Driving Speed Estimation and Trapped Drivers’ Detection inside Tunnels Using Distributed MIMO Bluetooth Devices

Accidents in highway tunnels involving trucks carrying flammable cargoes can be dangerous, needing immediate confrontation to detect and safely evacuate the trapped people to lead them to the safety exits. Unfortunately, existing sensing technologies fail to detect and track trapped persons or moving vehicles inside tunnels in such an environment. This paper presents a distributed Bluetooth system architecture that uses detection equipment following a MIMO approach. The proposed equipment uses two long-range Bluetooth and one BLE transponder to locate vehicles and trapped people in motorway tunnels. Moreover, the detector’s parts and distributed architecture are analytically described, along with interfacing with the authors’ resources management system implementation. Furthermore, the authors also propose a speed detection process, based on classifier training, using RSSI input and speed calculations from the tunnel inductive loops as output, instead of the Friis equation with Kalman filtering steps. The proposed detector was experimentally placed at the Votonosi tunnel of the EGNATIA motorway in Greece, and its detection functionality was validated. Finally, the detector classification process accuracy is evaluated using feedback from the existing tunnel inductive loop detectors. According to the evaluation process, classifiers based on decision trees or random forests achieve the highest accuracy.

Comparison of Empirical Mode Decomposition, Wavelets, and Different Machine Learning Approaches for Patient-Specific Seizure Detection Using Signal-Derived Empirical Dictionary Approach

Analysis of long-term multichannel EEG signals for automatic seizure detection is an active area of research that has seen application of methods from different domains of signal processing and machine learning. The majority of approaches developed in this context consist of extraction of hand-crafted features that are used to train a classifier for eventual seizure detection. Approaches that are data-driven, do not use hand-crafted features, and use small amounts of patients' historical EEG data for classifier training are few in number. The approach presented in this paper falls in the latter category, and is based on a signal-derived empirical dictionary approach, which utilizes empirical mode decomposition (EMD) and discrete wavelet transform (DWT) based dictionaries learned using a framework inspired by traditional methods of dictionary learning. Three features associated with traditional dictionary learning approaches, namely projection coefficients, coefficient vector and reconstruction error, are extracted from both EMD and DWT based dictionaries for automated seizure detection. This is the first time these features have been applied for automatic seizure detection using an empirical dictionary approach. Small amounts of patients' historical multi-channel EEG data are used for classifier training, and multiple classifiers are used for seizure detection using newer data. In addition, the seizure detection results are validated using 5-fold cross-validation to rule out any bias in the results. The CHB-MIT benchmark database containing long-term EEG recordings of pediatric patients is used for validation of the approach, and seizure detection performance comparable to the state-of-the-art is obtained. Seizure detection is performed using five classifiers, thereby allowing a comparison of the dictionary approaches, features extracted, and classifiers used. The best seizure detection performance is obtained using EMD based dictionary and reconstruction error feature and support vector machine classifier, with accuracy, sensitivity and specificity values of 88.2, 90.3, and 88.1%, respectively. Comparison is also made with other recent studies using the same database. The methodology presented in this paper is shown to be computationally efficient and robust for patient-specific automatic seizure detection. A data-driven methodology utilizing a small amount of patients' historical data is hence demonstrated as a practical solution for automatic seizure detection.

Development of Software for Control of Virtual Objects Using Event-Related Potential P300

The article considers the principle of operation of brain-computer interfaces (BCI) and a method for detecting the focus of a person's attention using event-related potential (P300). The review of the existing hardware and software solutions for the implementation of BCIs was performed including the identification of their advantages and disadvantages. The program that allows you to choose the desired stimulus from a variety of presented was developed.An electroencephalograph of the BiTronics Lab company on the Arduino platform was used to receive the signal. Signal filtering, classifier training and visualization are implemented in Python.

Recognition and Positioning of Container Lock Holes for Intelligent Handling Terminal Based on Convolutional Neural Network

Container handling is a key link in container transport. In an automated handling terminal, the work efficiency directly depends on the time cost of the alignment between the spreader and the lock holes of the container. This paper attempts to improve the recognition and location of container lock holes with the aid of machine vision. Firstly, a lock hole recognition algorithm was designed based on local binary pattern (LBP) feature and classifier. After feature extraction and classifier training, multi-scale sliding window was used to recognize each lock hole. To realize real-time, accurate recognition of lock holes, the convolutional neural network (CNN) with improved threshold was incorporated to our algorithm. The tests on actual datasets show that our algorithm can effectively locate container lock holes.

COMPARISON OF CLASSIFIER TRAINING METHODS FOR IMAGE RECOGNITION

В работе рассматриваются различные методы распознавания изображений. Представлено их сравнение и выбора оптимального. This paper discusses various methods of image recognition. Their comparison and selection of the optimal one are presented.

Privacy-Preserving Blockchain-Based Nonlinear SVM Classifier Training for Social Networks

With the development of social networks, there are more and more social data produced, which usually contain valuable knowledge that can be utilized in many fields, such as commodity recommendation and sentimental analysis. The SVM classifier, as one of the most prevailing machine learning techniques for classification, is a crucial tool for social data analysis. Since training a high-quality SVM classifier usually requires a huge amount of data, it is a better choice for individuals and small enterprises to conduct collaborative training with multiple parties. Nevertheless, it causes privacy risks when sharing sensitive data with untrusted people and enterprises. Existing solutions mainly adopt the computation-intensive cryptographic methods which are not efficient for practical applications. Therefore, it is an urgent and challenging task to realize efficient SVM classifier training while protecting privacy. In this paper, we propose a novel privacy-preserving nonlinear SVM classifier training scheme based on blockchain. We first design a series of secure computation protocols which can achieve secure nonlinear SVM classifier training with minimal computation overheads. Then, leveraging these building blocks, we propose a blockchain-based secure nonlinear SVM classifier training scheme that realizes collaborative training while protecting privacy. We conduct a thorough analysis of the security properties of our scheme. Experiments over a real dataset show that our scheme achieves high accuracy and practical efficiency.

Exploring Significant Characteristics and Models for Classification of Structure Function of Academic Documents

With the increasing abundance of literature resources, how to acquire knowledge elements efficiently and accurately is the key to achieving accurate literature retrieval and utilization of available literature resources. The identification of the structure function of academic documents is a fundamental work to meet the above requirements. In this study, the proceedings of the Association for Computational Linguistics (ACL) conferences are used as the primitive corpus, and the training corpus of chapter category is obtained by manual annotation. Based on the chapter titles and the in-chapter texts, traditional machine learning and deep learning models are both used for classifier training. Our results show that the title of a chapter is more beneficial to the identification of the structure function of academic documents than the in-chapter texts. The highest F1 value in our experiments is 0.9249, which is obtained on the traditional logistic regression (LR) and support vector machine (SVM) models (slightly higher than on the convolutional neural network [CNN]). And through the experiment of adding other chapter characteristics based on the traditional model, we find that combining the relative position of chapters can effectively improve the classification performance. Finally, this study compares the results of experimental groups with different methods, analyzes the misclassification of the structure function of academic documents, and points out the main direction to improve the classification performance in the future.