Comparison of the pencil beam convolution algorithm and the analytical anisotropic algorithm in breast tumors

The calculation of the dose is one of the key steps in radiotherapy planning1-5. This calculation should be as accurate as possible, and over the years it became feasible through the implementation of new algorithms to calculate the dose on the treatment planning systems applied in radiotherapy. When a breast tumour is irradiated, it is fundamental a precise dose distribution to ensure the planning target volume (PTV) coverage and prevent skin complications. Some investigations, using breast cases, showed that the pencil beam convolution algorithm (PBC) overestimates the dose in the PTV and in the proximal region of the ipsilateral lung. However, underestimates the dose in the distal region of the ipsilateral lung, when compared with analytical anisotropic algorithm (AAA). With this study we aim to compare the performance in breast tumors of the PBC and AAA algorithms.

Provably Good Task Assignment for Two-Type Heterogeneous Multiprocessors Using Cutting Planes

Consider scheduling of real-time tasks on a multiprocessor where migration is forbidden. Specifically, consider the problem of determining a task-to-processor assignment for a given collection of implicit-deadline sporadic tasks upon a multiprocessor platform in which there are two distinct types of processors. For this problem, we propose a new algorithm, LPC (task assignment based on solving a Linear Program with Cutting planes). The algorithm offers the following guarantee: for a given task set and a platform, if there exists a feasible task-to-processor assignment, then LPC succeeds in finding such a feasible task-to-processor assignment as well but on a platform in which each processor is 1.5 × faster and has three additional processors. For systems with a large number of processors, LPC has a better approximation ratio than state-of-the-art algorithms. To the best of our knowledge, this is the first work that develops a provably good real-time task assignment algorithm using cutting planes.

NoC contention analysis using a branch-and-prune algorithm

“Many-core” systems based on a Network-on-Chip (NoC) architecture offer various opportunities in terms of performance and computing capabilities, but at the same time they pose many challenges for the deployment of real-time systems, which must fulfill specific timing requirements at runtime. It is therefore essential to identify, at design time, the parameters that have an impact on the execution time of the tasks deployed on these systems and the upper bounds on the other key parameters. The focus of this work is to determine an upper bound on the traversal time of a packet when it is transmitted over the NoC infrastructure. Towards this aim, we first identify and explore some limitations in the existing recursive-calculus-based approaches to compute the Worst-Case Traversal Time (WCTT) of a packet. Then, we extend the existing model by integrating the characteristics of the tasks that generate the packets. For this extended model, we propose an algorithm called “Branch and Prune” (BP). Our proposed method provides tighter and safe estimates than the existing recursive-calculus-based approaches. Finally, we introduce a more general approach, namely “Branch, Prune and Collapse” (BPC) which offers a configurable parameter that provides a flexible trade-off between the computational complexity and the tightness of the computed estimate. The recursive-calculus methods and BP present two special cases of BPC when a trade-off parameter is 1 or ∞, respectively. Through simulations, we analyze this trade-off, reason about the implications of certain choices, and also provide some case studies to observe the impact of task parameters on the WCTT estimates.

Cutting path as a rural postman problem

The Rural Postman Problem (RPP) is a particular Arc Routing Problem (ARP) which consists of determining a minimum cost circuit on a graph so that a given subset of required edges is traversed. The RPP is an NP-hard problem with significant real-life applications. This paper introduces an original approach based on Memetic Algorithms - the MARP algorithm - to solve the RPP and, also deals with an interesting Industrial Application, which focuses on the path optimization for component cutting operations. Memetic Algorithms are a class of Metaheuristics which may be seen as a population strategy that involves cooperation and competition processes between population elements and integrates “social knowledge”, using a local search procedure. The MARP algorithm is tested with different groups of instances and the results are compared with those gathered from other publications. MARP is also used in the context of various real-life applications.

MOEA/VAN: Multiobjective Evolutionary Algorithm based on Vector Angle Neighborhood

Natural selection favors the survival and reproduction of organisms that are best adapted to their environment. Selection mechanism in evolutionary algorithms mimics this process, aiming to create environmental conditions in which artificial organisms could evolve solving the problem at hand. This paper proposes a new selection scheme for evolutionary multiobjective optimization. The similarity measure that defines the concept of the neighborhood is a key feature of the proposed selection. Contrary to commonly used approaches, usually defined on the basis of distances between either individuals or weight vectors, it is suggested to consider the similarity and neighborhood based on the angle between individuals in the objective space. The smaller the angle, the more similar individuals. This notion is exploited during the mating and environmental selections. The convergence is ensured by minimizing distances from individuals to a reference point, whereas the diversity is preserved by maximizing angles between neighboring individuals. Experimental results reveal a highly competitive performance and useful characteristics of the proposed selection. Its strong diversity preserving ability allows to produce a significantly better performance on some problems when compared with stat-of-the-art algorithms.

Automatic measurement of key ski jumping phases and temporal events with a wearable system.

