Epilepsy is one of the most common neurological disorders, affecting between 0.4% and 1% of the world's population. While seizures can be controlled in approximately two thirds of newly diagnosed patients through the use of one or more antiepileptic drugs (AEDs), the remainder experience seizures even on multiple medications. The primary impacts of the chronic condition of epilepsy on a patient are a lower quality of life, loss of productivity, comorbidities, and increased risk of death. Epilepsy is an intermittent brain disorder, and in localization-related epilepsy, which is the most common form of epilepsy, one or a few discrete brain areas (the seizure focus or seizure foci) are believed to be responsible for seizure initiation. More recent approaches with implantable electrical stimulation seizure control devices hold value as a therapeutic option for the control of seizures. These devices, directly or indirectly, target the seizure focus and seek to control its expression. In this project we will build a multichannel brain implantable device based on emerging cyber physical system (CPS) principles. This brain implantable CPS device will incorporate key design features to make the device dependable, scalable, composable, certifiable, and interoperable. The device will operate over the life of an animal, or a patient, and continuously record brain activity and stimulate the brain when seizure related activity is detected to abort an impending seizure. Episodic brain disorders such as epilepsy have a considerable impact on a patient's productivity and quality of life and may be life-threatening when seizures cannot be controlled with medications. The goal of this project is to create a second generation brain-implantable sensing and stimulating device (BISSD) based on emerging CPS principles and practice. The development of a BISSD as a exemplifies several defining aspects that inform and illustrate core CPS principles. First, to meet the important challenge of regulatory approval a composable, scalable and certifiable framework that supports testing in multiple species is proposed. Second, a BISSD must be wholly integrated with the patient and fully cognizant at every instant of brain state, including dynamic changes in both the normal and abnormal expression of brain physiology and therapeutic intervention. Thus, this project seeks a tight conjunction of the cyber solution that must monitor itself and monitor and stimulate the brain using implanted, adaptable, distributed, and networked electrodes, and the physical system which in this case is the intermittently failing human brain. Third, a BISSD must function for an extensive period of time, up to the life of the patient, because each surgery to place and retrieve a BISSD carries an attendant risk. This requirement necessitates a dependable solution, which this project seeks to reliably achieve through both an understanding of the brain's foreign body response and a unique hierarchical fault-tolerant design. Fourth, an advanced salient approaches to acquire, compress, and analyze sensor signals to achieve real-time monitoring and control of seizures is employed. This project should yield a powerful, scalable CPS framework for robust fault-tolerant implantable medical devices with real-time processing that can grow with advances in sensors, sensing modalities, time-series analysis, real-time computation, control, materials, power and knowledge of underlying biology. The USA has a competitive advantage in the control of seizures in medically refractory epilepsy. In the modern era, epilepsy surgery evolved in the USA in the 1970s and spread from here to other parts of the world. Similarly, the USA enjoys a competitive advantage in BISSDs, and success in this effort will enable the USA to build on and maintain this advantage. In addition to epilepsy, advances made here can be expected to benefit the treatment of other neurological and psychiatric brain disorders.

Parking can take up a significant amount of the trip costs (time and money) in urban travel. As such, it can considerably influence travelers' choices of modes, locations, and time of travel. The advent of smart sensors, wireless communications, social media and big data analytics offers a unique opportunity to tap parking's influence on travel to make the transportation system more efficient, cleaner, and more resilient. A cyber-physical social system for parking is proposed to realize parking's potential in achieving the above goals. This cyber-physical system consists of smart parking sensors, a parking and traffic data repository, parking management systems, and dynamic traffic flow control. If successful, the results of the investigation will create a new paradigm for managing parking to reduce traffic congestion, emissions and fuel consumption and to enhance system resilience. These results will be disseminated broadly through publications, workshops and seminars. The research will provide interdisciplinary training to both graduate and undergraduate students. The results of this research also fills a void in our graduate transportation curriculum in which parking management gets little coverage. The investigators will organize an online short training course in Coursera and National Highway Institute to bring results to a broader audience. The investigators will also collaborate with Carnegie Museum of Natural History to develop an online digital map and related educational programs, which will be presented in the museum galleries during public events. Technically, new theories, algorithms and systems for efficient management of transportation infrastructure through parking will be developed in this research, leveraging cutting-edge sensing technology, communication technology, big data analytics and feedback control. The research probes massive individualized and infrastructure based traffic and parking data to gain a deeper understanding of travel and parking behavior, and develops a novel reservoir-based network flow model that lays the foundation for modeling the complex interactions between parking and traffic flow in large-scale transportation networks. The theory will be investigated at different levels of granularity to reveal how parking information and pricing mechanisms affect network flow in a competitive market of private and public parking. In addition, this research proposes closed-loop control mechanisms to enhance mobility and sustainability of urban networks. Prices, access and information of publicly owned on-street and off-street parking are dynamically controlled to: a) change day-to-day behavior of all commuters through day-to-day travel experience and/or online information systems; b) change travel behavior of a fraction of adaptive travelers on the fly who are aware of time-of-day parking information and comply to the recommendations; and c) influence the market prices of privately owned parking areas through a competitive parking market.

Children affected by neurological conditions (e.g., Cerebral Palsy, Muscular Atrophy, Spina Bifida and Severe head trauma) often develop significant disabilities including impaired motor control. In many cases, walking becomes a non-functional and exhausting skill that demands the use of the aids or the substitution of function, such as wheelchair. This usually cause these children not to acquire locomotion skills, and consequently to lose their independence. However, it is well understood that bipedal locomotion, an essential human characteristic, ensures the best physiological motor pattern acquisition. For this reason, in children with neurological and neuromuscular diseases, independent walking is a significant rehabilitation goal that must be pursued in a specific temporal window due to the plasticity of central nervous system. In other words, children with neurological conditions have a small window of time to acquire locomotion skills through assisted walking rehearsals. The objective of this research work is to create and experimentally validate a set of technologies that form the framework for the development of adaptive, self-balancing, and modular exoskeleton robotics systems for children with neurological disorders. It is our belief that the exoskeleton (and its associated infrastructure) resulting from this research will offer an effective tool to promote locomotion skill acquisition, and in general health, during a critical period in the early life of children with neurological conditions. This research proposal develops a data-driven human-machine modeling specific to physiological conditions. This creates regression models that predict the user behavior without explicit modeling the complex human musculoskeletal dynamics and motor control mechanism. Additionally this research project formulates a safe adaptive control problem as a model predictive control (MPC) problem. In this method, an optimal input sequence is computed by solving a constrained finite-time optimal control problem where exoskeleton intrusion (input from exoskeleton) is minimized to maximize the user's intent to promote learning. This project further develops a novel approach for stabilizing and preventing fall of the exoskeleton and the child as a whole. This method allows a child wearing an exoskeleton to learn locomotion skills described above with less likelihood of falls. This research project furthermore evaluates the developed technologies in terms of efficiency and efficacy and creates a novel fun game using exoskeleton for children to promote locomotion skills.