Deep Neural Network-based System Research Articles

IoT-based trusted wireless communication framework by machine learning approach

The traditional Radio-Frequency Systems (RFS) authentication methods, designed to ensure secure data transmission on the web, may not always effectively prevent adversaries from gaining access to concealed IDs or asymmetric cryptography through infiltrative, side-channel, training, and computer attacks. In contrast, Unaccounted Information (UAI) has the potential to exploit irregularities in production systems to automatically identify microchips, offering a highly robust and cost-effective security solution. This approach introduces RFS-UAI, a deep neural network-based system that efficiently manages wireless node identification by leveraging synthetic RFS characteristics of remote controls (Tx) learned through supervised methods in Wireless Sensor Networks (WSN). Unlike traditional methods that require the development of specialized transistors for UAI or semantic segmentation, this approach utilizes the existing asymmetrical RFS communication networks. Similar to the way the human brain processes information, Rx handles the entire device identification process at the gateway. According to test results, which include assessing process capability at a specified 65 nm threshold voltage and characteristics such as Local Oscillator (LO) misalignment and I-Q disparity using a probabilistic model with 52 hidden units, the system can distinguish up to 4800 transmitters with a remarkable 99.9 % accuracy under various channel conditions, all without the need for regular preambles. This recommended method can serve as a standalone security measure or be integrated into a biometric identification system.

Deep Neural Network-Based System Identification for Nonlinear MPC: Enhancements for Massive Multi-Output Systems and Experimental Validation with 1D Camera Image Outputs*

In this research, we investigate a system identification method based on deep neural networks for nonlinear Model Predictive Control (MPC), focusing on efficiently managing massive multi-output systems. This method involves the direct synthesis of state estimators and output predictors represented by neural networks from experimental data. The integration of these components with the Levenberg-Marquardt optimization method, coupled with the use of automatic differentiation, enables efficient realization of nonlinear MPC.In this research, we propose a specific architecture for the state estimator and output predictor, designed to suit multi-output systems. This approach is applied to a miniature four-wheeled vehicle equipped with a 1D camera, which generates 160-pixel image outputs. The experimental application to this test vehicle demonstrates the method's capability in effectively managing complex, multi-output systems.

Recognizing Command Words using Deep Recurrent Neural Network for Both Acoustic and Throat Speech

The importance of speech command recognition in a human-machine interaction system is increased in recent years. In this study, we propose a deep neural network-based system for acoustic and throat command speech recognition. We apply a preprocessed pipeline to create the input of the deep learning model. Firstly, speech commands are decomposed into components using well-known signal decomposition techniques. The Mel-frequency cepstral coefficients (MFCC) feature extraction method is applied to each component of the speech commands to obtain the feature inputs for the recognition system. At this stage, we apply and compare performance using different speech decomposition techniques such as wavelet packet decomposition (WPD), continuous wavelet transform (CWT), and empirical mode decomposition (EMD) in order to find out the best technique for our model. We observe that WPD shows the best performance in terms of classification accuracy. This paper investigates long short-term memory (LSTM)-based recurrent neural network (RNN), which is trained using the extracted MFCC features. The proposed neural network is trained and tested using acoustic speech commands. Moreover, we also train and test the proposed model using a throat mic. speech commands as well. Lastly, the transfer learning technique is employed to increase the test accuracy for throat speech recognition. The weights of the model train with the acoustic signal are used to initialize the model used for throat speech recognition. Overall, we have found significant classification accuracy for both acoustic and throat command speech. We obtain LSTM is much better than the GMM-HMM model, convolutional neural networks such as CNN-tpool2 and residual networks such as res15 and res26 with an accuracy score of over 97% on Google’s Speech Commands dataset and we achieve 95.35% accuracy on our throat speech data set using the transfer learning technique.

Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning.

