Abstract: The advancements in electronics, embedded controllers, smart communicating devices as well as the progress towards a better informed, knowledge based society increase the demand for small size, affordable sensors that allow accurate and reliable data recording, processing, storing and communication. This led to the paradigm known as Internet of Things (IoT) in which Wireless Sensor Nodes are most important elements.

The seminar will present research activities on development of IoT and WSN based system towards managing our health and home in a better way. A holistic view of IoT, its challenges and opportunities will be presented. New IoT enabled sensors for early detection of osteoporosis as well as shared. Recent works on Lumber Interbody Fusion will be presented. Recent work on sensors for Smart city applications will be shared.

Abstract: The rapidly growing elderly population in many parts of the world has started to impact people – emotionally, socially and economically. Current demography, medical and social trends in the U.S. indicate a large population of older adults are at risk of early cognitive impairment and dementia (e.g., Alzheimer’s). This motivates us to design innovative non-invasive technology solutions for cognitive health assessment, care and well-being in people’s own environments of daily life. A significant challenge in such technology-assisted solutions is collecting multi-modal sensing data and their analytics on resource-constrained wearables and IoT devices, in presence of uncertainty and noise. Another challenge is how to translate the extracted knowledge into actionable information for effective use by family members, doctors and caregivers. This talk will present our ongoing research on recognition of complex at-home activities of daily living (ADLs) with smartphones and wearables, and cognitive behavior detection with the help of a smart chair that we designed. For activity recognition, our innovative methodology can detect up to 21 fine-grained at-home activities, as opposed to typical 6-12 activities recognized by existing works. The smart chair based novel framework can detect user’s functional and emotional activities, in addition to static and movement based sedentary postures, thus differentiating between dementia and mild cognitive impairment (MCI). In collaboration with a local hospital, our proposed solutions are tested and validated with pre-clinical data and a large number of real patient studies.

Abstract: The distribution for the sum of lognormal random variables finds applications in many science and engineering disciplines, and it is particularly important for wireless communication engineers. However, the distribution for the simplistic sum of independent lognormal random variables is analytically intractable, and it is more so for a sum of correlated lognormal random variables with non-identical parameters. In 1934, Wilkinson from Bell Telephone Labs first studied this problem in an unpublished work. Since then, various approximations have been proposed in the literature. All these approximations fail to accurately quantify the left tail (or right tail) behavior of the distribution function of a sum of lognormal random variables. In this talk, in the context of diversity receptions over lognormal fading channels, we first present that the left tail distribution of the sum of independent lognormal random variables can be accurately represented by a Marcum Q-function. The proposed analytical result outperforms all existing well-known sum of lognormal approximations. Using a different approach, we then extend the problem to a sum of correlated and non-identically lognormal random variables, and show that its left-tail distribution can again be represented by another Marcum Q-function. Our study reveals a number of new and surprising engineering insights into the transmission characteristics over the lognormal fading channels. For example, for the dual-branch case, we show that the outage performance of negatively correlated lognormal channels is better than that of independent lognormal channels. We also show that under certain parameter conditions, one of the two lognormal channels can contribute no performance gain to the diversity reception systems. This implies that one link can be discarded without causing asymptotic performance loss. These new findings can guide the communication engineers to design better systems for transmission over the lognormal fading channels.

Abstract: Non-orthogonal multiple access (NOMA) is an emerging paradigm for the enabling of massive connectivity in 5G networks and beyond. There are two main types of NOMA systems, i.e., power-domain NOMA (PD-NOMA) and code-domain NOMA (CD-NOMA), in which multiple users are separated by assigning different power levels and different codebooks, respectively. Despite numerous research attempts on these two types of NOMA systems, it is intriguing to ask which one outperforms the other from a practical implementation point of view. New advances on NOMA communications will be introduced, including the recent standardization activities in 3GPP, error rate comparison of different NOMA systems (e.g., sparse code multiple access and dense code multiple access), as well as new applications of NOMA with other disruptive wireless technologies.

