Fighting tumors is one of the most important problems of medical research. In this paper, antiangiogenic cancer therapy is investigated through its mathematical model.This tumor treatment method targets the endothelium of a growing tumor and belongs to the targeted molecular therapies.The aim of the therapy is not to eliminate the entire tumor,but to decrease the tumor to a minimal volume. An advantage of applying antiangiogenic treatment is that tumor cells show lower tendency of becoming resistant to the applied drugs.Adaptive fuzzy control is implemented for a simplified model to elaborate a control technique which is able to handle the effects of parameter perturbations and uncertainties while keeping the daily and total inhibitor inlet under a given limit.
When the spans of an overhead line are large (for instance over 400 metres) the conductor curve cannot be considered as a parabola, since in that case the difference in comparison to the catenary cannot be neglected. At such times the exact calculation has to be applied, i.e. the conductor curve has to be considered as a catenary (hyperbolic cosine). The catenary based calculation does not have limitations, it can be used for small and large spans as well, but in comparison to the parabola method it is significantly more complicated. This article shows the way of derivation of new equations for the conductor and sag curves based on a known catenary constant, which refers to the chosen conductor type, span length, tension and temperature of the overhead line. The shown formulas ensure exact computing of the conductor height and sag at any point of the span, avoiding errors generated by the approximation of the catenary by a parabola.
In wireless sensor networks developed for ambient assisted living (AAL) applications, power supply is one of the most challenging problems. In the case when measurements have low cost; a method is proposed for decreasing the time of communication by handling the measured data locally. In AAL applications the position tracking of a person is an essential task. Position tracking with motion sensors requires high number of messages and most of them are caused by local movements. Our suggestion is to eliminate these messages. The method is based on Hidden Markov Model of the motions of an observed person. The model provides information based on the estimated global state of the system, which is the position of the person in the space of interest. This state can be forwarded to the nodes so they locally perform the filtering to save valuable energy by not transmitting messages which are not relevant.
The failure of safety-critical embedded systems may have catastrophic consequences, therefore their development process requires a strong verification procedure to obtain a high confidence of correctness in the specification and implementation. Formal modelling and model checking provides a rigorous, mathematically precise verification method. Practical embedded systems are typically complex, distributed and asynchronous, thus they need expressive and compact formal models, and efficient model checking approaches. The saturation algorithm has an efficient iteration strategy. Combined with symbolic data structures, it can be used for state space generation and model checking of asynchronous systems. Coloured Petri nets are a good choice for modelling distributed and asynchronous systems, however their integration with saturation has not been solved in the past. In this paper we describe a new approach for applying saturation-based state space generation and model checking to coloured Petri nets. We demonstrate the performance of our new algorithm on the verification of a safety function used in the Reactor Protection System of a nuclear power plant.
Several studies have presented different issues of an ageing population including the need of enhancing care systems for older people using smart technologies. Falling accidents have a significant impact on healthy life expectancy and are a major problem among independently living older people. This paper presents a solution of the fall detection problem utilizing bio-inspired asynchronous temporal-contrast sensors and neural networks, realizing automated, robust, reliable and unobtrusive fall-detection. A noise reduction scheme suited to the unique nature of the sensor is presented, enabling their use in various applications in addition to fall detection. The process of transforming raw sensor output to a suitable neural network input is also described, along with the neural network creation process, including structure selection, training data assembly, and training algorithm selection for a truly large-scale network.
Mass points are very useful objects not only in physics but also in geometry. There are several ways to approach the mathematics of mass points. In this paper we give an independent interpretation. We define kantor space and kantors as the elements of it. We prove that this is a vector space and give a short overview of the types of bases and the connections between them. One of our important tools is the symmetric distance formula for kantors, which expresses the distance of two points in terms of their kantric coordinates. We introduce the kantric scalar product, which allows us to prove easily the existence of an orthogonal point and give a formula of the radius of the circumscribed sphere of affinely independent set of points, which is our main result.
This paper presents a carrier phase differential GPS technique for vehicle navigation. The orientation and position of the vehicle can reliably be calculated by the proposed solutions. The developed methods are aided by inertial and magnetic sensors. They are designed for dead reckoning and to handle phase slip. Therefore these solutions can be applied when the set of the available satellites changes frequently. Extended Kalman filters perform state estimation. The paper also presents the low-cost hardware/software architecture of the navigation system. The effectiveness of the methods have been proven in real car and flight tests.
