scholarly journals A Deep Learning Framework for Signal Detection and Modulation Classification

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4042 ◽  
Author(s):  
Xiong Zha ◽  
Hua Peng ◽  
Xin Qin ◽  
Guang Li ◽  
Sihan Yang
Keyword(s):  
Deep Learning ◽  
Signal Detection ◽  
Communication Systems ◽  
Center Frequency ◽  
Great Success ◽  
Single Shot ◽  
Modulation Classification ◽  
Modulation Recognition ◽  
Modulation Format ◽  
Learning Framework

Deep learning (DL) is a powerful technique which has achieved great success in many applications. However, its usage in communication systems has not been well explored. This paper investigates algorithms for multi-signals detection and modulation classification, which are significant in many communication systems. In this work, a DL framework for multi-signals detection and modulation recognition is proposed. Compared to some existing methods, the signal modulation format, center frequency, and start-stop time can be obtained from the proposed scheme. Furthermore, two types of networks are built: (1) Single shot multibox detector (SSD) networks for signal detection and (2) multi-inputs convolutional neural networks (CNNs) for modulation recognition. Additionally, the importance of signal representation to different tasks is investigated. Experimental results demonstrate that the DL framework is capable of detecting and recognizing signals. And compared to the traditional methods and other deep network techniques, the current built DL framework can achieve better performance.

scholarly journals ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 268
Author(s):  
Yeganeh Jalali ◽  
Mansoor Fateh ◽  
Mohsen Rezvani ◽  
Vahid Abolghasemi ◽  
Mohammad Hossein Anisi
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Great Success ◽  
Morphological Operations ◽  
Ct Image ◽  
Feature Maps ◽  
Learning Framework ◽  
Lung Ct

Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing similar image densities in the pulmonary structures, different types of scanners, and scanning protocols. Most of the current semi-automatic segmentation methods rely on human factors therefore it might suffer from lack of accuracy. Another shortcoming of these methods is their high false-positive rate. In recent years, several approaches, based on a deep learning framework, have been effectively applied in medical image segmentation. Among existing deep neural networks, the U-Net has provided great success in this field. In this paper, we propose a deep neural network architecture to perform an automatic lung CT image segmentation process. In the proposed method, several extensive preprocessing techniques are applied to raw CT images. Then, ground truths corresponding to these images are extracted via some morphological operations and manual reforms. Finally, all the prepared images with the corresponding ground truth are fed into a modified U-Net in which the encoder is replaced with a pre-trained ResNet-34 network (referred to as Res BCDU-Net). In the architecture, we employ BConvLSTM (Bidirectional Convolutional Long Short-term Memory)as an advanced integrator module instead of simple traditional concatenators. This is to merge the extracted feature maps of the corresponding contracting path into the previous expansion of the up-convolutional layer. Finally, a densely connected convolutional layer is utilized for the contracting path. The results of our extensive experiments on lung CT images (LIDC-IDRI database) confirm the effectiveness of the proposed method where a dice coefficient index of 97.31% is achieved.

scholarly journals Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 583 ◽  
Author(s):  
Khang Nguyen ◽  
Nhut T. Huynh ◽  
Phat C. Nguyen ◽  
Khanh-Duy Nguyen ◽  
Nguyen D. Vo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Unmanned Aircraft ◽  
Aerial Images ◽  
Great Success ◽  
Single Shot ◽  
Convolutional Networks ◽  
Image Pyramids ◽  
Fully Convolutional Networks ◽  
Wide Range

Unmanned aircraft systems or drones enable us to record or capture many scenes from the bird’s-eye view and they have been fast deployed to a wide range of practical domains, i.e., agriculture, aerial photography, fast delivery and surveillance. Object detection task is one of the core steps in understanding videos collected from the drones. However, this task is very challenging due to the unconstrained viewpoints and low resolution of captured videos. While deep-learning modern object detectors have recently achieved great success in general benchmarks, i.e., PASCAL-VOC and MS-COCO, the robustness of these detectors on aerial images captured by drones is not well studied. In this paper, we present an evaluation of state-of-the-art deep-learning detectors including Faster R-CNN (Faster Regional CNN), RFCN (Region-based Fully Convolutional Networks), SNIPER (Scale Normalization for Image Pyramids with Efficient Resampling), Single-Shot Detector (SSD), YOLO (You Only Look Once), RetinaNet, and CenterNet for the object detection in videos captured by drones. We conduct experiments on VisDrone2019 dataset which contains 96 videos with 39,988 annotated frames and provide insights into efficient object detectors for aerial images.

scholarly journals LightAMC: Lightweight Automatic Modulation Classification via Deep Learning and Compressive Sensing

2020 ◽  
Author(s):  
Yu Wang ◽  
Jie Yang ◽  
Miao Liu ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Compressive Sensing ◽  
Communication Systems ◽  
Scaling Factor ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Performance Loss ◽  
Computational Speed ◽  
Aerial Vehicle ◽  
Large Model