This talk will be based on a recent preprint “Sparse or Dense: A Comparative Study of Code-Domain NOMA Systems” at https://arxiv.org/abs/2009.04148.

Abstract: In order to boost resilience against network issues, natural disasters, and unexpected traffic in dense urban areas, the unmanned aerial vehicle (UAV)-assisted wireless communication systems can provide a unique opportunity to cater for such demands in a timely fashion without relying on the overly engineered cellular network. However, for UAV-assisted communication, the mobile node positioning and UAV placement are considered to be a paramount importance due to its on demand deployment, high mobility and desirable line of sight (LOS) links. In this talk, we will discuss various issues related to the UAV-enabled communication such as position optimization, coverage improvement and trajectory design. Moreover, we will talk over integration of non-orthogonal multiple access (NOMA) into UAV-enabled communication system which brings improvement in overall throughput of the system in harsh dynamic environments.

Abstract: Location estimation in an indoor Internet of Things (IoT) environment is a challenging task due to multipath signals and obstacles that cause shadowing and fading effects, and change the received signal power considerably. Most of the existing path loss based localization methods assume only a lognormal shadowing model and ignore small scale fading effects. This talk considers a generic combined lognormal shadowing and Rayleigh fading model for efficient localization of smart devices in an indoor IoT environment. In particular, the maximum likelihood estimate of the location and path loss exponent (PLE), and Cramer Rao Lower Bound (CRLB) are derived. The localization parameters are estimated using a novel adaptive mini-batch gradient ascent method that maximizes the log-likelihood function with an appropriate batch size based on the convergence factor. Hence, the proposed method addresses the challenge of an arbitrary selection of a fixed batch size for a gradient ascent method by utilizing this convergence factor. Performance evaluation by a simulation study and real experiments from an indoor IoT testbed provide a more accurate joint estimation of model parameters and smart device localization.

Abstract: The trillions of interconnected objects and sensors embedded objects, vehicles and humans that collectively build the giant network termed IoT that collects and shares data used for a myriad of applications. This requires them to know their location, which is a challenge in GPS-denied environments, such as most indoor locations, tunnels and urban canyons. Several approaches have been suggested that use smart devices to cooperatively find and communicate their positions in such environments. Localization could be helpful in applications ranging from autonomous vehicles to asset tracking, from supply-chain monitoring to smart cities and real-time mapping. Traditional network localization methods estimate a single value for each geospatial variable, such as the distance between two nodes. Therefore, localization accuracy drops sharply in environments such as multi-path and a limited view of the sky severely degrade GPS and wireless signals. The use of radio link measurements to estimate the object’s location is essential for such scenarios. The primary properties of wireless links applied in radio-sensing are RSS and CSI,that are sometimes referred to as signal fingerprint. Two types of approaches take into account the measured properties for localization such as shadowing or link quality measurement, and reflection or scattering of the link. Hence the methods are also categorized as a vision-based approach (uses shadowing or link quality measurement), and a radar-based approach (uses reflection or scattering of the link).

Abstract: mmWave is a key technology for 5G and beyond 5G cellular networks. mmWave has also been explored for localization and object classification. In this talk, we will explore application of some machine learning techniques for angle estimation and field of view (FoV) enhancement. We utilize range FFT based features for object classification. The proposed techniques are useful for several applications in cost effective and reliable autonomous systems such as ground station traffic monitoring and control systems for both on ground and aerial vehicles.

Abstract: In this talk, an overview of artificial intelligence (AI) and machine learning (ML) techniques for localization systems are given. We will have a look at the basic concepts and characteristics of localization based on radio positioning (e.g., in 5G systems) and sensor fusion, emphasis on utilizing AI and machine learning in complex environments such as indoors and outdoor urban canyons. Some of the successful and promising AI/ML methods and techniques from research and commercial use cases and applications are represented. Finally, the role of machine learning and its recent advances in 6G localization and sensing are envisioned.