Single Shot Radar Target Detection and Localization using Deep Neural Network

In this paper, alternative direction finding methods have been proposed using deep learning techniques. Firstly, Regeression and Classification models have created by using Convolutional Neural Networks (CNNs). In the second Convolutional Neural Networks and Recurrent Neural Networks (RNNs) have been utilized in the proposed methods. Despite having vast amount of direction finding studies, utilization of neural networks is scarce in literature and past works mostly only includes usage of CNNs. In this study, direction finding is performed by learning signals reaching multiple antenna arrays by networks. Created neural networks have been fed with different data formats and their performances against noisy and no-noise data have been shown. In addition, comparative analysis of the developed models were made in the similar Signal-to-Noise Ratio (SNR) range with the subspace based MUSIC algorithm, which is frequently used in direction finding.

The study presents the results of the development and testing of deep learning neural network architectures, which demonstrate high accuracy rates in classifying neurophysiological data, in particular hemodynamic brain activation patterns obtained by functional near-infrared spectroscopy, during solving mathematical problems on spatial-numerical associations. The analyzed signal represents a multidimensional time series of oxyhemoglobin and deoxyhemoglobin dynamics. Taking the specificity of the fNIRS signal into account, a comparative analysis of 2 types of neural network architectures was carried out: (1) architectures based on recurrent neural networks: recurrent neural network with long short-term memory, recurrent neural network with long short-term memory with fully connected layers, bidirectional recurrent neural network with long short-term memory, convolutional recurrent neural network with long short-term memory; (2) architectures based on convolutional neural networks with 1D convolutions: convolutional neural network, fully convolutional neural network, residual neural network. Trained long short-term memory recurrent neural network architectures showed worse results in accuracy in comparison with 1D convolutional neural network architectures. Residual neural network (model_Resnet) demonstrated the highest accuracy rates in three experimental conditions more than 88% in detecting age-related differences in brain activation during spatial-numerical association tasks considering the individual characteristics of the respondents’ signal.

A hybrid model based on neural networks for biomedical relation extraction

This paper proposes a novel deep learning architecture involving combinations of Convolutional Neural Networks (CNN) layers and Recurrent neural networks (RNN) layers that can be used to perform segmentation and classification of 5 cardiac rhythms based on ECG recordings. The algorithm is developed in a sequence to sequence setting where the input is a sequence of five second ECG signal sliding windows and the output is a sequence of cardiac rhythm labels. The novel architecture processes as input both the spectrograms of the ECG signal as well as the heartbeats' signal waveform. Additionally, we are able to train the model in the presence of label noise. The model's performance and generalizability is verified on an external database different from the one we used to train. Experimental result shows this approach can achieve an average F1 scores of 0.89 (averaged across 5 classes). The proposed model also achieves comparable classification performance to existing state-of-the-art approach with considerably less number of training parameters.

The presented paper proposes a new speech command recognition model for novel engineering applications with limited resources. We built the proposed model with the help of a Convolutional Recurrent Neural Network (CRNN). The use of CRNN instead of Convolutional Neural Network (CNN) helps us to reduce the model parameters and memory requirement as per resource constraints. Furthermore, we insert transmute and curtailment layer between the layers of CRNN. By doing this we further reduce model parameters and float number of operations to half of the CRNN requirement. The proposed model is tested on Google’s speech command dataset. The obtained result shows that the proposed CRNN model requires 1/3 parameters as compared to the CNN model. The number of parameters of the CRNN model is further reduced by 45% and the float numbers of operations between 2% to 12 % in different recognition tasks. The recognition accuracy of the proposed model is 96% on Google’s speech command dataset, and on laboratory recording, its recognition accuracy is 89%.

In existing convolutional neural networks (CNNs), both convolution and pooling are locally performed for image regions separately, no contextual dependencies between different image regions have been taken into consideration. Such dependencies represent useful spatial structure information in images. Whereas recurrent neural networks (RNNs) are designed for learning contextual dependencies among sequential data by using the recurrent (feedback) connections. In this work, we propose the convolutional recurrent neural network (C-RNN), which learns the spatial dependencies between image regions to enhance the discriminative power of image representation. The C-RNN is trained in an end-to-end manner from raw pixel images. CNN layers are firstly processed to generate middle level features. RNN layer is then learned to encode spatial dependencies. The C-RNN can learn better image representation, especially for images with obvious spatial contextual dependencies. Our method achieves competitive performance on ILSVRC 2012, SUN 397, and MIT indoor.

