scholarly journals DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 863
Author(s):  
Jianming Cui ◽  
Wenxiu Kong ◽  
Xiaojun Zhang ◽  
Da Chen ◽  
Qingtian Zeng
Keyword(s):  
Channel Coding ◽  
Network Architecture ◽  
Short Term Memory ◽  
Gaussian Approximation ◽  
Polar Codes ◽  
Performance Loss ◽  
Fifth Generation ◽  
Successive Cancellation ◽  
Significant Performance ◽  
Long Short Term Memory

Polar code has been adopted as the control channel coding scheme for the fifth generation (5G), and the performance of short polar codes is receiving intensive attention. The successive cancellation flipping (SC flipping) algorithm suffers a significant performance loss in short block lengths. To address this issue, we propose a double long short-term memory (DLSTM) neural network to locate the first error bit. To enhance the prediction accuracy of the DLSTM network, all frozen bits are clipped in the output layer. Then, Gaussian approximation is applied to measure the channel reliability and rank the flipping set to choose the least reliable position for multi-bit flipping. To be robust under different codewords, padding and masking strategies aid the network architecture to be compatible with multiple block lengths. Numerical results indicate that the error-correction performance of the proposed algorithm is competitive with that of the CA-SCL algorithm. It has better performance than the machine learning-based multi-bit flipping SC (ML-MSCF) decoder and the dynamic SC flipping (DSCF) decoder for short polar codes.

scholarly journals An overview of channel coding for 5G NR cellular communications

2019 ◽  
Vol 8 ◽  
Author(s):  
Jung Hyun Bae ◽  
Ahmed Abotabl ◽  
Hsien-Ping Lin ◽  
Kee-Bong Song ◽  
Jungwon Lee
Keyword(s):  
Channel Coding ◽  
Ldpc Codes ◽  
Low Latency ◽  
Polar Codes ◽  
Coding Scheme ◽  
Coding Schemes ◽  
Successive Cancellation ◽  
New Radio ◽  
Key Characteristics ◽  
User Data

AbstractA 5G new radio cellular system is characterized by three main usage scenarios of enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC), and massive machine type communications, which require improved throughput, latency, and reliability compared with a 4G system. This overview paper discusses key characteristics of 5G channel coding schemes which are mainly designed for the eMBB scenario as well as for partial support of the URLLC scenario focusing on low latency. Two capacity-achieving channel coding schemes of low-density parity-check (LDPC) codes and polar codes have been adopted for 5G where the former is for user data and the latter is for control information. As a coding scheme for data, 5G LDPC codes are designed to support high throughput, a variable code rate and length and hybrid automatic repeat request in addition to good error correcting capability. 5G polar codes, as a coding scheme for control, are designed to perform well with short block length while addressing a latency issue of successive cancellation decoding.

scholarly journals Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5611 ◽  
Author(s):  
Mihail Burduja ◽  
Radu Tudor Ionescu ◽  
Nicolae Verga
Keyword(s):  
Neural Network ◽  
Intracranial Hemorrhage ◽  
Network Architecture ◽  
Short Term Memory ◽  
Ct Scans ◽  
Short Term ◽  
Term Memory ◽  
Deep Model ◽  
Long Short Term Memory ◽  
Ct Slices

In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) that takes as input individual CT slices, and a Long Short-Term Memory (LSTM) network that takes as input multiple feature embeddings provided by the CNN. For efficient processing, we consider various feature selection methods to produce a subset of useful CNN features for the LSTM. Furthermore, we reduce the CT slices by a factor of 2×, which enables us to train the model faster. Even if our model is designed to balance speed and accuracy, we report a weighted mean log loss of 0.04989 on the final test set, which places us in the top 30 ranking (2%) from a total of 1345 participants. While our computing infrastructure does not allow it, processing CT slices at their original scale is likely to improve performance. In order to enable others to reproduce our results, we provide our code as open source. After the challenge, we conducted a subjective intracranial hemorrhage detection assessment by radiologists, indicating that the performance of our deep model is on par with that of doctors specialized in reading CT scans. Another contribution of our work is to integrate Grad-CAM visualizations in our system, providing useful explanations for its predictions. We therefore consider our system as a viable option when a fast diagnosis or a second opinion on intracranial hemorrhage detection are needed.

scholarly journals Application of deep learning methods to predict ionosphere parameters in real time

2020 ◽  
Vol 196 ◽  
pp. 02007
Author(s):  
Vladimir Mochalov ◽  
Anastasia Mochalova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Short Term Memory ◽  
Neural Network Architecture ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

scholarly journals Improving the Learning Power of Artificial Intelligence Using Multimodal Deep Learning

2021 ◽  
Vol 248 ◽  
pp. 01017
Author(s):  
Eugene Yu. Shchetinin ◽  
Leonid Sevastianov
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Short Term ◽  
Security Systems ◽  
Term Memory ◽  
Human Voice ◽  
Long Short Term Memory ◽  
Effective Analysis

