Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays

Train rescheduling after a perturbation is a challenging task and is an important concern of the railway industry as delayed trains can lead to large fines, disgruntled customers and loss of revenue. Sometimes not just one delay but several unrelated delays can occur in a short space of time which makes the problem even more challenging. In addition, the problem is a dynamic one that changes over time for, as trains are waiting to be rescheduled at the junction, more timetabled trains will be arriving, which will change the nature of the problem. The aim of this research is to investigate the application of several different ant colony optimization (ACO) algorithms to the problem of a dynamic train delay scenario with multiple delays. The algorithms not only resequence the trains at the junction but also resequence the trains at the stations, which is considered to be a first step towards expanding the problem to consider a larger area of the railway network. The results show that, in this dynamic rescheduling problem, ACO algorithms with a memory cope with dynamic changes better than an ACO algorithm that uses only pheromone evaporation to remove redundant pheromone trails. In addition, it has been shown that if the ant solutions in memory become irreparably infeasible it is possible to replace them with elite immigrants, based on the best-so-far ant, and still obtain a good performance.     

Network-level polymorphic shellcode detection using emulation

Significant progress has been made in recent years towards preventing code injection attacks at the network level. However, as state-of-the-art attack detection technology becomes more prevalent, attackers are likely to evolve, employing techniques such as polymorphism and metamorphism to defeat these defenses. A major outstanding question in security research and engineering is thus whether we can proactively develop the tools needed to contain advanced polymorphic and metamorphic attacks. While recent results have been promising, most of the existing proposals can be defeated using only minor enhancements to the attack vector. In fact, some publicly-available polymorphic shellcode engines are currently one step ahead of the most advanced publicly-documented network-level detectors. In this paper, we present a heuristic detection method that scans network traffic streams for the presence of previously unknown polymorphic shellcode. In contrast to previous work, our approach relies on a NIDS- embedded CPU emulator that executes every potential instruction sequence in the inspected traffic, aiming to identify the execution behavior of polymorphic shellcode. Our analysis demonstrates that the proposed approach is more robust to obfuscation techniques like self-modifications compared to previous proposals, but also highlights advanced evasion techniques that need to be more closely examined towards a satisfactory solution to the polymorphic shellcode detection problem.     

A genetically optimized level set approach to segmentation of thyroid ultrasound images

This paper presents a novel framework for thyroid ultrasound image segmentation that aims to accurately delineate thyroid nodules. This framework, named GA-VBAC incorporates a level set approach named Variable Background Active Contour model (VBAC) that utilizes variable background regions, to reduce the effects of the intensity inhomogeneity in the thyroid ultrasound images. Moreover, a parameter tuning mechanism based on Genetic Algorithms (GA) has been considered to search for the optimal VBAC parameters automatically, without requiring technical skills. Experiments were conducted over a range of ultrasound images displaying thyroid nodules. The results show that the proposed GA-VBAC framework provides an efficient, effective and highly objective system for the delineation of thyroid nodules.     