Automatic modulation classification (AMC) is an promising technology for non-cooperative communication systems in both military and civilian scenarios. Recently, deep learning (DL) based AMC methods have been proposed with outstanding performances. However, both high computing cost and large model sizes are the biggest hinders for deployment of the conventional DL based methods, particularly in the application of internet-of-things (IoT) networks and unmanned aerial vehicle (UAV)-aided systems. In this correspondence, a novel DL based lightweight AMC (LightAMC) method is proposed with smaller model sizes and faster computational speed. We first introduce a scaling factor for each neuron in convolutional neural network (CNN) and enforce scaling factors sparsity via compressive sensing. It can give an assist to screen out redundant neurons and then these neurons are pruned. Experimental results show that the proposed LightAMC method can effectively reduce model sizes and accelerate computation with the slight performance loss.

scholarly journals Bi-LSTM based deep learning method for 5G signal detection and channel estimation

2021 ◽  
Vol 5 (4) ◽  
pp. 334-341
Author(s):  
D Venkata Ratnam ◽  
◽  
K Nageswara Rao ◽  
Keyword(s):  
Deep Learning ◽  
Wireless Communication ◽  
Channel Estimation ◽  
Signal Detection ◽  
Communication Systems ◽  
Estimation Method ◽  
Estimation Methods ◽  
Delay Spread ◽  
Learning Method ◽  
The Impact

<abstract> <p>The advanced neural network methods solve significant signal estimation and channel characterization difficulties in the next-generation 5G wireless communication systems. The number of transmitted signal copies received through multiple paths at the receiver leads to delay spread, which intern causes interference in communication. These adverse effects of the interference can be mitigated with the orthogonal frequency division modulation (OFDM) technique. Furthermore, the proper signal detection methods optimal channel estimation enhances the performance of the multicarrier wireless communication system. In this paper, bi-directional long short-term memory (Bi-LSTM) based deep learning method is implemented to estimate the channel in different multipath scenarios. The impact of the pilots and cyclic prefix on the performance of Bi LSTM algorithm is analyzed. It is evident from the symbol-error rate (SER) results that the Bi-LSTM algorithm performs better than the state of art channel estimation methods known as the Minimum Mean Square and Error (MMSE) estimation method.</p> </abstract>

scholarly journals Automated detection of colorectal tumors based on artificial intelligence

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kwang-Sig Lee ◽  
Sang-Hyuk Son ◽  
Sang-Hyun Park ◽  
Eun Sun Kim
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Automated Detection ◽  
Single Shot ◽  
Learning Framework ◽  
Average Accuracy ◽  
Decision Supporting ◽  
Decision Supporting System ◽  
Validation Set ◽  
The Stability

Abstract Background This study developed a diagnostic tool to automatically detect normal, unclear and tumor images from colonoscopy videos using artificial intelligence. Methods For the creation of training and validation sets, 47,555 images in the jpg format were extracted from colonoscopy videos for 24 patients in Korea University Anam Hospital. A gastroenterologist with the clinical experience of 15 years divided the 47,555 images into three classes of Normal (25,895), Unclear (2038) and Tumor (19,622). A single shot detector, a deep learning framework designed for object detection, was trained using the 47,255 images and validated with two sets of 300 images—each validation set included 150 images (50 normal, 50 unclear and 50 tumor cases). Half of the 47,255 images were used for building the model and the other half were used for testing the model. The learning rate of the model was 0.0001 during 250 epochs (training cycles). Results The average accuracy, precision, recall, and F1 score over the category were 0.9067, 0.9744, 0.9067 and 0.9393, respectively. These performance measures had no change with respect to the intersection-over-union threshold (0.45, 0.50, and 0.55). This finding suggests the stability of the model. Conclusion Automated detection of normal, unclear and tumor images from colonoscopy videos is possible by using a deep learning framework. This is expected to provide an invaluable decision supporting system for clinical experts.

scholarly journals Automatic Modulation Classification for MIMO Systems via Deep Learning and Zero-Forcing Equalization

2020 ◽  
Author(s):  
Guan Gui ◽  
Yu Wang ◽  
Yue Yin ◽  
Juan Wang ◽  
Jinlong Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Communication Systems ◽  
Mimo Systems ◽  
Modulation Classification ◽  
Imperfect Csi ◽  
Zero Forcing ◽  
Automatic Modulation Classification ◽  
Simulation Results ◽  
The Impact

Automatic modulation classification (AMC) is one of the most critical technologies for non-cooperative communication systems. Recently, deep learning (DL) based AMC (DL-AMC) methods have attracted significant attention due to their preferable performance. However, the study of most of DL-AMC methods are concentrated in the single-input and single-output (SISO) systems, while there are only a few works on DL-based AMC methods in multiple-input and multiple-output (MIMO) systems. Therefore, we propose in this work a convolutional neural network (CNN) based zero-forcing (ZF) equalization AMC (CNN/ZF-AMC) method for MIMO systems. Simulation results demonstrate that the CNN/ZF-AMC method achieves better performance than the artificial neural network (ANN) with high order cumulants (HOC)-based AMC method under the condition of the perfect channel state information (CSI). Moreover, we also explore the impact of the imperfect CSI on the performance of the CNN/ZF-AMC method. Simulation results demonstrated that the classification performance is not only influenced by the imperfect CSI, but also associated with the number of the transmit and receive antennas.<br>

scholarly journals Deep Learning-based Cooperative Automatic Modulation Classification Method for MIMO Systems