Objective. Event-related potential (ERP) sensitivity to faces is predominantly characterized by an N170 peak that has greater amplitude and shorter latency when elicited by human faces than images of other objects. We aimed to develop a computational model of visual ERP generation to study this phenomenon which consisted of a three-dimensional convolutional neural network (CNN) connected to a recurrent neural network (RNN). Approach. The CNN provided image representation learning, complimenting sequence learning of the RNN for modeling visually-evoked potentials. We used open-access data from ERP Compendium of Open Resources and Experiments (40 subjects) to develop the model, generated synthetic images for simulating experiments with a generative adversarial network, then collected additional data (16 subjects) to validate predictions of these simulations. For modeling, visual stimuli presented during ERP experiments were represented as sequences of images (time x pixels). These were provided as inputs to the model. By filtering and pooling over spatial dimensions, the CNN transformed these inputs into sequences of vectors that were passed to the RNN. The ERP waveforms evoked by visual stimuli were provided to the RNN as labels for supervised learning. The whole model was trained end-to-end using data from the open-access dataset to reproduce ERP waveforms evoked by visual events. Main results. Cross-validation model outputs strongly correlated with open-access (r = 0.98) and validation study data (r = 0.78). Open-access and validation study data correlated similarly (r = 0.81). Some aspects of model behavior were consistent with neural recordings while others were not, suggesting promising albeit limited capacity for modeling the neurophysiology of face-sensitive ERP generation. Significance. The approach developed in this work is potentially of significant value for visual neuroscience research, where it may be adapted for multiple contexts to study computational relationships between visual stimuli and evoked neural activity.

In this digital era, there is an exponential growth of text-based content in the electronic world. Data as texts exist in the form of documents, social media posts on Facebook, Twitter, etc., logs, sensor data, and emails. Twitter is a social platform where users express their views on various aspects in a day to day life. Twitter produces over 500 million tweets daily that is 6000 tweets per second. Twitter data is, by definition, very noisy and unstructured in nature. Text classifications based on the machine learning techniques have problems like poor generalization ability and sparsity dimension explosion. Classifiers based on deep learning techniques are implemented to improve accuracy to overcome shortcomings of machine learning techniques and to avoid feature extraction processes and have high prediction accuracy and strong learning ability. In this work, the classification of tweets is performed on Covid-19 dataset by implementing deep learning techniques namely Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Recurrent Convolution Neural Network (RCNN), Recurrent Neural Network with Long Short Term Memory (RNN+LSTM), and Bidirectional Long Short Term Memory with Attention (BI-LSTM + Attention). The algorithms are implemented using two-word embedding techniques namely Global Vectors for Word Representation (GloVe) and Word2Vec. RNN with Bidirectional LSTM model has performed better than all the classifiers considered. It has classified the text with an accuracy of 93% and above when used with GloVe and Word2Vec.

ABSTRACTIn this paper, the convolutional recurrent neural network (ConvLSTM) architecture is proposed to predict individual stock prices. The characteristics of stock data are automatically extracted through convolutional neural network (CNN). Afterward, the extracted features are inputted into a long short-term memory (LSTM) model with memory characteristics for prediction. Since CNN has been a representation learning model, it is quite appropriate for automatic feature extraction. Besides, the LSTM architecture of the recurrent neural network could effectively surmount the issues of gradient disappearance and expansion of the time series data. Ten stock historical data had been collected in the experimental data set. Furthermore, many commonly used technical indicators are calculated in advance for the expansion of the dimension of the training samples. The experimental results obtain a 3.449 RMSE (root-mean-square error) in average.

Following recent advancements in deep learning and artificial intelligence, spoken language identification applications are playing an increasingly significant role in our day-to-day lives, especially in the domain of multi-lingual speech recognition. In this article, we propose a spoken language identification system that depends on the sequence of feature vectors. The proposed system uses a hybrid Convolutional Recurrent Neural Network (CRNN), which combines a Convolutional Neural Network (CNN) with a Recurrent Neural Network (RNN) network, for spoken language identification on seven languages, including Arabic, chosen from subsets of the Mozilla Common Voice (MCV) corpus. The proposed system exploits the advantages of both CNN and RNN architectures to construct the CRNN architecture. At the feature extraction stage, it compares the Gammatone Cepstral Coefficient (GTCC) feature and Mel Frequency Cepstral Coefficient (MFCC) feature, as well as a combination of both. Finally, the speech signals were represented as frames and used as the input for the CRNN architecture. After conducting experiments, the results of the proposed system indicate higher performance with combined GTCC and MFCC features compared to GTCC or MFCC features used individually. The average accuracy of the proposed system was 92.81% in the best experiment for spoken language identification. Furthermore, the system can learn language-specific patterns in various filter size representations of speech files.

