Control barrier function (CBF) based Quadratic Programs (QPs) were introduced in early 2014 as a means to guarantee safety in affine control systems in conjunction with stability/tracking. However, due to the presence of model-based terms, they fail to provide guarantees under model perturbations. Therefore, in this paper, we propose a new class of CBFs for robotic systems that augment kinetic energy with the traditional forms. We show that with torque limits permitting, and with the kinematic models accurately known, forward invariance of safe sets generated by kinematic constraints (position and velocity) can be guaranteed. The proposed methodology is motivated by the control Lyapunov function (CLF) based QPs that use the kinetic energy function. By the property of CBF-QPs, we show that the pointwise min-norm control laws obtained are feasible and Lipschitz continuous, and can be derived analytically via the KKT conditions. In order to include stability with safety, we also augment CLF based constraints in the CBF-QPs to realize a unified control law that allows tracking with safety irrespective of the inertial parameters of the robot. We will demonstrate the robustness of this class of CBF-QPs in two robotic platforms: a 1-DOF and a 2-DOF manipulator, by scaling the masses by up to 100, and then simulating the resulting dynamics.

We present a novel graph convolutional method called star topology convolution (STC). This method makes graph convolution more similar to conventional convolutional neural networks (CNNs) in Euclidean feature spaces. STC learns subgraphs which have a star topology rather than learning a fixed graph like most spectral methods. Due to the properties of a star topology, STC is graph-scale free (without a fixed graph size constraint). It has fewer parameters in its convolutional filter and is inductive, so it is more flexible and can be applied to large and evolving graphs. The convolutional filter is learnable and localized, similar to CNNs in Euclidean feature spaces, and can share weights across graphs. To test the method, STC was compared with state-of-the-art graph convolutional methods in a supervised learning setting on nine node properties prediction benchmark datasets: Cora, Citeseer, Pubmed, PPI, Arxiv, MAG, ACM, DBLP, and IMDB. The experimental results showed that STC achieved state-of-the-art performance on all these datasets and maintained good robustness. In an essential protein identification task, STC outperformed state-of-the-art essential protein identification methods. An application of using pretrained STC as the embedding for feature extraction of some downstream classification tasks was introduced. The experimental results showed that STC can share weights across different graphs and be used as the embedding to improve the performance of downstream tasks.

Transmission of video traffic over the Internet has grown exponentially in the past few years with no sign of waning. This increasing demand for video services has changed user expectation of quality. Various mechanisms have been proposed to optimise the Quality of Experience (QoE) of end users’ video. Studying these approaches are necessary for new methods to be proposed or combination of existing ones to be tailored. We discuss challenges facing the optimisation of QoE for video traffic in this paper. It surveys and classifies these mechanisms based on their functions. The limitation of each of them is identified and future directions are highlighted.

The popularity of the video services on the Internet has evolved various mechanisms that target the Quality of Experience (QoE) optimization of video traffic. The video quality has been enhanced through adapting the sending bitrates. However, rate adaptation alone is not sufficient for maintaining a good video QoE when congestion occurs. This paper presents a cross-layer architecture for video streaming that is QoE-aware. It combines adaptation capabilities of video applications and QoE-aware admission control to optimize the trade-off relationship between QoE and the number of admitted sessions. Simulation results showed the efficiency of the proposed architecture in terms of QoE and number of sessions compared to two other architectures (adaptive architecture and non-adaptive architecture ).

Modulated model predictive control (M2PC) has recently emerged as a possible solution for control in starter generator systems in the more electric aircraft (MEA), due to its advantages of fixed switching frequency, fast response and good performance. However, conventional M2PC requires the prediction of each possible output voltage vector, which involves a heavy computational burden for the processor, especially for multilevel converters. This is an obstacle for practical industrial applications. To solve this problem this paper introduces a new, low-complexity modulated model predictive control (LC-M2PC) for a starter generator control system with a neutral point clamped (NPC) converter. The proposed LC-M2PC only needs prediction action once in each control interval, which can reduce the computational burden of processor. Fixed switching frequency is maintained and it can achieve a lower total harmonic distortion (THD) current than conventional M2PC, using space vector modulation (SVM). This proposed LC-M2PC method is validated on a prototype electrical starter generator (ESG) system test rig with three-level NPC converter. Experimental results verify the effectiveness of the proposed method.

