Manos Roumeliotis

A Review of Experimental Investigations into Object-Oriented Technology

In recent years there has been a growing interest in empirically investigating object-oriented technology (OOT). Much of this empirical work has been experimental in nature. This paper reviews the published output of such experiments—18 in total—with the twin aims of, first, assessing what has been learnt about OOT and, second, what has been learnt about conducting experimental work. We note that much work has focused upon evaluation of the inheritance mechanism. Whilst such experiments are of some interest, we observe that this may be of less significance to the OOT community than experimenters seem to believe. Instead, OOT workers place more emphasis upon other mechanisms such as composition, components, frameworks, architectural styles and design patterns. This leads us to conclude that the empirical researchers need to ensure that their work keeps pace with technological developments in the fields they aim to investigate.

Real-time wireless multisensory smart surveillance with 3D-HEVC streams for internet-of-things (IoT)

This paper presents the design of a novel, real-time, wireless, multisensory, smart surveillance system with 3D-HEVC features. The proposed high-level system architecture of the surveillance system is analyzed. The advantages of HEVC encoding are presented. Methods for synchronization between multiple streams are presented. Available wireless standards are presented and compared. A network-adaptive transmission protocol for a reliable, real-time, multisensory surveillance system is proposed. Adaptive packet frame grouping (APFG) and adaptive quantization are deployed to maximize the quality-of-experience (QoE). Measurements of the proposed protocol have been shown to provide superior results compared to existing transport protocols.

Robust estimation of rigid-body 3-D motion parameters based on point correspondences

The estimation of rigid-body 3-D motion parameters using point correspondences from a pair of images under perspective projection is, typically, very sensitive to noise. We present a novel robust method combining two approaches: (1) the SVD analysis of a linear operator resulting from the feature points and the displacement vectors and (2) a modified version of the well-known weighted least-squares method proposed by Huber in the context of robust statistics. We give a detailed rank analysis of the involved linear operator and study the effects of noise. We also propose a robust method guided by the structure of this operator, using weighted least squares and data partitioning. The method has been tested on artificial data and on real image sequences showing a remarkable robustness, even in the presence of up to 50% outliers in the data set.

Efficient algorithm for transferring a real-time HEVC stream with haptic data through the internet

It is widely accepted that the growth of Internet and the improvement of Internet’s network conditions helped real-time applications to flourish. The demand for Ultra-High Definition video is constantly increasing. Apart from video and sound, a new kind of real-time data is making its appearance, haptic data. The efficient synchronization of video, audio, and haptic data is a rather challenging effort. The new High-Efficiency Video Coding (HEVC) is quite promising for real-time ultra-high definition video transferring through the Internet. This paper presents related work on High-Efficiency Video Coding. It points out the challenges and the synchronization techniques that have been proposed for synchronizing video and haptic data. Comparative tests between H.264 and HEVC are undertaken. Measurements for the network conditions of the Internet are carried out. The equations for the transferring delay of all the inter-prediction configurations of the HEVC are defined. Finally, it proposes a new efficient algorithm for transferring a real-time HEVC stream with haptic data through the Internet.

Survivability Analysis Using Probabilistic Model Checking: A Study on Wireless Sensor Networks

Survivability of a wireless sensor network (WSN) reflects the ability of the network to fulfill its mission despite the presence of abnormal events, such as failures. Given that sensor networks are receiving increasing attention due to the wide range of their applications, which include the critical areas of health, and military and security, survivability constitutes a key property for their study. This paper proposes a quantitative analysis for survivability evaluation of wireless sensors networks using probabilistic model checking. We define network survivability in line with four measures, namely, the frequency of failures, the data loss, the delay, and the compromised data due to a variety of failures. In particular, three types of failure events are considered, namely, node, link, and attack failures, which are due to power faults, communication faults, and black hole attacks, respectively. Then, we represent networks behavior with continuous-time Markov chains and randomly inject the aforementioned faults and attacks in the network to derive results that quantify the impact of them. Although the proposed study considers and provides results for a WSN architecture, it has the potential of being exploited in different networks with their own specifications.

Robust estimation of rigid body 3-D motion parameters from point correspondences

The estimation of rigid body 3-D motion parameters from perspective views is typically very sensitive to noise and also to the presence of outliers in the measurements. In this paper we present a robust 3-D motion estimation approach based on a previously proposed method using SVD analysis of the measurements matrix. On the introduction of noise and outliers the performance of the old method was seen to deteriorate rapidly. Here the problem is attached by splitting the measurement set into smaller subsets and combining the properties of the resulting submatrices with the properties of the desired solution vector in order to obtain our estimate. The method is very robust and it has been successfully tested in both artificial datasets and real images with up to 50% presence of outliers. In addition, the method is fast and more importantly, the estimate quality is independent of the percentage of outliers.

Total least squares 3-D motion estimation

A new method for estimating 3D motion parameters from point correspondences is presented in this paper. The problem formulation leads to the solution of an overdetermined linear system of equations. The total least squares (TLS) method is found to be the most suitable one for estimating the solution since our model includes noise both in the observation data and in the system matrix. The translation parameters are obtained immediately from the above solution whereas the rotation parameters are estimated from the solution of another TLS problem. Tests of our method on artificial data and on real images show its robustness against Gaussian additive noise and against digitalization noise introduced by finite pixel resolution.

Video scene segmentation using spatial contours and 3-D robust motion estimation

A novel image sequence segmentation method which combines both spatial and temporal information is presented in this paper. The first step is an intensity segmentation scheme based on the edgeflow method. The temporal information is introduced through three-dimensional (3-D) motion estimation parameters. In the second step, regions obtained from the first step are clustered according to their 3-D motion models. In order to reduce the noise sensitivity of the motion estimation process, we introduce a robust method which produces accurate motion parameters and facilitates the correct clustering that follows. This ensures that rigid objects with luminance discontinuities can be segmented correctly. The method has been successfully tested in real imagery and typical examples are presented in this paper.

A pipeline technique for dynamic data transfer on a multiprocessor grid

This paper describes a pipeline technique which is used to redistribute data on a multiprocessor grid during runtime. The main purposes of the algorithm are to minimize the data transfer time, prevent congestion on the ports of the receiving processors, and minimize the number of idle processors. One of the key ideas for this algorithm is the creation of processor classes, firstly introduced by Desprez et al. [IEEE Transactions on Parallel and Distributed Systems 9(2):102 (1998).] Based on the idea of classes, we create the pipeline tasks used to organize the redistribution of data. Our experimental results show that this pipeline technique can significantly reduce the amount of time required to complete a dynamic data transfer task.

Network Adaptive Flow Control Algorithm for Haptic Data Over the Internet–NAFCAH

This paper deals with the transfer of real time haptic data over the Internet. Some interested transport protocols have already been proposed for the transport of real time haptic data. This paper presents the related work on haptic data transferring. A new network adaptive flow control algorithm is proposed. The new algorithm combines most of the known flow control algorithms while taking into account the network conditions οf the Internet and the significant haptic events.