Computer paralinguistic analysis is widely used in security systems, biometric research, call centers and banks. Paralinguistic models estimate different physical properties of voice, such as pitch, intensity, formants and harmonics to classify emotions. The main goal is to find such features that would be robust to outliers and will retain variety of human voice properties at the same time. Moreover, the model used must be able to estimate features on a time scale for an effective analysis of voice variability. In this paper a paralinguistic model based on Bidirectional Long Short-Term Memory (BLSTM) neural network is described, which was trained for vocal-based emotion recognition. The main advantage of this network architecture is that each module of the network consists of several interconnected layers, providing the ability to recognize flexible long-term dependencies in data, which is important in context of vocal analysis. We explain the architecture of a bidirectional neural network model, its main advantages over regular neural networks and compare experimental results of BLSTM network with other models.

scholarly journals Echo State Networks and Long Short-Term Memory for Continuous Gesture Recognition: a Comparative Study

2020 ◽  
Author(s):  
Doreen Jirak ◽  
Stephan Tietz ◽  
Hassan Ali ◽  
Stefan Wermter
Keyword(s):  
Comparative Study ◽  
Real Time ◽  
Gesture Recognition ◽  
Network Architecture ◽  
Short Term Memory ◽  
Medical Rehabilitation ◽  
Measurement Unit ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract Recent developments of sensors that allow tracking of human movements and gestures enable rapid progress of applications in domains like medical rehabilitation or robotic control. Especially the inertial measurement unit (IMU) is an excellent device for real-time scenarios as it rapidly delivers data input. Therefore, a computational model must be able to learn gesture sequences in a fast yet robust way. We recently introduced an echo state network (ESN) framework for continuous gesture recognition (Tietz et al., 2019) including novel approaches for gesture spotting, i.e., the automatic detection of the start and end phase of a gesture. Although our results showed good classification performance, we identified significant factors which also negatively impact the performance like subgestures and gesture variability. To address these issues, we include experiments with Long Short-Term Memory (LSTM) networks, which is a state-of-the-art model for sequence processing, to compare the obtained results with our framework and to evaluate their robustness regarding pitfalls in the recognition process. In this study, we analyze the two conceptually different approaches processing continuous, variable-length gesture sequences, which shows interesting results comparing the distinct gesture accomplishments. In addition, our results demonstrate that our ESN framework achieves comparably good performance as the LSTM network but has significantly lower training times. We conclude from the present work that ESNs are viable models for continuous gesture recognition delivering reasonable performance for applications requiring real-time performance as in robotic or rehabilitation tasks. From our discussion of this comparative study, we suggest prospective improvements on both the experimental and network architecture level.

CCG supertagging with bidirectional long short-term memory networks

2017 ◽  
Vol 24 (1) ◽  
pp. 77-90 ◽  
Author(s):  
REKIA KADARI ◽  
YU ZHANG ◽  
WEINAN ZHANG ◽  
TING LIU
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
State Of The Art ◽  
Input Sequence ◽  
Entity Recognition ◽  
Short Term ◽  
Term Memory ◽  
Pos Tagging ◽  
Long Short Term Memory

AbstractNeural Network-based approaches have recently produced good performances in Natural language tasks, such as Supertagging. In the supertagging task, a Supertag (Lexical category) is assigned to each word in an input sequence. Combinatory Categorial Grammar Supertagging is a more challenging problem than various sequence-tagging problems, such as part-of-speech (POS) tagging and named entity recognition due to the large number of the lexical categories. Specifically, simple Recurrent Neural Network (RNN) has shown to significantly outperform the previous state-of-the-art feed-forward neural networks. On the other hand, it is well known that Recurrent Networks fail to learn long dependencies. In this paper, we introduce a new neural network architecture based on backward and Bidirectional Long Short-Term Memory (BLSTM) Networks that has the ability to memorize information for long dependencies and benefit from both past and future information. State-of-the-art methods focus on previous information, whereas BLSTM has access to information in both previous and future directions. Our main findings are that bidirectional networks outperform unidirectional ones, and Long Short-Term Memory (LSTM) networks are more precise and successful than both unidirectional and bidirectional standard RNNs. Experiment results reveal the effectiveness of our proposed method on both in-domain and out-of-domain datasets. Experiments show improvements about (1.2 per cent) over standard RNN.

scholarly journals Deep spatiotemporal models for robust proprioceptive terrain classification

2017 ◽  
Vol 36 (13-14) ◽  
pp. 1521-1539 ◽  
Author(s):  
Abhinav Valada ◽  
Wolfram Burgard
Keyword(s):  
Real World ◽  
Network Architecture ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Terrain Classification ◽  
Short Term ◽  
Term Memory ◽  
Training Scheme ◽  
Long Short Term Memory ◽  
Spatiotemporal Models

Terrain classification is a critical component of any autonomous mobile robot system operating in unknown real-world environments. Over the years, several proprioceptive terrain classification techniques have been introduced to increase robustness or act as a fallback for traditional vision based approaches. However, they lack widespread adaptation due to various factors that include inadequate accuracy, robustness and slow run-times. In this paper, we use vehicle-terrain interaction sounds as a proprioceptive modality and propose a deep long-short term memory based recurrent model that captures both the spatial and temporal dynamics of such a problem, thereby overcoming these past limitations. Our model consists of a new convolution neural network architecture that learns deep spatial features, complemented with long-short term memory units that learn complex temporal dynamics. Experiments on two extensive datasets collected with different microphones on various indoor and outdoor terrains demonstrate state-of-the-art performance compared to existing techniques. We additionally evaluate the performance in adverse acoustic conditions with high-ambient noise and propose a noise-aware training scheme that enables learning of more generalizable models that are essential for robust real-world deployments.

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