Of The Art ◽  
Power Devices ◽  
Power Mosfets ◽  
Large Numbers ◽  
Technical Issues ◽  
The Difference ◽  
Difficult Issue

In this paper, we review the state of the art of SiC switches and the technical issues which remain. Specifically, we will review the progress and remaining challenges associated with SiC power MOSFETs and BJTs. The most difficult issue when fabricating MOSFETs has been an excessive variation in threshold voltage from batch to batch. This difficulty arises due to the fact that the threshold voltage is determined by the difference between two large numbers, namely, a large fixed oxide charge and a large negative charge in the interface traps. There may also be some significant charge captured in the bulk traps in SiC and SiO2. The effect of recombination-induced stacking faults (SFs) on majority carrier mobility has been confirmed with 10 kV Merged PN Schottky (MPS) diodes and MOSFETs. The same SFs have been found to be responsible for degradation of BJTs.

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

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