The application of Bayesian network based methods is increasingly popular in several research fields where the investigation of complex dependency patterns are of central importance. Bayesian networks provide a rich, graph-based language for the refined characterization of relevance types, and has a built-in mechanism for the correction of multiple testing. In the paper we discuss two main topics: the effects of priors and the applicability of Bayesian structure based odds ratio. The selection of an adequate prior is generally required by Bayesian methods and yet there is no general method for prior selection in the multivariate case. Here we analyze the effects of different priors and propose a method for prior selection based on expected effect size. In the second part of the paper we investigate structural and parametric aspects of relevance, and demonstrate a hybrid effect size measure that allows an integrated analysis of these aspects.
The high level synthesis (HLS) tools may result in a multiprocessing structure, where the time demand of the interchip data transfer (briefly the communication) between the processing units (hardware or software) is determined exactly only after the task-allocation. However, a realistic preliminary estimation of the communication time would help to shape the scheduling and the allocation procedures just for attempting to minimize the communication times in the final structure. Compared to the task-execution times of the processing units, especially significant communication times are required by the serial communication interfaces which are frequently used in microcontroller systems. This paper presents an estimation method by analysing four well-known serial communication interfaces (SPI, CAN, I2C, UART).
The reliability investigation of the lead-free solders is still a current issue. In this paper, Electrochemical Migration (ECM) behavior of novel lead-free micro-alloyed low Ag content solders were investigated by water drop test in NaCl solution. The results were compared to commonly used lead-free and lead bearing solder alloys. It was found that some micro-alloyed solders can have similar low ECM susceptibility than lead bearing ones. X-ray photoelectron spectroscopy was also carried out to find the root cause of different ECM behaviour of micro-alloyed solders. The results of water drop test and x-ray photoelectron spectroscopy were in good agreement relating electrochemical migration susceptibility. Since SAC0807 solder alloy has shown higher corrosion rate than SAC0307 and has also higher ability for ECM as well. It is concluded, that some lead-free micro-alloyed low Ag content solder alloy (e.g.: SAC0807) could have a high reliability risk in the electronic devices during operation.
This paper deals with corner detection of simple geometric objects in quantized range images. Low depth resolution and noise introduce challenges in edge and corner detection. Corner detection and classification is based on layer by layer depth data extraction and morphologic operations. Appearance based heuristics are applied to identify different corner types defined in this paper. Both computer generated and captured range images are dealt with. Synthetic range images have arbitrary range resolution while captured images are based on the sensor used. Real world data is collected using a structured light based sensor to provide dense range map.
The purpose of our study is to prove that eliminating bone shadows from chest radiographs can greatly improve the accuracy of automated lesion detection. To free images from rib and clavicle shadows, they are first segmented using a dynamic programming approach. The segmented shadows are eliminated in difference space. The cleaned images are processed by a hybrid lesion detector based on gradient convergence, contrast and intensity statistics. False findings are eliminated by a Support Vector Machine. Our method can eliminate approximately 80% of bone shadows (84% for posterior part) with an average segmentation error of 1 mm. With shadow removal the number of false findings dropped from 2.94 to 1.23 at 63% of sensitivity for cancerous tumors. The output of the improved system showed much less dependence on bone shadows. Our findings show that putting emphasis on bone shadow elimination can lead to great benefits for computer aided detection.
Analog-digital converters are inherently nonlinear. Conventional A/D conversion allows no real remedy afterwards. However, an alternative is to increase the available information by observing the transition instants, even with a conventional ADC. Performing the conversion with significant oversampling data points at threshold level crosses can be used. The values of these samples are precisely known, assuming histogram test was executed on the ADC beforehand. For almost constant signals having too few transition level crossings, dither is added. Interpolation is utilized to ensure uniformly sampled data points. This method reduces the conversion error considerably.
This paper explores novel, polynomial time, heuristic, approximate solutions to the NP-hard problem of finding the optimal job schedule on identical machines which minimizes total weighted tardiness (TWT). We map the TWT problem to quadratic optimization and demonstrate that the Hopfield Neural Network (HNN) can successfully solve it. Furthermore, the solution can be significantly sped up by choosing the initial state of the HNN as the result of a known simple heuristic, we call this Smart Hopfield Neural Network (SHNN). We also demonstrate, through extensive simulations, that by considering random perturbations to the Largest Weighted Process First (LWPF) and SHNN methods, we can introduce further improvements to the quality of the solution, we call the latter Perturbed Smart Hopfield Neural Network (PSHNN). Finally, we argue that due to parallelization techniques, such as the use of GPGPU, the additional cost of these improvements is small. Numerical simulations demonstrate that PSHNN outperforms HNN in over 99% of all randomly generated cases by an average of 3-7%, depending on the problem size. On a specific, large scale scheduling problem arising in computational finance at Morgan Stanley, one of the largest financial institutions in the world, PSHNN produced a 5% improvement over the next best heuristic.