Vehicle Velocity Estimation Based on RSS Measurements

This paper presents a technique which is based on pattern recognition techniques, in order to estimate Mobile Terminal (MT) velocity. The proposed technique applies on received signal strength (RSS) measurements and more precisely on information extracted from Iub air interface, in wIDeband code-division multiple access (WCDMA) systems for transmission control purposes. Pattern recognition is performed by HIDden Markov Model (HMM), which is trained with downlink signal strength measurements for specific areas, employing Clustering LARge Applications (CLARA) like a clustering method. Accurate results from a single probe vehicle show the potential of the method, when applied to large scale of MTs. Theodore S. Stamoulakatos is a Senior Research Associate with the Department of Electrical and Computer Engineering at National Technical University of Athens (NTUA). He received his B.Sc. in Mathematics from University of the Aegean, Greece, in 1997, and the M.Sc. in Computer Applications from Dublin City University, Ireland, in 1999 with scholarship from the Irish Ministry of Education. On April ’05 he received his Ph.D. degree from the Department of Electrical Engineering and Computer Science of the National Technical University of Athens. He has been lecturing in DCU various courses including Algorithms & Data Structures, Computer Systems, and Advanced Network Management to both undergraduate and postgraduate students. During his research in NTUA, he has been actively involved in many European and National projects that match his research interests. Both his academic as well as his industrial experience (four years in OTEnet S.A.) allow him to publish several papers in journals and international conferences, which are in the fields of Mobile and Personal Communication Networks, Active Networks, Location Based Services as well as Network and Service Management. Dr. Stamoulakatos is a member of the IEEE. Antonis E. Markopoulos obtained his degree in Informatics and Telecommunications Engineering from University of Athens, Greece in 2000. During his studies he participated in various research projects dealing with the management of fixed and wireless networks. He has also industrial experience for 2 years in INTRASOFT International S.A participating in several projects, national and European. He received his PhD in the field of Cellular and Wireless Communication from the National Technical University of Athens in 2005, where he is working as a Senior Research Engineer in the Telecommunication Laboratory. He has published several papers in journals, international conferences and book chapters. His research interests are in the fields of cellular and wireless networks of present and future generation (4G, WLAN/WPAN, WiMAX) and more specific in the areas of radio resource management and security. He has been mainly involved in many European (IST-CELLO, IST-PACWOMAN, IST-MAGNET, a.o) and National (Greek IST, GGRT) projects. Dr Markopoulos is a member of the IEEE and of the Greek Association of Mechanical and Electrical Engineers. Miltiades E. Anagnostou was born in Athens, Greece, in 1958. He received the Electrical Engineer’s Diploma from the National Technical University of Athens (NTUA) in 1981. In 1987 he received his PhD in the area of computer networks. Since 1989 he has been teaching at the Electrical and Computer Engineering School of NTUA, where he is currently a Full Professor. He teaches courses on modern telecommunications, computer networks, formal specification, stochastic processes, and network algorithms. His research spans several fields, including broadband networks, mobile and personal communications, service engineering, mobile agents, pervasive computing, network algorithms and queuing systems. He is a member of the IEEE and the ACM. Michael E. Theologou received the degree in Electrical Engineering from Patras University and his Ph.D. degree from the Department of Electrical Engineering and Computer Science of the National Technical University of Athens. Currently he is a Professor at National Technical University of Athens, Department of Electrical and Computer Engineering conducting teaching and research in the wider area of Telecommunication Networks and Systems. His research interests are in the fields of Mobile and Personal Communication Networks, Computer Networks, Quality of Service. He has many publications in the above areas.     

Phase resetting in one-dimensional model of the sinoatrial node

In this paper, we use a one-dimensional model of the rabbit sinoatrial node (SAN), and we investigate the response of the model to hyperpolarizing and depolarizing stimulus. Depending on the stimulus timing, either a delay or an advance in the occurrence of next action potential is produced. This resetting behavior of the model is quantified in terms of phase transition curves (PTCs) for short electrical current pulses of varying amplitude which span the whole period. The main focus of this paper is to compare the dynamic properties of the spatially extended system and the single cell model. The detailed analysis of the results provides new insights in the understanding of the transition from the theoretical single cell models to the spatially extended systems.     

High-grade glioma diffusive modeling using statistical tissue information and diffusion tensors extracted from atlases

Glioma, especially glioblastoma, is a leading cause of brain cancer fatality involving highly invasive and neoplastic growth. Diffusive models of glioma growth use variations of the diffusion-reaction equation in order to simulate the invasive patterns of glioma cells by approximating the spatiotemporal change of glioma cell concentration. The most advanced diffusive models take into consideration the heterogeneous velocity of glioma in gray and white matter, by using two different discrete diffusion coefficients in these areas. Moreover, by using diffusion tensor imaging (DTI), they simulate the anisotropic migration of glioma cells, which is facilitated along white fibers, assuming diffusion tensors with different diffusion coefficients along each candidate direction of growth. Our study extends this concept by fully exploiting the proportions of white and gray matter extracted by normal brain atlases, rather than discretizing diffusion coefficients. Moreover, the proportions of white and gray matter, as well as the diffusion tensors, are extracted by the respective atlases; thus, no DTI processing is needed. Finally, we applied this novel glioma growth model on real data and the results indicate that prognostication rates can be improved.     

