Smart parking solutions for big cities are critically important for reducing traffic congestion and vehicle energy consumption in big cities. Visual-based detection methods have been proposed since their maintenance costs are typically lower. However, visual based detection methods in existing systems are not robust due to varying light intensities, occlusions, and their deployment costs can still be expensive. In this paper, we present a collaborative UAV aided smart parking solution where a small team of low cost UAVs are used to collaboratively identify free parking spots of parking lots within campuses. We design a novel navigation control scheme to allow such UAVs to avoid obstacles and cover the parking lots sufficiently. In addition, we also present a visual detection scheme using the generative adversarial network (GAN) which allows us to predict parking availability without requiring pre-identification of parking spots. Simulation results indicate that our approach show promising results.
In recent years, image recognition related applications has become very popular especially after Google popularized the technology with Google Googles in 2010. Image recognition is used in many new technologies such as self-driving cars, e-commerce e.g. searching and buying similar apparel based on a snapshot. However, several challenges remain if we want highly accurate image recognition anywhere anytime using mobile devices. Traditional object recognition algorithms operate robustly only in controlled environments but exhibit high variation in accuracy for more generic mobile environments. Some key reasons are (i) visual domain shift, e.g., changes in image resolution, lighting, background, viewpoint often causes performance degradation, (ii) hard to predict all objects to be recognized. We recently designed an adaptive mobile object recognition framework (joint research with IBM researchers) which allows deep learning techniques to be used successfully in mobile environments. Together with a Georgia Tech researcher, we also design MOCA, an energy-efficient and privacy-preserving deep processing system for mobile vision-based applications. Our solution decouples the hierarchical neural network layers and splits them across mobile and cloud for energy efficiency. MOCA provides adjustable privacy settings using a DNN-based privacy quantification framework to meet the privacy requirements of different mobile applications.
Human action recognition is a major component of many computer vision applications, e.g., video surveillance, patient monitoring, and smart homes. Existing algorithms designed to recognize single-person actions from sequences of 3D joint positions cannot be used to recognize multi-person interactions, and vice versa. This is because the two categories of actions are exclusive by definition. We present a category-blind human action recognition method (CHARM) which is more suitable for realworld scenarios. Compared to the existing action recognition approaches that are designed to cope with only one action category, and are difficult to be extended to scenarios where different action categories co-exist, CHARM achieves comparable or better performance without any prior action categorization. Our work has been published in ICCV 2015. Below is a video demo.
Mobile phones have become increasingly popular and gradually woven into our lives. Smartphones equipped with powerful embedded sensors (e.g., accelerometers, GPS, microphones, and etc.) can be used to monitor multiple dimensions of human behaviors including physical, mental and social behaviors of wellbeing. The collected sensing data can thus be mined not only for the understanding of daily life activities or human behaviors but also for supporting a broad range of mobile healthcare applications. We are designing a smartphone based healthcare monitoring system which monitor users for their mental, cognitive, and physical well-being and hence facilitate early diagnosis of potential illnesses and taking possible preventive measures. One project called SENSCOPS is funded by NSF. For more information about SENSCOPS , click here.
Rapid advancement of the wireless technologies provide new opportunities for mobile users to have easy access to real-time data, derive useful social information, and stay connected with business partners, colleagues and friends. Towards this end, mobile social networking applications have recently emerged to meet these needs. This project aims to build a secure mobile information sharing system (SEMOIS) that supports secure and privacy-preserving real-time information sharing. SEMOIS has the ability to store secure data items with flexible access control at insecure storage nodes and enables users to send context-based messages with late-binding features. Additionally, a set of smart learning methods are developed to extract short-term and long-term geo-social patterns from multimodal sensing data collected by mobile devices for social networking purposes, e.g., geo-social patterns are used to derive hidden communities. For more information on SEMOIS , click here.
A smartgrid contains components that enable userful information to be collected from a power grid such that control centers can estimate the current states of the power grid. Such information is sent via wireless or wired networks. Local-area communication subsystems in a smartgrid tend to employ wireless links for their last mile communication. Unfortunately, various attacks e.g. channel jamming attacks or DoS attacks can be launched in wireless networks. Together with collaborators from Steven Institute of Technology, we designed a communication subsystem with self-healing feature via intelligent local controller switching to combat against jamming attacks. Our design allows the smartgrid to continue to collect smart meter readings under jamming attacks. We also provide guidelines on optimal placement of local controllers to ensure effective smart meter switching. Our work has been published as IEEE CNS 2013 conference paper which won the best runner-up paper award and as a journal paper in IEEE Transaction on Dependable and Secure Computing (TDSC). Recently, we secure DOE funding to work on protecting smartgrid against cyber attacks. Specifically, we will design P2PBotnet detection scheme suitable for smartgrid.
In this project, we build a low-cost interactive assistive robot for special need children. Our initial prototype called LILI was built by several REU students and a MS degree student under the supervision of Prof Spletzer and Prof Chuah. LILI can be controlled by authorized users (via face recognition) using voice commands. LILI can navigate through environments with obstacles. We intend to add more human intelligence to LILI. More information about LILI can be found here.
Several undergraduates work with Prof Chuah via senior design projects or NSF REU grants. One senior design project called "Kinect-for-Kids" is a game using Kinect sensor designed by Jesse Kurtz and Kyle Moore. This game was introduced to children with Autism Spectrum Disorder in an Autisim Respite event held in Dec 6, 2014 at a nearby church in Bethlehem. Chidlren who have played with this game loved it. Additionally, we released several Android applications for special need children. Click here for more details. In Fall 2015, undergraduates Noel Morris and Andrew McMullen designed MemCare (photo, poster) which is a memory game for evaluating cognitive functions, e.g., memory recall of sleep deprived college students or old people.
New wireless technologies allow mobile users to have easy access to real-time data, and stay connected with friends and colleagues. However, emerging mobile applications are data-centric but existing IP oriented communication paradigms are not flexible enough to support this. Additionally, serious security and privacy concerns have also been raised. To fully support emerging mobile applications, we are developing a next generation mobile network called SECON that supports mobile content centric networking features and applications that use such features. For more information on SECON , click here.
The Smart Spaces REU Site will provide motivated computer science and engineering students with a unique research experience centered around the redevelopment of the Mountaintop Campus at Lehigh University. The Smart Spaces REU Site will be an interdisciplinary research environment. Some potential research topics include: 3-D Augmented Reality, Ambient Intelligence, Computer Vision, Embedded Devices, Mobile Computing, Networking and Security, Robotics, and User Interface Design. For more information on SmartSpace, click here.