2020 ◽  
Author(s):  
Yu Wang ◽  
Juan Wang ◽  
Jie Yang ◽  
Wei Zhang ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Decision Rules ◽  
Modulation Classification ◽  
Single Input Single Output ◽  
Automatic Modulation Classification ◽  
Single Output ◽  
Input Multiple Output

Automatic modulation classification (AMC) is one of the most essential algorithms to identify the modulation types for the non-cooperative communication systems. Recently, it has been demonstrated that deep learning (DL)-based AMC method effectively works in the single-input single-output (SISO) systems, but DL-based AMC method is scarcely explored in the multiple-input multiple-output (MIMO) systems. In this correspondence, we propose a convolutional neural network (CNN)-based cooperative AMC (Co-AMC) method for the MIMO systems, where the receiver equipped with multiple antennas cooperatively recognizes the modulation types. Specifically, each received antenna gives their recognition sub-results via the CNN, respectively. Then, the decision maker identifies the modulation types with the recognition sub-results and cooperative decision rules, such as direct voting (DV), weighty voting (WV), direct averaging (DA) and weighty averaging (WA). The simulation results demonstrate that the Co-AMC method, based on the CNN and WA, has the highest correct classification probability in the four cooperative decision rules. In addition, the CNN-based Co-AMC method also performs better than the high order cumulants (HOC)-based traditional AMC methods, which shows the effective feature extraction and powerful classification capabilities of the CNN.

scholarly journals Automatic Modulation Classification Method for Multiple Antenna System Based on Convolutional Neural Network

2020 ◽  
Author(s):  
Juan Wang ◽  
Yu Wang ◽  
Wenmei Li ◽  
Guan Gui ◽  
Fumiyuki Adachi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Communication Systems ◽  
Antenna System ◽  
Arithmetic Mean ◽  
Classification Method ◽  
Modulation Classification ◽  
Multiple Antenna ◽  
Modulation Recognition ◽  
Communication Signals ◽  
Multiple Antenna System

<div>In order to transmit communication signals of</div><div>different properties, quickly, effectively, and accurately, various</div><div>different modulation styles can be adopted. Accurate recognition</div><div>of signal modulation is required at the receive side. Automatic</div><div>modulation recognition (AMR) is a key technique to identify</div><div>various styles of modulation of signals received in wireless</div><div>channels. It can be used in many kinds of communication systems,</div><div>including single antenna system and multiple antenna system. In</div><div>this paper, we propose a convolutional neural networks (CNN)</div><div>aided AMR method for multiple antenna system. Compared with</div><div>the high order cumulants (HOC) and artificial neural networks</div><div>(ANN) aided traditional AMR classification method, both with</div><div>two specific combination strategies, such as relative majority</div><div>voting method and arithmetic mean method, the proposed</div><div>AMR with arithmetic mean method has the best classification</div><div>performance. The experimental results obtained verify that the</div><div>CNN, one of the representative algorithms of deep learning, has</div><div>a strong ability to exploit dominant features and classify the</div><div>modulation styles.</div>

scholarly journals A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2526 ◽  
Author(s):  
Chuan Lin ◽  
Qing Chang ◽  
Xianxu Li
Keyword(s):  
Deep Learning ◽  
Signal Detection ◽  
Power Allocation ◽  
Interference Cancellation ◽  
Communication Systems ◽  
High Efficiency ◽  
Optimal Solution ◽  
Optimal Order ◽  
Learning Method ◽  
Allocation Factor

As a key candidate technique for fifth-generation (5G) mobile communication systems, non-orthogonal multiple access (NOMA) has attracted considerable attention in the field of wireless communication. Successive interference cancellation (SIC) is the main NOMA detection method applied at receivers for both uplink and downlink NOMA transmissions. However, SIC is limited by the receiver complex and error propagation problems. Toward this end, we explore a high-performance, high-efficiency tool—deep learning (DL). In this paper, we propose a learning method that automatically analyzes the channel state information (CSI) of the communication system and detects the original transmit sequences. In contrast to existing SIC schemes, which must search for the optimal order of the channel gain and remove the signal with higher power allocation factor while detecting a signal with a lower power allocation factor, the proposed deep learning method can combine the channel estimation process with recovery of the desired signal suffering from channel distortion and multiuser signal superposition. Extensive performance simulations were conducted for the proposed MIMO-NOMA-DL system, and the results were compared with those of the conventional SIC method. According to our simulation results, the deep learning method can successfully address channel impairment and achieve good detection performance. In contrast to implementing well-designed detection algorithms, MIMO-NOMA-DL searches for the optimal solution via a neural network (NN). Consequently, deep learning is a powerful and effective tool for NOMA signal detection.

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