For wireless power transfer and energy harvesting applications, an advantage of having a broadband rectifier is the coverage of a wide range of narrowband frequencies as well as wideband pulses. Many broadband rectifiers have been recently proposed, but their reported characteristics are mainly evaluated at discrete frequencies. Due to the nonlinear nature of rectifiers, such frequency domain characteristics may not directly correspond to the time-domain, especially when input signals are wideband pulses. Here report the performance evaluation of a broadband rectifier on wideband pulses with various pulse parameters. We use our recently proposed broadband rectifier as a test device. Using Gaussian-modulated RF pulses with various pulse parameters, the rectification performance is verified via simulation and measurement. The results demonstrate sufficient rectification efficiency even for short pulses under appropriate parameters, where a maximum rectification efficiency of 71 % is obtained.

Since the initial reports of the Coronavirus surfacing in Wuhan, China; the novel virus currently without a cure has spread like a wildfire across the globe. The virus spread exponentially across all inhabited continent; catching local governments by surprise in many cases and bringing the world economy to a standstill. As local authorities work on a response to deal with the virus, the scientific community has stepped in to help analyse and predict the pattern and conditions that would influence the spread of this unforgiving virus. Using existing statistical modelling tools to latest AI technology; the scientific community has used public and privately available data to help with predictions. A lot of this data research has enabled local authorities to plan their response – whether that is to deploy tightly available medical resources like ventilators or how and when to enforce policies to social distance including lockdowns. On one hand, this paper shows what accuracy of research brings to enable fighting this disease; while on the other hand it also shows what lack of response from local authorities can do in spreading this virus. This is our attempt in compiling different research methods and comparing their accuracy in predicting the spread of COVID-19.

The quadratic assignment problem (QAP) is one of the hardest NPhard problems and problems with a dimension of 20 or more can be difficult to solve using exact methods. The QAP has a set of facilities and a set of locations. The goal is to assign each facility to a location such that the product of the flow between pairs of facilities and the distance between them are minimized. Sometimes there is also a cost associated with assigning a facility to a location. In this work, I solve the QAP using a population based iterative local search with open source code in C++. Results show that the code is able to solve all nug instances to optimality, thereby proving that the algorithm is capable of solving larger problems for which optimum solutions are not known.

Manual visual inspection, typically performed by certified inspectors, is still the main form of road pothole detection. This process is, however, not only tedious, time-consuming and costly, but also dangerous for the inspectors. Furthermore, the road pothole detection results are always subjective, because they depend entirely on the inspector’s experience. In this paper, we first introduce a disparity (or inverse depth) image processing module, named quasi inverse perspective transformation (QIPT), which can make the damaged road areas become highly distinguishable. Then, we propose a novel attention aggregation (AA) framework, which can improve the semantic segmentation networks for better road pothole detection, by taking the advantages of different types of attention modules. Moreover, we develop a novel training set augmentation technique based on adversarial domain adaptation, where synthetic road RGB images and transformed road disparity (or inverse depth) images are generated to enhance the training of semantic segmentation networks. The experimental results illustrate that, firstly, the disparity (or inverse depth) images transformed by our QIPT module become more informative; secondly, the adversarial domain adaptation can not only significantly improve the performance of the state-of-the-art semantic segmentation networks, but also accelerate their convergence. In addition, AA-UNet and AA-RTFNet, our best performing implementations, respectively outperform all other state-of-the-art single-modal and data-fusion networks for road pothole detection.

The proposed system provides a smart and energy efficient means of waste segregation.

The COVID-19 pandemic has accelerated methods to facilitate contactless evaluation of patients in hospital settings. By minimizing unnecessary in-person contact with individuals who may have COVID-19 disease, healthcare workers (HCW) can prevent disease transmission, and conserve personal protective equipment. Obtaining vital signs is a ubiquitous task that is commonly done in-person. To eliminate the need for in-person contact for vital signs measurement in the hospital setting, we developed Dr. Spot, an agile quadruped robotic system that comprises a set of contactless monitoring systems for measuring vital signs and a tablet computer to enable face-to-face medical interviewing. Dr. Spot is teleoperated by trained clinical staff to facilitate enhanced telemedicine. Specifically, it has the potential to simultaneously measure skin temperature, respiratory rate, heart rate, and blood oxygen saturation simultaneously while maintaining social distancing from the patients. This is important because fluctuations in vital sign parameters are commonly used in algorithmic decisions to admit or discharge individuals with COVID-19 disease. Here, we deployed Dr. Spot in a hospital setting with the ability to measure the vital signs from healthy volunteers from which the measurements of elevated skin temperature screening, respiratory rate, heart rate, and SpO2 were carefully verified with ground-truth sensors.

