A new method is proposed to increase the accuracy of the state-of-the-art single image super-resolution (SISR) using novel training procedure. The proposed method, named post-trained convolutional neural network (CNN), is carried out stochastic dual simplex algorithm (SDSA) in the last reconstruction layer. The method utilizes contextual information to update the last reconstruction layer of CNN. The extracted contextual information is projected to the last reconstructed layer by optimized weights and the bias is managed through SDSA. Post-trained CNN is applied to the very deep super-resolution (VDSR) method to show its performance. The quantitative and visual results demonstrate that the proposed post-trained VDSR (PTVDSR) exhibits excellent and competitive performance when compared with the VDSR and other super-resolution methods.

Time-Slotted Channel Hopping (TSCH) is a synchronous medium access mode of the IEEE 802.15.4e standard designed for providing low-latency and highly-reliable end-to-end communication. TSCH constructs a communication schedule by combining frequency channel hopping with Time Division Multiple Access (TDMA). In recent years, IETF designed several standards to define general mechanisms for the implementation of TSCH. However, the problem of updating the TSCH schedule according to the changes of the wireless link quality and node's traffic load left unresolved. In this paper, we use non-cooperative game theory to propose GT-TSCH, a distributed TSCH scheduler designed for low-power IoT applications. By considering selfish behavior of nodes in packet forwarding, GT-TSCH updates the TSCH schedule in a distributed approach with low control overhead by monitoring the queue length, the place of the node in the Directed Acyclic Graph (DAG) topology, the quality of the wireless link, and the data packet generation rate. We prove the existence and uniqueness of Nash equilibrium in our game model and we find the optimal number of TSCH Tx timeslots to update the TSCH slotframe. To examine the performance of our contribution, we implement GT-TSCH on Zolertia Firefly IoT motes and the Contiki-NG Operating System (OS). The evaluation results reveal that GT-TSCH improves performance in terms of throughput and end-to-end delay compared to the state-of-the-art method.

Abstract Multi-way data analysis has become an essential tool for capturing underlying structures in higher-order data sets where standard two-way analysis techniques often fail to discover the hidden correlations between variables in multi-way data. We propose a multi-objective variational autoencoder (MO-VAE) method for smart infrastructure damage detection and diagnosis in multi-way sensing data based on the reconstruction probability of autoencoder deep neural network (ADNN). Our method fuses data from multiple sensors in one ADNN at which informative features are being extracted and utilized for damage identification. It generates probabilistic anomaly scores to detect damage, asses its severity and further localize it via a new localization layer introduced in the ADNN. We evaluated our method on multi-way laboratory-based and real-life structural datasets in the area of structural health monitoring for damage diagnosis purposes. The data was collected from our deployed data acquisition system on a cable-stayed bridge in Western Sydney, a reinforced concrete cantilever beam which replicates one of the major structural components on the Sydney Harbour Bridge and a laboratory based building structure obtained from Los Alamos National Laboratory (LANL). Experimental results show that the proposed method can accurately detect structural damage. It was also able to estimate the different levels of damage severity, and capture damage locations in an unsupervised aspect. Compared to the state-of-the-art approaches, our proposed method shows better performance in terms of damage detection and localization.

To accurately predict the regional spread of coronavirus disease 2019 (COVID-19) infection, this study proposes a novel hybrid model, which combines a long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. Several factors and control strategies affect the virus spread, and the uncertainty arising from confounding variables underlying the spread of the COVID-19 infection is substantial. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries at the time of the study. The results show that the proposed model closely replicates the test data, such that not only it provides accurate predictions but it also replicates the daily behavior of the system under uncertainty. The hybrid model outperforms the LSTM model while accounting for data limitation. The parameters of the hybrid models are optimized using a genetic algorithm for each country to improve the prediction power while considering regional properties. Since the proposed model can accurately predict the short-term to medium-term daily spreading of the COVID-19 infection, it is capable of being used for policy assessment, planning, and decision making.