We propose a new method, based on inertial sensors, to automatically measure at high frequency the durations of the main phases of ski jumping (i.e. take-off release, take-off, and early flight). The kinematics of the ski jumping movement were recorded by four inertial sensors, attached to the thigh and shank of junior athletes, for 40 jumps performed during indoor conditions and 36 jumps in field conditions. An algorithm was designed to detect temporal events from the recorded signals and to estimate the duration of each phase. These durations were evaluated against a reference camera-based motion capture system and by trainers conducting video observations. The precision for the take-off release and take-off durations (indoor < 39 ms, outdoor = 27 ms) can be considered technically valid for performance assessment. The errors for early flight duration (indoor = 22 ms, outdoor = 119 ms) were comparable to the trainers' variability and should be interpreted with caution. No significant changes in the error were noted between indoor and outdoor conditions, and individual jumping technique did not influence the error of take-off release and take-off. Therefore, the proposed system can provide valuable information for performance evaluation of ski jumpers during training sessions.

Female Genital Mutilation/Cutting - A Literature Review

This report represents the result of two different strands of work by the Women's Health Council. At the beginning of 2006, due to the recent significant inward migration experienced in Ireland, the Council's board identified the promotion of the health of ethnic minority women as a key area of work in its strategic plan for the period 2007-2009. At the same time, it was also decided that the problem of gender-based violence would also be addressed through a number of research and policy initiatives. This report focuses on a health issuethat marries these two concerns, Female Genital Mutilation/Cutting (FGM/C – see below for definition) and serves as an accompanying document to the recently published Violence Against Women and Health (2007) and the forthcoming study on Ethnic Minority Women and Gender-Based Violence. Download document here

Cutting edge: a Toll-like receptor 2 polymorphism that is associated with lepromatous leprosy is unable to mediate mycobacterial signaling.

Toll-like receptors (TLRs) are key mediators of the innate immune response to microbial pathogens. We investigated the role of TLRs in the recognition of Mycobacterium leprae and the significance of TLR2Arg(677)Trp, a recently discovered human polymorphism that is associated with lepromatous leprosy. In mice, TNF-alpha production in response to M. leprae was essentially absent in TLR2-deficient macrophages. Similarly, human TLR2 mediated M. leprae-dependent activation of NF-kappaB in transfected Chinese hamster ovary and human embryonic kidney 293 cells, with enhancement of this signaling in the presence of CD14. In contrast, activation of NF-kappaB by human TLR2Arg(677)Trp was abolished in response to M. leprae and Mycobacterium tuberculosis. The impaired function of this TLR2 variant provides a molecular mechanism for the poor cellular immune response associated with lepromatous leprosy and may have important implications for understanding the pathogenesis of other mycobacterial infections.

Market potentialfor free-cutting steels in Europe

Työn tavoitteena oli selvittää tilannetta Euroopan automaattiteräsmarkkinoilla ja sen perusteella arvioida Imatra Steelin mahdollisuuksia kilpailla kyseessä olevilla markkinoilla. Tärkein tavoite oli kokonaismarkkinapotentiaalin arvioiminen Saksan, Ruotsin, Englannin ja Suomen markkinoilla. Lisäksi selvitettiin käytetyt automaattiteräslajit ja mitta-alue, hintataso sekä koneistukseenliittyviä teknisiä yksityiskohtia.Tavoitteena oli myös kartoittaa asenteita ja mielipiteitä mahdollisesta lyijyn käytön kieltämisestä teräksen seosaineena tulevaisuudessa. Paremman kokonaiskuvan saamiseksi analysoitiin myös kilpailutilannetta Euroopassa. Työn teoriakehyksessä tutkittiin teollisuustuotteiden markkinatutkimuksen suorittamisen erityispiirteitä, markkinapotentiaalin määrittämiseen liittyviä käsitteitä ja kilpailija-analyysin suorittamista. Empiirinen tutkimus suoritettiin pääasiassa asiantuntijoiden haastattelujen ja kyselyjen avulla. Haastateltavina oli tukkureita ja loppukäyttäjiä. Kilpailutilanteen kartoittaminen perustuu lähinnä sekundääriseen tietoon, Internet-sivuihin ja myyntikonttoreiden aikaisemmin keräämään tietoon.Automaattiterästen kokonaispotentiaaliksi Euroopassa arvioitiin miljoona tonnia ja suurin osa kaupasta käydään tutkituilla markkina-alueilla. Suurimmat volyymit sijoittuvat pienemmille mitta-alueille, Æ 12 - 50 mm. Markkinoita hallitsee muutama suuri teräksen valmistaja. Imatra Steel kohtuullisen pienenä toimittajana ei pysty kilpailemaan volyymilla ja tuotevalikoimallaan suurten teräsjättien kanssa. Imatra Steelin mahdollinen strategiavaihtoehto olisi yrittää löytää ne kapeat segmentit ja markkinaraot, joilla sen tuotteet jatietotaito tuovat asiakkaalle suurimman mahdollisen hyödyn verrattuna kilpailijoihin.

