scholarly journals Multi-Indices Quantification for Left Ventricle via DenseNet and GRU-Based Encoder-Decoder with Attention

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhi Liu ◽  
Yunhua Lu ◽  
Xiaochuan Zhang ◽  
Sen Wang ◽  
Shuo Li ◽  
...  
Keyword(s):  
Left Ventricle ◽  
State Of The Art ◽  
Input Sequence ◽  
Time Sequence ◽  
Sequence Information ◽  
Spatial Features ◽  
L2 Norm ◽  
Different Types ◽  
Gated Recurrent Unit ◽  
Temporal Feature

More and more research on left ventricle quantification skips segmentation due to its requirement of large amounts of pixel-by-pixel labels. In this study, a framework is developed to directly quantify left ventricle multiple indices without the process of segmentation. At first, DenseNet is utilized to extract spatial features for each cardiac frame. Then, in order to take advantage of the time sequence information, the temporal feature for consecutive frames is encoded using gated recurrent unit (GRU). After that, the attention mechanism is integrated into the decoder to effectively establish the mappings between the input sequence and corresponding output sequence. Simultaneously, a regression layer with the same decoder output is used to predict multi-indices of the left ventricle. Different weights are set for different types of indices based on experience, and l2-norm is used to avoid model overfitting. Compared with the state-of-the-art (SOTA), our method can not only produce more competitive results but also be more flexible. This is because the prediction results in our study can be obtained for each frame online while the SOTA only can output results after all frames are analyzed.

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

scholarly journals Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 261-273
Author(s):  
Mario Manzo ◽  
Simone Pellino
Keyword(s):  
Network Architecture ◽  
State Of The Art ◽  
Ensemble Classification ◽  
Effective Vaccine ◽  
Rt Pcr ◽  
Neural Network Architecture ◽  
Experimental Phase ◽  
Different Types ◽  
Polymerase Chain ◽  
Better Than

COVID-19 has been a great challenge for humanity since the year 2020. The whole world has made a huge effort to find an effective vaccine in order to save those not yet infected. The alternative solution is early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) tests or thorax Computer Tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for image analysis. They optimize the classification design task, which is essential for an automatic approach with different types of images, including medical. In this paper, we adopt a pretrained deep convolutional neural network architecture in order to diagnose COVID-19 disease from CT images. Our idea is inspired by what the whole of humanity is achieving, as the set of multiple contributions is better than any single one for the fight against the pandemic. First, we adapt, and subsequently retrain for our assumption, some neural architectures that have been adopted in other application domains. Secondly, we combine the knowledge extracted from images by the neural architectures in an ensemble classification context. Our experimental phase is performed on a CT image dataset, and the results obtained show the effectiveness of the proposed approach with respect to the state-of-the-art competitors.

A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network

2021 ◽  
Vol 11 (3) ◽  
pp. 1327
Author(s):  
Rui Zhang ◽  
Zhendong Yin ◽  
Zhilu Wu ◽  
Siyang Zhou
Keyword(s):  
Neural Network ◽  
Communication Systems ◽  
Parallel Structure ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Spatial Features ◽  
Temporal Features ◽  
Different Types ◽  
In Series ◽  
Parallel Network

Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset.

License Plate Tilt Correction: A Review

2021 ◽  
Vol 39 (1B) ◽  
pp. 101-116
Author(s):  
Nada N. Kamal ◽  
Enas Tariq
Keyword(s):  
State Of The Art ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition ◽  
Tilt Correction ◽  
Detection Algorithms ◽  
Essential Step ◽  
Tilting Angle ◽  
Different Types ◽  
Line Fitting

Tilt correction is an essential step in the license plate recognition system (LPR). The main goal of this article is to provide a review of the various methods that are presented in the literature and used to correct different types of tilt that appear in the digital image of the license plates (LP). This theoretical survey will enable the researchers to have an overview of the available implemented tilt detection and correction algorithms. That’s how this review will simplify for the researchers the choice to determine which of the available rotation correction and detection algorithms to implement while designing their LPR system. This review also simplifies the decision for the researchers to choose whether to combine two or more of the existing algorithms or simply create a new efficient one. This review doesn’t recite the described models in the literature in a hard-narrative tale, but instead, it clarifies how the tilt correction stage is divided based on its initial steps. The steps include: locating the plate corners, finding the tilting angle of the plate, then, correcting its horizontal, vertical, and sheared inclination. For the tilt correction stage, this review clarifies how state-of-the-art literature handled each step individually. As a result, it has been noticed that line fitting, Hough transform, and Randon transform are the most used methods to correct the tilt of a LP.