Automated ischemic beat classification using genetic algorithms and multicriteria decision analysis

Cardiac beat classification is a key process in the detection of myocardial ischemic episodes in the electrocardiographic signal. In the present study, we propose a multicriteria sorting method for classifying the cardiac beats as ischemic or not. Through a supervised learning procedure, each beat is compared to preclassified category prototypes under five criteria. These criteria refer to ST segment changes, T wave alterations, and the patient's age. The difficulty in applying the above criteria is the determination of the required method parameters, namely the thresholds and weight values. To overcome this problem, we employed a genetic algorithm, which, after proper training, automatically calculates the optimum values for the above parameters. A task-specific cardiac beat database was developed for training and testing the proposed method using data from the European Society of Cardiology ST-T database. Various experimental tests were carried out in order to adjust each module of the classification system. The obtained performance was 91% in terms of both sensitivity and specificity and compares favorably to other beat classification approaches proposed in the literature.     

Exhausted but Not Senescent T Lymphocytes Predominate in Lupus Nephritis Patients

Lupus nephritis (LN), a chronic inflammatory disease, is characterized by the substantial disruption of immune homeostasis. This study examines its effects on the T lymphocyte phenotype and, particularly, its senescence- and exhaustion-related immune alterations. T cell subpopulations were determined with flow cytometry in 30 LN patients and 20 healthy controls (HCs), according to the expression of senescence- (CD45RA, CCR7, CD31, CD28, CD57), and exhaustion- (PD1) related markers. The immune phenotype was associated with disease activity and renal histology. LN patients were characterized by pronounced lymphopenia, mainly affecting the CD4 compartment, with a concurrent reduction in the naïve, central and effector memory subsets compared to the HCs. In the CD8 compartment, the naïve subsets were significantly lower than that of the HCs, but a shift in the T cells occurred towards the central memory population. CD4+PD1+ and CD8+PD1+ cells were increased in the LN patients compared to the HCs. However, in CD4 T cells, the increase was limited to CD45RA+, whereas in CD8 T cells, both CD45RA+ and CD45RA− subsets were affected. Disease activity was correlated with CD4+PD1+ and highly differentiated CD4+CD28-CD57+ cells. Histology was only associated with CD4 T cell disturbances, with stage IV presenting reduced naïve and increased senescent subsets. Exhausted T lymphocyte subpopulations predominate within LN patients, while the T cell phenotype varies depending on disease activity.     

Understanding and Exploiting the Trade-Offs between Broadcasting and Multicasting in Mobile Ad Hoc Networks

We find that current group communications protocols are far from "one size fits all", they are typically geared toward and optimized for particular scenarios. Multicasting, in general, works well if the density of group members is sparse and in low mobility; broadcasting, in contrast, works well with a high density of group members and in high mobility. Due to the dynamics of the network, one strategy may be preferable to the other at different times and in different localized regions. In this paper, we first quantify the trade-offs between broadcasting and multicasting and evaluate the suitability of a strategy in various scenarios of deployment. Based on the lessons learned, we design a protocol that adapts in response to the dynamics of the network. We named our protocol Fireworks. Fireworks is a hybrid two-tier multicast/broadcast protocol that provides efficient and lightweight multicast dissemination and self-adapts in response to variations in the density and distribution of group members to provide efficient performance. Fireworks creates pockets of broadcast distribution in areas with many members, while it creates and maintains a multicast backbone to interconnect these dense pockets. Fireworks offers packet delivery statistics comparable to that of a pure multicast scheme but with significantly lower overheads. We also show that Fireworks has a lower level of degrading influence on the performance of coexisting unicast sessions than either traditional multicast or broadcast methods