Deep neural network-based systems are now state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness. Small perturbations to sensor inputs (from noise or adversarial examples) are often enough to change network-based decisions, which was recently shown to cause an autonomous vehicle to swerve into another lane. In light of these dangers, numerous algorithms have been developed as defensive mechanisms from these adversarial inputs, some of which provide formal robustness guarantees or certificates. This work leverages research on certified adversarial robustness to develop an online certifiably robust for deep reinforcement learning algorithms. The proposed defense computes guaranteed lower bounds on state-action values during execution to identify and choose a robust action under a worst case deviation in input space due to possible adversaries or noise. Moreover, the resulting policy comes with a certificate of solution quality, even though the true state and optimal action are unknown to the certifier due to the perturbations. The approach is demonstrated on a deep Q-network (DQN) policy and is shown to increase robustness to noise and adversaries in pedestrian collision avoidance scenarios, a classic control task, and Atari Pong. This article extends our prior work with new performance guarantees, extensions to other reinforcement learning algorithms, expanded results aggregated across more scenarios, an extension into scenarios with adversarial behavior, comparisons with a more computationally expensive method, and visualizations that provide intuition about the robustness algorithm.

PerAnSel: A Novel Deep Neural Network-Based System for Persian Question Answering.

Question answering (QA) systems have attracted considerable attention in recent years. They receive the user's questions in natural language and respond to them with precise answers. Most of the works on QA were initially proposed for the English language, but some research studies have recently been performed on non-English languages. Answer selection (AS) is a critical component in QA systems. To the best of our knowledge, there is no research on AS for the Persian language. Persian is a (1) free word order, (2) right-to-left, (3) morphologically rich, and (4) low-resource language. Deep learning (DL) techniques have shown promising accuracy in AS. Although DL performs very well on QA, it requires a considerable amount of annotated data for training. Many annotated datasets have been built for the AS task; most of them are exclusively in English. In order to address the need for a high-quality AS dataset in the Persian language, we present PASD; the first large-scale native AS dataset for the Persian language. To show the quality of PASD, we employed it to train state-of-the-art QA systems. We also present PerAnSel: a novel deep neural network-based system for Persian question answering. Since the Persian language is a free word-order language, in PerAnSel, we parallelize a sequential method and a transformer-based method to handle various orders in the Persian language. We then evaluate PerAnSel on three datasets: PASD, PerCQA, and WikiFA. The experimental results indicate strong performance on the Persian datasets beating state-of-the-art answer selection methods by 10.66% on PASD, 8.42% on PerCQA, and 3.08% on WikiFA datasets in terms of MRR.

A Novel deep neural network-based emotion analysis system for automatic detection of mild cognitive impairment in the elderly

A significant number of people are suffering from cognitive impairment all over the world. Early detection of cognitive impairment is of great importance to both patients and caregivers. However, existing approaches have their shortages, such as time consumption and financial expenses involved in clinics and the neuroimaging stage. It has been found that patients with cognitive impairment show abnormal emotion patterns. In this paper, we present a novel deep neural network-based system to detect the cognitive impairment through the analysis of the evolution of facial emotions while participants are watching designed video stimuli. In our proposed system, a novel facial expression recognition algorithm is developed using layers from MobileNet and Support Vector Machine (SVM), which showed satisfactory performance in 3 datasets. To verify the proposed system in detecting cognitive impairment, 61 elderly people including patients with cognitive impairment and healthy people as a control group have been invited to participate in the experiments and a dataset was built accordingly. With this dataset, the proposed system has successfully achieved the detection accuracy of 73.3%.