An energy efficient vertical handover decision algorithm

As wireless communications evolve towards heterogeneousnetworks, mobile terminals have been enabled tohandover seamlessly from one network to another. At the sametime, the continuous increase in the terminal power consumptionhas resulted in an ever-decreasing battery lifetime. To that end,the network selection is expected to play a key role on howto minimize the energy consumption, and thus to extend theterminal lifetime. Hitherto, terminals select the network thatprovides the highest received power. However, it has been provedthat this solution does not provide the highest energy efficiency.Thus, this paper proposes an energy efficient vertical handoveralgorithm that selects the most energy efficient network thatminimizes the uplink power consumption. The performance of theproposed algorithm is evaluated through extensive simulationsand it is shown to achieve high energy efficiency gains comparedto the conventional approach.

Supply chain costs of a small board lot: sheet cutting and logistics

Succeeding in small board lot (0-20 tons) deliveries, is not always prosperous and failures as well as extra costs compared to standard costs arise. Failure deliveries from converting plants to customer locations tie a lot of unwanted and unexpected costs. Extra costs are handled as quality costs and more precise, internal failure costs. These costs revolve from unsuccessful truck payloads, redundant warehousing or unfavorable routing as examples. Quality costs are becoming more and more important factor in company’s financial decision making. Actual, realized truck payload correlates with the extra costs occurring, so filling the truck payload all get-out well is a key to lower the extra costs. Case company in this study is Corporation A, business segment Boards. Boards have outsourced half of their converting in order to gain better customer service via flexibility, lead time reductions and logistics efficiency improvements. Examination period of the study is first two quarters of year 2008 and deliveries examined are from converters to the customer locations. In Corporation A’s case, the total loss in failure deliveries is hundreds of thousands of Euros during the examination period. So, the logistics goal of getting the right product to the right place and right time for the least cost, does not completely realize.

GAMS MINLP Solver comparisons and some improvements to the AlphaECP algorithm

I doktorsavhandlingen undersöks förmågan att lösa hos ett antal lösare för optimeringsproblem och ett antal svårigheter med att göra en rättvis lösarjämförelse avslöjas. Dessutom framläggs några förbättringar som utförts på en av lösarna som heter GAMS/AlphaECP. Optimering innebär, i det här sammanhanget, att finna den bästa möjliga lösningen på ett problem. Den undersökta klassen av problem kan karaktäriseras som svårlöst och förekommer inom ett flertal industriområden. Målet har varit att undersöka om det finns en lösare som är universellt snabbare och hittar lösningar med högre kvalitet än någon av de andra lösarna. Det kommersiella optimeringssystemet GAMS (General Algebraic Modeling System) och omfattande problembibliotek har använts för att jämföra lösare. Förbättringarna som presenterats har utförts på GAMS/AlphaECP lösaren som baserar sig på skärplansmetoden Extended Cutting Plane (ECP). ECP-metoden har utvecklats främst av professor Tapio Westerlund på Anläggnings- och systemteknik vid Åbo Akademi.

An Approach Towards The Development Of An Efficient Symmetric Key Encryption Scheme

n the recent years protection of information in digital form is becoming more important. Image and video encryption has applications in various fields including Internet communications, multimedia systems, medical imaging, Tele-medicine and military communications. During storage as well as in transmission, the multimedia information is being exposed to unauthorized entities unless otherwise adequate security measures are built around the information system. There are many kinds of security threats during the transmission of vital classified information through insecure communication channels. Various encryption schemes are available today to deal with information security issues. Data encryption is widely used to protect sensitive data against the security threat in the form of “attack on confidentiality”. Secure transmission of information through insecure communication channels also requires encryption at the sending side and decryption at the receiving side. Encryption of large text message and image takes time before they can be transmitted, causing considerable delay in successive transmission of information in real-time. In order to minimize the latency, efficient encryption algorithms are needed. An encryption procedure with adequate security and high throughput is sought in multimedia encryption applications. Traditional symmetric key block ciphers like Data Encryption Standard (DES), Advanced Encryption Standard (AES) and Escrowed Encryption Standard (EES) are not efficient when the data size is large. With the availability of fast computing tools and communication networks at relatively lower costs today, these encryption standards appear to be not as fast as one would like. High throughput encryption and decryption are becoming increasingly important in the area of high-speed networking. Fast encryption algorithms are needed in these days for high-speed secure communication of multimedia data. It has been shown that public key algorithms are not a substitute for symmetric-key algorithms. Public key algorithms are slow, whereas symmetric key algorithms generally run much faster. Also, public key systems are vulnerable to chosen plaintext attack. In this research work, a fast symmetric key encryption scheme, entitled “Matrix Array Symmetric Key (MASK) encryption” based on matrix and array manipulations has been conceived and developed. Fast conversion has been achieved with the use of matrix table look-up substitution, array based transposition and circular shift operations that are performed in the algorithm. MASK encryption is a new concept in symmetric key cryptography. It employs matrix and array manipulation technique using secret information and data values. It is a block cipher operated on plain text message (or image) blocks of 128 bits using a secret key of size 128 bits producing cipher text message (or cipher image) blocks of the same size. This cipher has two advantages over traditional ciphers. First, the encryption and decryption procedures are much simpler, and consequently, much faster. Second, the key avalanche effect produced in the ciphertext output is better than that of AES.

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year