Low-power wide area (LPWA) technologies are strongly recommended as the underlying networks for Internet of Things (IoT) applications. They offer attractive features, including wide-range coverage, long battery life, and low data rates. This paper reviews the current trends in this technology, with an emphasis on the services it provides and the challenges it faces. The industrial paradigms for LPWA implementation are presented. Compared with other work in the field, this paper focuses on the need for integration among different LPWA technologies and recommends the appropriate LPWA solutions for a wide range of IoT application and service use cases. Opportunities created by these technologies in the market are also analyzed. The latest research efforts to investigate and improve the operation of LPWA networks are also compared and classified to enable researchers to quickly get up to speed on the current status of this technology. Finally, challenges facing LPWA are identified and directions for future research are recommended.

Consumer behavior in retail stores gives rise to product graphs based on copurchasing or co-viewing behavior. These product graphs can be analyzed using the known methods of graph analysis. In this paper, we analyze the product graph at Target Corporation based on the Erd˝os-Renyi random graph model. In particular, we compute clustering coefficients of actual and random graphs, and we find that the clustering coefficients of actual graphs are much higher than random graphs. We conduct the analysis on the entire set of products and also on a per category basis and find interesting results. We also compute the degree distribution and we find that the degree distribution is a power law as expected from real world networks, contrasting with the ER random graph.

A clique in a graph is a set of vertices that are all connected to each other. A maximum clique is a clique of maximum size. A graph may have more than one maximum cliques. The problem of finding a maximum clique is a strongly hard NP-hard problem. It is not possible to find an approximation algorithm which finds a maximum clique that is a constant factor of the optimum solution. In this work, we present a genetic algorithm for the maximum clique problem that is able to find optimum or close to optimum solutions to most DIMACS graphs. The genetic algorithm uses new crossover mechanisms which are able to find reasonably good cliques which can then be used in other applications downstream. We also provide C++ code for our algorithm. Results show that our algorithm is able to find maximum cliques for most DIMACS instances, and if not, close to optimum solutions for the other instances.

A clique in a graph is a set of vertices that are all directly connected to each other i.e. a complete sub-graph. A clique of the largest size is called a maximum clique. Finding the maximum clique in a graph is an NP-hard problem and it cannot be solved by an approximation algorithm that returns a solution within a constant factor of the optimum. In this work, we present a simple and very fast randomized algorithm for the maximum clique problem. We also provide Java code of the algorithm in our git repository. Results show that the algorithm is able to find reasonably good solutions to some randomly chosen DIMACS benchmark graphs. Rather than aiming for optimality, we aim to find good solutions very fast.

Introduced to electrical power networks at the turn of the century, the use of power electronic devices to control and regulate single-phase networks has trailed behind. A likely reason for this outcome is the relative cost of ‘smart devices’ in a system of low earned revenue. Now a feature of grid modernisation projects, interest in ‘smart devices’ as a means to extend the useful life of distribution assets, delay capital expenditure, lower operating costs and to improve the supply reliability, is growing. Described in this paper for the control of single-phase transformers and reactors is a concept ‘smart switch’ that uses a low voltage low power thyristor. Built with a novel magnetic core and winding arrangement, the disconnection and reconnection of a transformer or reactor is controlled by the semiconductor switch. The concept design is demonstrated in this paper for a thermal overload and switched shunt reactor transformer applications.

The internet of smart devices is a network of intelligent gadgets with sensors, programs, Wi-Fi and communication network connections. These devices store the data in cloud and process data outside the device using the proposed Cloud-Internet communication framework. These devices can communicate with other devices using the proposed framework. However, there are many challenges for communication security among the internet of smart devices. The Cloud can store the device data with security, reliability, privacy and service availability. The communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud computing. Our purpose of this study is to create a framework for providing the communication security among smart devices network for the internet of things using cloud computing. Our main contribution links a new study for providing communication security for the internet of smart devices using the cloud-Internet framework. This study can be helpful for communication security problem in the framework of the Internet of Things. The proposed study generates a new framework for solving the issue of communication security among internet of smart devices.

In the next generation of computing, Mobile ad-hoc network (MANET) will play a very important role in the Internet of Things (IoT). The MANET is a kind of wireless networks that are self-organizing and auto connected in a decentralized system. Every device in MANET can be moved freely from one location to another in any direction. They can create a network with their neighbors’ smart devices and forward data to another device. The IoT-Cloud-MANET framework of smart devices is composed of IoT, cloud computing, and MANET. This framework can access and deliver cloud services to the MANET users through their smart devices in the IoT framework where all computations, data handling, and resource management are performed. The smart devices can move from one location to another within the range of the MANET network. Various MANETs can connect to the same cloud, they can use cloud service in a real time. For connecting the smart device of MANET to cloud needs integration with mobile apps. My main contribution in this research links a new methodology for providing secure communication on the internet of smart devices using MANET Concept in 5G. The research methodology uses the correct and efficient simulation of the desired study and can be implemented in a framework of the Internet of Things in 5G.