Addison Clark’s research while affiliated with The University of Texas at Arlington and other places

A wide range of critical infrastructures are connected via wide area networks as well as the Internet-of-Thing (IoT). Apart from natural disasters, these infrastructures, providing services such as electricity, water, gas, and Internet, are vulnerable to terrorist attacks. Clearly, damages to these infrastructures can have dire consequences on economics, health services, security and safety, and various business sectors. An infrastructure network can be represented as a directed graph in which nodes and edges denote operation entities and dependencies between entities, respectively. A knowledgeable attacker who plans to harm the system would aim to use the minimum amount of effort, cost, or resources to yield the maximum amount of damage. Their best strategy would be to attack the most critical nodes of the infrastructure. From the defender’s side, the strategy would be to minimize the potential damage by investing resources in bolstering the security of the critical nodes. Thus, in the struggle between the attacker and defender, it becomes important for both the attacker and defender to identify which nodes are most critically significant to the system. Identifying critical nodes is a complex optimization problem. In this paper, we first present the problem model and then propose a solution for computing the optimal cost attack while considering the failure propagation. The proposed model represents one or multiple interconnected infrastructures. While considering the attack cost of each node, the proposed method computes the optimal attack that a rational attacker would make. Our problem model simulates one of two goals: maximizing the damage for a given attack budget or minimizing the cost for a given amount of damage. Our technique obtains solutions to optimize the objective functions by utilizing integer-linear programming while observing the constraints for each of the specified goals. The paper reports an extensive set of experiments using various graphs. The results show the efficacy of our technique in terms of its ability to obtain solutions with fast turnaround times.

Sustainable development is defined as meeting the needs of the present without compromising the ability of future generations to meet their needs. A sustainable approach aims to maintain a delicate balance between the current resource utilization and future requirements. This implies meeting developmental needs without harming the resources ecosystem. IoT is spearheading the technological transformation of the world and can play a very crucial role in achieving sustainable use of resources. In this chapter, we provide an overview of the challenges faced to achieve a sustainable world and highlight opportunities that the use of IoT offers for achieving sustainable development. We briefly cover sustainability, Sustainable Development Goals (SDGs) set forth by the United Nations, and IoT protocols and architectures. Next, we review various application of IoT in areas related to SDGs. Specifically, salient application areas covered include clean energy, healthcare, smart cities, climate action, food security, and some other selected applications. Finally, we briefly elaborate on a myriad of challenges in the use of IoT for sustainable development.

Human-robot interfaces encompass developing technologies that determine how a user interacts with robot systems. Touchless and nonverbal interfaces are a subset of these in which the user can interact with robots without the use of buttons, speech, or other physical methods. The two driving forces behind the development of these methods are the desire for more natural Human-Robot Interaction (HRI) and the need for more efficacious assistive technologies. Many interface methods inspired by natural human interaction can improve HRIs. These include gaze/eye contact, emotion detection, and gesture recognition. Assistive robot systems can efficiently utilize these methods, as well as other interfaces that are uncommon in human interactions. These includes several unexplored methods such as biopotential signals, the electroencephalogram (EEG) being one example. Research is also underway in exploring the use of involuntary or subconscious signals in human interaction, such as lie detection or behavior analysis. This paper compiles research findings in several of these areas and discusses the benefits, limitations, and potential for future development in several essential touchless and nonverbal human-robot interface methods. This includes a discussion of Internet-of-Things (IoT) devices as they relate to how robot systems can gather information through the use of sensors. The paper also examines cognitive robots and explores possible advancements that can be explored in this area using next-generation mobile networking (5G and 6G) to allow for high data transfer rates, highlighting important information gathered by robots through different interfacing methods. This leads to several new avenues of future research.

Falls are a major health concern among elderly populations. There is a critical need to develop automated systems for assessing a patient's fall risk although the methodologies for determining this risk vary in efficacy, accessibility, and comfort. With advancements in smart home technol-ogy, aging in place and accurate fall risk assessment are no longer mutually exclusive. This paper presents a user friendly fall risk assessment system designed for care providers to non-invasively but continuously monitor their patient's risk of falling. The proposed system employs a pressure sensor-embedded floor - a SmartFloor - installed in the patient's home to monitor trends in gait pa-rameters like gait speed, stride length, and step width. The system should allow care providers to visualize dangerous changes to their patient's gait 24/7 and without disturbing the patient. How-ever, falls are few and far between, making it hard to evaluate how effective fall prediction systems are. To facilitate diagnoses and fall risk assessment, the system also reconstructs a skeletal visu-alization of each recorded walking segment. This is done using a motion similarity algorithm and a database of SmartFloor and Microsoft Kinect data. We tested the accuracy of several variations of the motion similarity algorithm using a small pool of seven participants and the results are presented in this paper.

People with hearing and speaking disabilities face significant hurdles in communication. The knowledge of sign language can help mitigate these hurdles, but most people without disabilities, including relatives, friends, and care providers, cannot understand sign language. The availability of automated tools can allow people with disabilities and those around them to communicate ubiquitously and in a variety of situations with non-signers. There are currently two main approaches for recognizing sign language gestures. The first is a hardware-based approach, involving gloves or other hardware to track hand position and determine gestures. The second is a software-based approach, where a video is taken of the hands and gestures are classified using computer vision techniques. However, some hardware, such as a phone's internal sensor or a device worn on the arm to track muscle data, is less accurate, and wearing them can be cumbersome or uncomfortable. The software-based approach, on the other hand, is dependent on the lighting conditions and on the contrast between the hands and the background. We propose a hybrid approach that takes advantage of low-cost sensory hardware and combines it with a smart sign-recognition algorithm with the goal of developing a more efficient gesture recognition system. The Myo band-based approach using the Support Vector Machine method achieves an accuracy of only 49%. The software-based approach uses the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) methods to train the Myo-based module and achieves an accuracy of over 80% in our experiments. Our method combines the two approaches and shows the potential for improvement. Our experiments are done with a dataset of nine gestures generated from multiple videos, each repeated five times for a total of 45 trials for both the software-based and hardware-based modules. Apart from showing the performance of each approach, our results show that with a more improved hardware module, the accuracy of the combined approach can be significantly improved.