Report from the European Society of Gynaecological Oncology (ESGO) 2020 State-of-the-Art Virtual Meeting

2021 ◽  
Vol 31 (5) ◽  
pp. 658-669
Author(s):  
Zoia Razumova ◽  
Nicolò Bizzarri ◽  
Joanna Kacperczyk-Bartnik ◽  
Andrei Pletnev ◽  
Antonio Gonzalez Martin ◽  
...  
Keyword(s):  
European Society ◽  
State Of The Art ◽  
Opinion Leaders ◽  
European Network ◽  
Case Presentation ◽  
Gynaecological Oncology ◽  
Virtual Meeting ◽  
Different Types ◽  
Cancer Guidelines ◽  
First Time

This is a report from the European Society of Gynaecological Oncology State-of-the-Art Virtual Meeting held December 14–16, 2020. The unique 3-day conference offered comprehensive state-of-the-art summaries on the major advances in the treatment of different types of gynecological cancers. Sessions opened with a case presentation followed by a keynote lecture and interactive debates with opinion leaders in the field. The speakers also presented scientific reviews on the clinical trial landscape in collaboration with the European Network of Gynecological Oncological Trial (ENGOT) groups. In addition, the new ESGO-ESRTO-ESP endometrial cancer guidelines were officially presented in public. This paper describes the key information and latest studies that were presented for the first time at the conference.

scholarly journals Finding the Perfect Match: Different Heavy-Duty Mobile Applications Call for Different Actuators

Proceedings ◽  
2020 ◽  
Vol 64 (1) ◽  
pp. 22
Author(s):  
David Fassbender ◽  
Tatina Minav
Keyword(s):  
Mobile Applications ◽  
State Of The Art ◽  
Perfect Match ◽  
The Novel ◽  
Hydraulic Systems ◽  
Heavy Duty ◽  
Different Types ◽  
Evaluation Algorithm ◽  
Novel Concept ◽  
Valve Control

For the longest time, valve-controlled, centralized hydraulic systems have been the state-of-the-art technology to actuate heavy-duty mobile machine (HDMM) implements. Due to the typically low energy efficiency of those systems, a high number of promising, more-efficient actuator concepts has been proposed by academia as well as industry over the last decades as potential replacements for valve control—e.g., independent metering, displacement control, different types of electro-hydraulic actuators (EHAs), electro-mechanic actuators, or hydraulic transformers. This paper takes a closer look on specific HDMM applications for these actuator concepts to figure out where which novel concept can be a better alternative to conventional actuator concepts, and where novel concepts might fail to improve. For this purpose, a novel evaluation algorithm for actuator–HDMM matches is developed based on problem aspects that can indicate an unsuitable actuator–HDMM match. To demonstrate the functionality of the match evaluation algorithm, four actuator concepts and four HDMM types are analyzed and rated in order to form 16 potential actuator–HDMM matches that can be evaluated by the novel algorithm. The four actuator concepts comprise a conventional valve-controlled concept and three different types of EHAs. The HDMM types are excavator, wheel loader, backhoe, and telehandler. Finally, the evaluation of the 16 matches results in 16 mismatch values, of which the lowest indicates the “perfect match”. Low mismatch values could be found in general for EHAs in combination with most HDMMs but also for a valve-controlled actuator concept in combination with a backhoe. Furthermore, an analysis of the concept limitations with suggestions for improvement is included.

scholarly journals BARcode DEmixing through Non-negative Spatial Regression (BarDensr)

2021 ◽  
Vol 17 (3) ◽  
pp. e1008256
Author(s):  
Shuonan Chen ◽  
Jackson Loper ◽  
Xiaoyin Chen ◽  
Alex Vaughan ◽  
Anthony M. Zador ◽  
...  
Keyword(s):  
State Of The Art ◽  
Spatial Regression ◽  
Image Data ◽  
Real Data ◽  
Optimization Methods ◽  
Spatial Density ◽  
Signal Recovery ◽  
Cloud Platform ◽  
Rna Transcripts ◽  
Different Types

Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply BarDensr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the ‘NeuroCAAS’ cloud platform.

scholarly journals Evaluation of Audio Compression Artifacts

10.14311/906 ◽  
2007 ◽  
Vol 47 (1) ◽  
Author(s):  
M. Herrera Martinez
Keyword(s):  
State Of The Art ◽  
Subjective Evaluation ◽  
Audio Coding ◽  
Coding System ◽  
Audio Compression ◽  
Coding Systems ◽  
Wide Range ◽  
Different Types ◽  
Processing Techniques ◽  
Signal Processing Techniques

This paper deals with subjective evaluation of audio-coding systems. From this evaluation, it is found that, depending on the type of signal and the algorithm of the audio-coding system, different types of audible errors arise. These errors are called coding artifacts. Although three kinds of artifacts are perceivable in the auditory domain, the author proposes that in the coding domain there is only one common cause for the appearance of the artifact, inefficient tracking of transient-stochastic signals. For this purpose, state-of-the art audio coding systems use a wide range of signal processing techniques, including application of the wavelet transform, which is described here. 

Multisensor systems based on phthalocyanines for monitoring the quality of grapes

2016 ◽  
Vol 20 (08n11) ◽  
pp. 889-894 ◽  
Author(s):  
Maria Luz Rodriguez-Mendez ◽  
Celia García-Hernandez ◽  
Cristina Medina-Plaza ◽  
Cristina García-Cabezón ◽  
Jose Antonio de Saja
Keyword(s):  
State Of The Art ◽  
Layer By Layer ◽  
Carbon Paste ◽  
Electron Mediator ◽  
Multisensor Systems ◽  
Langmuir Blodgett ◽  
Different Types ◽  
Production Stages ◽  
Nanostructured Sensors

Arrays of phthalocyanine-based sensors with complementary activity have been used to develop voltammetric electronic tongues. Such systems have demonstrated to be useful in enology for the evaluation of quality of wines in different production stages, from grapes to bottles. In this paper, the state of the art of multisensor systems based on phthalocyanines dedicated to the analysis of musts (juices obtained from crushed grapes) is described. Such multisensor systems cover different types of sensors from simple Carbon Paste Electrodes, to sophiticated nanostructured sensors, including Langmuir–Blodgett or Layer by Layer thin films and biomimetic biosensors where phthalocyanines play a crucial role as electron mediator between enzymes and electrodes. In all cases, multisensor systems based on phthalocyanines have been able to discriminate musts prepared from different varieties of grapes. The performance of these systems can be improved by combining non-specific sensors with biosensors containing enzymes selective to phenols. In this case, excellent relationships have been found between the responses provided by the array and the content in phenols and acids provided by traditional chemical analysis.

scholarly journals A Review on Recent Advancements in FOREX Currency Prediction

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 186 ◽  
Author(s):  
Md. Saiful Islam ◽  
Emam Hossain ◽  
Abdur Rahman ◽  
Mohammad Shahadat Hossain ◽  
Karl Andersson
Keyword(s):  
Selection Criteria ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Optimization Techniques ◽  
Selection Algorithm ◽  
Relevant Research ◽  
Short Term ◽  
Different Types ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from researchers all over the world. Due to its vulnerable characteristics, different types of research have been conducted to accomplish the task of predicting future FOREX currency prices accurately. In this research, we present a comprehensive review of the recent advancements of FOREX currency prediction approaches. Besides, we provide some information about the FOREX market and cryptocurrency market. We wanted to analyze the most recent works in this field and therefore considered only those papers which were published from 2017 to 2019. We used a keyword-based searching technique to filter out popular and relevant research. Moreover, we have applied a selection algorithm to determine which papers to include in this review. Based on our selection criteria, we have reviewed 39 research articles that were published on “Elsevier”, “Springer”, and “IEEE Xplore” that predicted future FOREX prices within the stipulated time. Our research shows that in recent years, researchers have been interested mostly in neural networks models, pattern-based approaches, and optimization techniques. Our review also shows that many deep learning algorithms, such as gated recurrent unit (GRU) and long short term memory (LSTM), have been fully explored and show huge potential in time series prediction.

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