We develop a new Multiple Object Tracking (MOT) scheme for fisheye cameras that can directly perform vehicle detection, re-identification, and tracking under fisheye distortions without explicit dewarping. Fisheye cameras provide omnidirectional coverage that is wider than traditional cameras, reducing fewer need of cameras to monitor road intersections. However, the problem of distorted views introduces new challenges for fisheye MOT. In this paper, we propose a Fish-Eye Multiple Object Tracking (FEMOT) approach with two novelties. We develop the Distorted Fisheye Image Augmentation (DFIA) method to improve object detection and re-identification on fisheye cameras, where fisheye model training can be performed on existing datasets of traditional cameras via fisheye data synthesis and augmentation. We also develop the Hybrid Data Association (HDA) method to perform tracking directly on fisheye views, without the need of de-warping. The developed FEMOT framework provides practical design and advancement that enables large-scale use of fisheye cameras in smart city and surveillance applications.

Traffic congestion is one of the major issues in urban areas, particularly when traffic loads exceed the road’s capacity, resulting in higher petrol consumption and carbon emissions as well as delays and stress for road users. In Asia, the traffic situation can be further deteriorated by road sharing of scooters. How to control the traffic flow to mitigate the congestion has been one of the central issues in transportation research. In this study, we employ a quantum annealing approach to optimize the traffic signals control at a real-life intersection with mixed traffic flows of vehicles and scooters. Considering traffic flow is a continuous and emerging phenomenon, we used quadratic unconstrained binary optimization (QUBO) formalism for traffic optimization, which has a natural equivalence to the Ising model and can be solved efficiently on the quantum annealers, quantum computers or digital annealers. In this article, we first applied the QUBO traffic optimization to artificially generated traffic for a simple intersection, and then we used real-time traffic data to simulate a real "Dongda-Keyuan" intersection with dedicated cars and scooter lanes, as well as mixed scooter and car lanes. We introduced two types of traffic light control systems for traffic optimization: C-QUBO and QUBO. Our rigorous QUBO optimizations show that C-QUBO and QUBO outperform the commonly used fixed cycle method, with QUBO outperforming C-QUBO in some instances. It has been found that QUBO optimization significantly relieves traffic congestion for the unbalanced traffic volume. Furthermore, we found that dynamic changes in traffic light signal duration greatly reduce traffic congestion.

In this paper, we propose a novel data hiding algorithm for high dynamic range (HDR) images encoded by the OpenEXR file format. The proposed algorithm exploits each of three 10-bit mantissa fields as an embedding unit in order to conceal k bits of a secret message using an optimal base which produces the least pixel variation. An aggressive bit encoding and decomposition scheme is recommended, which offers a high probability to convey (k + 1) bits without increasing the pixel variation caused by message concealment. In addition, we present a bit inversion embedding strategy to further increase the capacities when the probability of appearance of secret bit “1” is greater than 0.5. Furthermore, we introduce an adaptive data hiding approach for concealing more secret messages in pixels with low luminance, exploiting the features of the human visual system to achieve luminance-aware adaptive data hiding. The stego HDR images produced by our algorithm coincide with the HDR image file format, causing no suspicion from malicious eavesdroppers. The generated stego HDR images and their tone-mapped low dynamic range (LDR) images reveal no perceptual differences when subjected to quantitative testing by visual difference predictor. Our algorithm can resist steganalytic attacks from the HDR and LDR RS and SPAM steganalyzers. We present the first data hiding algorithm for OpenEXR HDR images offering a high embedding rate and producing high visual quality of the stego images. Our algorithm outperforms the current state-of-the-art works.

In the era of information explosion, the traditional approach to storing data have faced challenges due to the rapid growth of information. It becomes an important issue when we come to processing the large dataset and trying to retrieve the useful information from a big database. This paper presents an effective scheme which employs hybrid cache architecture on the existing database system to enhance the query performance without increasing the hardware cost. Our scheme can be applied to satisfy the traditional database query over the Internet. The experimental results collected from a practical application show that the data query can be speeded up to 26 times. Performance evaluation shows that the response time of queries is less than 0.02 seconds when the query hits the shared memory cache. The proposed architecture can be easily applied to the traditional SQL database to accelerate the database query without significant programing efforts.

Random unique number generator can be used for generating a series of unpredictable and unrepeatable numbers within limited ranges of data and numbers. These numbers are usually distributed equally, random, independent, unpredictable and unrepeatable. A good random number generator has to be effective for a long period and has good statistical distribution and efficient generating performance. This study proposes a computational methodology to generate pseudo-random numbers based on random base polynomial, which uses less memory but generates a great deal of unrepeated pseudo-random numbers. Then this method adopts the multi-thread parallelization to effectively get the benefits of multi-core processors to accelerate the generation of a huge amount of pseudo-random numbers.

Universities are constantly searching for ways that prepare students as effective global professionals. At the same time, cyber infrastructure leverages computing, information, and communication technology to perform research, often in an international context. In this paper we discuss a novel model, called the Cyber infrastructure Internship Program (CIP), which serves both of these goals. Specifically, students apply or develop cyber infrastructure to solve challenging research problems, but they do this via summer internships abroad. CIP has been implemented at three different Universities: The University of California San Diego in the US, Osaka University in Japan and and Monash University in Australia. We discuss details of the schemes and provide some initial evaluations of their success.

Industry widely employs the two-dimensional videoconferencing system as a long distance communication tool, but current limitations such as its tendency to misrepresent eye contact prevent it from becoming more widely adopted. We are exploring the possibility of a three-dimensional videoconferencing system for future interactive streaming of point cloud data, and present the preliminary research results in this paper. We have tested thus far with one sender and one receiver, using pre-recorded data for the sender. The sender, encircled by high-definition cameras, stands and speaks in a room. A cluster of computers reconstructs each frame of the camera images into a 3D point cloud and streams it across a high-speed, low-latency network. On the receiving end, a splat-based renderer employs a new algorithm to efficiently resample the points in real-time, maintaining a user-specified frame rate. Parallel hardware projects onto multiple screens while head tracking equipment records the viewer's movements, allowing the receiver to view a stereoscopic 3D representation of the sender from multiple angles. We can combine these visuals with appropriate use of multiple audio channels to forge an unparalleled virtual experience. This next step towards immersive 3D videoconferencing brings us closer to empowering worldwide collaboration between research departments.