Backdoor Attacks to Deep Neural Network-Based System for COVID-19 Detection from Chest X-ray Images

Open-source deep neural networks (DNNs) for medical imaging are significant in emergent situations, such as during the pandemic of the 2019 novel coronavirus disease (COVID-19), since they accelerate the development of high-performance DNN-based systems. However, adversarial attacks are not negligible during open-source development. Since DNNs are used as computer-aided systems for COVID-19 screening from radiography images, we investigated the vulnerability of the COVID-Net model, a representative open-source DNN for COVID-19 detection from chest X-ray images to backdoor attacks that modify DNN models and cause their misclassification when a specific trigger input is added. The results showed that backdoors for both non-targeted attacks, for which DNNs classify inputs into incorrect labels, and targeted attacks, for which DNNs classify inputs into a specific target class, could be established in the COVID-Net model using a small trigger and small fraction of training data. Moreover, the backdoors were effective for models fine-tuned from the backdoored COVID-Net models, although the performance of non-targeted attacks was limited. This indicated that backdoored models could be spread via fine-tuning (thereby becoming a significant security threat). The findings showed that emphasis is required on open-source development and practical applications of DNNs for COVID-19 detection.

SSSCPreds: Deep Neural Network-Based Software for the Prediction of Conformational Variability and Application to SARS-CoV-2.

Amino acid mutations that improve protein stability and rigidity can accompany increases in binding affinity. Therefore, conserved amino acids located on a protein surface may be successfully targeted by antibodies. The quantitative deep mutational scanning approach is an excellent technique to understand viral evolution, and the obtained data can be utilized to develop a vaccine. However, the application of the approach to all of the proteins in general is difficult in terms of cost. To address this need, we report the construction of a deep neural network-based program for sequence-based prediction of supersecondary structure codes (SSSCs), called SSSCPrediction (SSSCPred). Further, to predict conformational flexibility or rigidity in proteins, a comparison program called SSSCPreds that consists of three deep neural network-based prediction systems (SSSCPred, SSSCPred100, and SSSCPred200) has also been developed. Using our algorithms we calculated here shows the degree of flexibility for the receptor-binding motif of SARS-CoV-2 spike protein and the rigidity of the unique motif (SSSC: SSSHSSHHHH) at the S2 subunit and has a value independent of the X-ray and Cryo-EM structures. The fact that the sequence flexibility/rigidity map of SARS-CoV-2 RBD resembles the sequence-to-phenotype maps of ACE2-binding affinity and expression, which were experimentally obtained by deep mutational scanning, suggests that the identical SSSC sequences among the ones predicted by three deep neural network-based systems correlate well with the sequences with both lower ACE2-binding affinity and lower expression. The combined analysis of predicted and observed SSSCs with keyword-tagged datasets would be helpful in understanding the structural correlation to the examined system.

Development and clinical application of deep learning model for lung nodules screening on CT images

Lung cancer screening based on low-dose CT (LDCT) has now been widely applied because of its effectiveness and ease of performance. Radiologists who evaluate a large LDCT screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. It requires an efficient method for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness. Many novel deep neural network-based systems have demonstrated the potential for use in the proposed technique to detect lung nodules. However, the effectiveness of clinical practice has not been fully recognized or proven. Therefore, the aim of this study to develop and assess a deep learning (DL) algorithm in identifying pulmonary nodules (PNs) on LDCT and investigate the prevalence of the PNs in China. Radiologists and algorithm performance were assessed using the FROC score, ROC-AUC, and average time consumption. Agreement between the reference standard and the DL algorithm in detecting positive nodules was assessed per-study by Bland–Altman analysis. The Lung Nodule Analysis (LUNA) public database was used as the external test. The prevalence of NCPNs was investigated as well as other detailed information regarding the number of pulmonary nodules, their location, and characteristics, as interpreted by two radiologists.

Deep Neural Network-Based System for Autonomous Navigation in Paddy Field

This paper presents a novel vision based approach for detecting rows of crop in paddy field. The precise detection of crop row enables a farm-tractor to autonomously navigate the field for successful inter-row weeding. While prior works on crop row detection rely primarily on various image based features, a deep neural network based approach for learning semantic graphics to directly extract the crop rows from an input image is used in this work. A deep convolutional encoder decoder network is trained to detect the crop lines using semantic graphics. The detected crop lines are then used to derive control signal for steering the tractor autonomously in the field. The results demonstrate that the proposed method is able to detect the rows of paddy accurately and enable the tractor to navigate autonomously along the crop rows even with a simple proportional only controller.