Analysis of spectrum sensing using deep learning algorithms: CNNs and RNNs

Global climate change has become a critical factor influencing marine and coastal safety. According to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), even with global mitigation efforts, the trend of global warming remains irreversible. The increasing intensity of future extreme climate events is expected to affect wave dynamics, storm surges, and coastal hazards. This study aims to assess long-term variations in extreme waves and storm surges around Taiwan under climate change scenarios and to establish design parameters that reflect future conditions for coastal hazard mitigation and engineering applications.Atmospheric data from the EC-Earth3 global climate model under the high-emission scenario (SSP5-8.5) were used to drive wave and ocean models simulating wave fields and storm surge residuals over the Northwest Pacific. To address the coarse resolution of global climate models, an artificial intelligence–based statistical downscaling framework was developed. This approach integrates Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) into a Convolutional Recurrent Neural Network (CRNN) architecture, improving spatial and temporal resolution and generating data representative of coastal-scale processes. The downscaled wave and surge results were analyzed using the Generalized Pareto Distribution (GPD) for extreme-value statistics to estimate design wave heights and storm surge residuals corresponding to return periods of 10, 25, 50, 75, 100, and 200 years. Statistical uncertainties were evaluated using a bootstrap resampling method.Results show that under the high-emission scenario, significant wave heights and storm surge residuals exhibit an increasing trend with longer return periods. Wave analysis reveals marked changes in northern coastal waters, where design wave heights increase from 3.05 m to 5.65 m. The eastern coast shows moderate increases (4.08–4.22 m), while the Taiwan Strait and southern waters remain relatively stable at about 3.6 m and 5.2 m, indicating higher sensitivity of the north to extreme forcing. For storm surges, historical maximum residuals ranged from 0.36 m to 1.49 m, while future projections range from 0.31 m to 1.35 m, showing a slight decrease but similar spatial distribution, with larger deviations along western and island coasts. Design storm surge residuals increase with return period, from about 0.20 m for a 10-year event to 0.32 m for a 200-year event. Under future conditions, increases are projected mainly for western and island coasts, with southern and eastern shores also showing gradual rises.Overall, extreme waves and storm surges around Taiwan exhibit long-term variations under climate change. Although short-term fluctuations remain moderate, both wave and surge intensities increase at longer return periods, implying that future coastal design standards should consider higher thresholds. The AI-based downscaling and extreme-value framework established in this study supports quantitative assessment of coastal hazards, engineering design, and adaptation planning in Taiwan.

Sound-event recognition often utilizes time-frequency analysis to produce an image-like spectrogram that provides a rich visual representation of original signal in time and frequency. Convolutional Neural Networks (CNN) with the ability of learning discriminative spectrogram patterns are suitable for sound-event recognition. However, there is relatively little effort that CNN makes full use of the important temporal information. In this paper, we propose MCRNN, a Convolutional Recurrent Neural Networks (CRNN) architecture for sound-event recognition, the letter “M” in the name “MCRNN” of our model denotes the multi-sized convolution filters. Richer features are extracted by using several different convolution filter sizes at the last convolution layer. In addition, cochleagram images are used as the input layer of the network, instead of the traditional spectrogram image of a sound signal. Experiments on the RWCP dataset shows that the recognition rate of the proposed method achieved 98.4% in clean conditions, and it robustly outperforms the existing methods, the recognition rate increased by 0.9%, 1.9% and 10.3% in 20 dB, 10 dB and 0 dB signal-to-noise ratios (SNR), respectively.

Deep learning performs a great role in solving many problems, particularly with image classification. Still, the performance of many architectures is often limited at some points, especially with regular architecture. In this paper, we propose an efficient and noncomplex image classification architecture using deep learning based on the most popular algorithms: Convolutional Neural Network (CNN) for Feature extraction, Stacked AutoEncoder (SAE) for reducing the dimensionality, and Recurrent Neural Network (RNN) for increasing the accuracy. The proposed network, called Convolutional Stacked AutoEncoder Recurrent Neural Network (CSAERNet), that takes the input to be features-extracted by CNN then put as input to SAE for dimensionality reduction after that the RNN takes it as input to increase the accuracy. The proposed CSAERNet has been evaluated on two widely-used benchmark datasets; CIFAR-10 and CIFAR-100. The obtained results are compared with the latest state-of-the-art approaches, the results showed that the proposed CSAERNet perform well on both datasets and outperforms on other models.

To develop a natural language processing system that identifies relations of medications with adverse drug events from clinical narratives. This project is part of the 2018 n2c2 challenge. We developed a novel clinical named entity recognition method based on an recurrent convolutional neural network and compared it to a recurrent neural network implemented using the long-short term memory architecture, explored methods to integrate medical knowledge as embedding layers in neural networks, and investigated 3 machine learning models, including support vector machines, random forests and gradient boosting for relation classification. The performance of our system was evaluated using annotated data and scripts provided by the 2018 n2c2 organizers. Our system was among the top ranked. Our best model submitted during this challenge (based on recurrent neural networks and support vector machines) achieved lenient F1 scores of 0.9287 for concept extraction (ranked third), 0.9459 for relation classification (ranked fourth), and 0.8778 for the end-to-end relation extraction (ranked second). We developed a novel named entity recognition model based on a recurrent convolutional neural network and further investigated gradient boosting for relation classification. The new methods improved the lenient F1 scores of the 3 subtasks to 0.9292, 0.9633, and 0.8880, respectively, which are comparable to the best performance reported in this challenge. This study demonstrated the feasibility of using machine learning methods to extract the relations of medications with adverse drug events from clinical narratives.