A new heuristic optimization algorithm is presented to solve the nonlinear optimization problems. The proposed algorithm utilizes a stochastic method to achieve the optimal point based on simplex techniques. A dual simplex is distributed stochastically in the search space to find the best optimal point. Simplexes share the best and worst vertices of one another to move better through search space. The proposed algorithm is applied to 25 well-known benchmarks, and its performance is compared with grey wolf optimizer (GWO), particle swarm optimization (PSO), Nelder-Mead simplex algorithm, hybrid GWO combined with pattern search (hGWO-PS), and hybrid GWO algorithm combined with random exploratory search algorithm (hGWO-RES). The numerical results show that the proposed algorithm, called stochastic dual simplex algorithm (SDSA), has a competitive performance in terms of accuracy and complexity.

The online analysis of multi-way data stored in a tensor has become an essential tool for capturing the underlying structures and extracting the sensitive features that can be used to learn a predictive model. However, data distributions often evolve with time and a current predictive model may not be sufficiently representative in the future. Therefore, incrementally updating the tensor-based features and model coefficients are required in such situations. A new efficient tensor-based feature extraction, named Nesterov Stochastic Gradient Descent (NeSGD), is proposed for online (CP) decomposition. According to the new features obtained from the resultant matrices of NeSGD, a new criterion is triggered for the updated process of the online predictive model. Experimental evaluation in the field of structural health monitoring using laboratory-based and real-life structural datasets shows that our methods provide more accurate results compared with existing online tensor analysis and model learning. The results showed that the proposed methods significantly improved the classification error rates, were able to assimilate the changes in the positive data distribution over time, and maintained a high predictive accuracy in all case studies.

We propose a multi-objective autoencoder method for fault detection and diagnosis in multi-way data based on the reconstruction error of autoencoder deep neural network (ADNN). Multi-way data analysis has become an essential tool for capturing underlying structures in higher-order data sets. Our method fuses data from multiple sources in one ADNN at which informative features are being extracted and utilized for anomaly detection. It also uses the generated anomaly scores to asses the severity of the anomalous data and localize it via a localization layer in the autoencoder. We evaluated our method on multi-way datasets in the area of structural health monitoring for damage detection purposes. Experimental results show that the proposed method can accurately detect structural damage. It was also able to estimate the different levels of damage severity, and capture damage locations in an unsupervised aspect. Compared to the state-of-the-art approaches, our proposed method shows better performance in terms of damage detection and localization.

Automated person re-identification is a challenging research problem that has many real-world applications, especially in video surveillance. While many recent studies have been focusing on solving the person re-identification problem using full-scale images or video footages, little work has been done to solve the person re-identification problem in a top-down context. In this work, we propose a solution to the top-down reidentification problem that uses the Siamese architecture in conjunction with Convolutional Neural Networks. In our approach, a pair of top-down images is distinguished by a single Siamese network, which is trained to predict the similarity, or a distance between two input images. Experiments have shown that once the model is properly trained, it is able to achieve one-shot, top-down re-identification by learning unseen classes of person in real-time.

Augmented Reality (AR) is investigated as a unique technology to combine virtual and real world together. The key feature of AR is to demonstrate extra information in the field of view for those who interact with the authentic environment. Using AR in education setting helps students to engage in genuine exploration in the real world. In this regard, remote laboratories provide a special platform for AR technology to bridge the gap between theoretical and empirical knowledge. For revealing auxiliary data, object tracking scheme plays an imperative role to make visible invisible issue. In this scheme, Particle Swarm Optimization algorithm (PSO) is one of the most famous algorithm in linear and non-linear object tracking by particular identification of target. Contrary to other modified PSO, Elevated-PSO is adopted to improve diversity of particles when all particles are restricted close to the best predicted solution with the enough similarity. This proposed algorithm regarding to target tracking in AR remote labs can fulfill better solutions in comparison with classic and other improved PSO.