A new approach of nonholonomic path planning for car-like robots is presented. The main idea is similar to many existing approaches which obtain a path in two phases. It is familiar in nonholonomic planning that at first a holonomic path is planned which is approximated by a nonholonomic one in a second step by subdividing it into smaller parts and replacing them with local paths fulfilling the kinematic constraints. These methods mostly rely on probabilistic methods and heuristic optimization. Our approach uses a holonomic preliminary path as well, but it serves only as a "loose guidance" to the second phase of the planning process. The final path is not required to contain any of the intermediate points of the preliminary path at all. The method is effective in environments consisting of narrow corridors but having wider free areas as well which can be used for maneuvering.
This paper is intended to describe the design and manufacturing aspects of a simple micromachined capacitive pressure sensor working in the pressure range of 0-1000 mbar. 500 µm thick Borofloat ® 33 glass and silicon wafers were used as substrates. The basic transducer structure consists of a rectangular silicon membrane as deformable electrode and a fix aluminum electrode formed on the glass surface. In order to determine the exact geometry of the silicon electrode structure numerical models and simulations were applied. The thin silicon membrane was fabricated by Si bulk micromachining, i.e. anisotropic alkaline etching with electrochemical etch-stop. The two wafers were bonded together at low temperature by anodic bonding. After bonding and dicing the wafers the pressure sensors were characterized mechanically and functionally also. Our results demonstrate the functional behavior of the manufactured sensor structures and provide excellent verification of the preliminary expectations based on theoretical calculations and electro-mechanical simulations.
Back-end analysis tools aiming to carry out model-based verification and validation of dynamic behavioral models frequently produce sequences of simulation steps (called execution traces) as their output. In order to support back-annotation of such traces, we need to store and replay them within a modeling environment (outside the analysis tool). In the paper, we present a technique for replaying recorded execution traces of dynamic modeling languages. Our approach complements static and dynamic metamodels by introducing a generic execution trace metamodel which is used to replay completed executions of a simulation directly over the dynamic model. Furthermore, we present a technique to drive a simulation according to execution trace models. Our approach will be exemplified by the modeling language and trace information of the SAL model checker and BPEL business processes.
Different continuous PWM strategies of multi-phase inverter-fed ac motors are investigated from the point of view of ac motor harmonic losses. It is shown that for phase number over seven - in comparison with the space vector or with the harmonic injection methods - the natural or regular sinusoidal PWM of multi-phase system gives the best solution both from the point of view of the simplicity of realization and the value of harmonic losses. However, the stator harmonic current losses are slightly higher while the rotor harmonic current losses considerably lower than those of the three-phase system. The theoretically derived expressions for stator and rotor loss (for infinite value of switching frequency (MATHCAD program) are checked with resulting of direct computation for finite value of switching frequency (MATLAB program) and by simulation.
We study the combinatorial properties associated with an earlier published, geometric algorithm capable of generating convex bodies in any primary equilibrium class (i.e.bodies with arbitrary numbers of equilibrium points) from a single ancestor.Primary equilibrium classes contain several topological secondary classes based on the arrangement of the equilibrium points.Here we show that the associated graph expansion algorithm is incomplete in the sense that using the same ancestor, not all secondary classes can be generated and we point out the nontrivial set of ancestors necessary to generate all secondary classes.
In this paper, we study the problem of finding sparse, mean reverting portfolios in multivariate time series.This can be applied to developing profitable convergence trading strategies by identifying portfolios which can be traded advantageously when their prices differ from their identified long-term mean.Assuming that the underlying assets follow a VAR(1) process, we propose simplified, dense parameter estimation techniques which also provide a goodness of model fit measure based on historical data.Using these dense estimated parameters, we describe an exhaustive method to select an optimal sparse mean-reverting portfolio which can be used as a benchmark to evaluate faster, heuristic methods such as greedy search.We also present a simple and very fast heuristic to solve the same problem, based on eigenvector truncation.We observe that convergence trading using these portfolio selection methods is able to generate profits on historical financial time series.
