Reza Pulungan

Model checking Markov reward models with impulse rewards

This paper considers model checking of Markov reward models (MRMs), continuous-time Markov chains with state rewards as well as impulse rewards. The reward extension of the logic CSL (continuous stochastic logic) is interpreted over such MRMs, and two numerical algorithms are provided to check the reachability of a set of goal states under a time and an accumulated reward constraint. This extends existing model-checking techniques for MRMs with just state rewards, and improves the applicability to thousands of states. Our approach is illustrated by using rewards for energy consumption in the setting of dynamic power management.

Time-Dependent Analysis of Attacks

The success of a security attack crucially depends on time: the more time available to the attacker, the higher the probability of a successful attack; when given enough time, any system can be compromised. Insight in time-dependent behaviors of attacks and the evolution of the attacker’s success as time progresses is therefore a key for effective countermeasures in securing systems. This paper presents an efficient technique to analyze attack times for an extension of the prominent formalism of attack trees. If each basic attack step, i.e., each leaf in an attack tree, is annotated with a probability distribution of the time needed for this step to be successful, we show how this information can be propagated to an analysis of the entire tree. In this way, we obtain the probability distribution for the entire system to be attacked successfully as time progresses. For our approach to be effective, we take great care to always work with the best possible compression of the representations of the probability distributions arising. This is achieved by an elegant calculus of acyclic phase type distributions, together with an effective compositional compression technique. We demonstrate the effectiveness of this approach on three case studies, exhibiting orders of magnitude of compression.

Compositional Performability Evaluation for STATEMATE

This paper reports on our efforts to link an industrial state-of-the-art modelling tool to academic state-of-the-art analysis algorithms. In a nutshell, we enable timed reachability analysis of uniform continuous-time Markov decision processes, which are generated from STATEMATE models. We give a detailed explanation of several construction, transformation, reduction, and analysis steps required to make this possible. The entire tool flow has been implemented, and it is applied to a nontrivial example

Does clock precision influence ZigBee's energy consumptions?

Wireless embedded sensor networks are predicted to provide attractive application possibilities in industry as well as at home. IEEE 802.15.4 and ZigBee are proposed as standards for such networks with a particular focus on pairing reliability with energy efficiency, while sacrificing high data rates. IEEE 802.15.4 is configurable in many aspects, including the synchronicity of the communication, and the periodicity in which battery-powered sensors need to wake up to communicate. This paper develops a formal behavioral model for the energy implications of these options. The model is modularly specified using the language MODEST, which has an operational semantics mapping on stochastic timed automata. The latter are simulated using a variant of discrete-event simulation implemented in the tool MOBIUS. We obtain estimated energy consumptions of a number of possible communication scenarios in accordance with the standards, and derive conclusions about the energy-optimal configuration of such networks. As a specific fine point, we investigate the effects of drifting clocks on the energy behavior of various application scenarios.

PLANT RECOGNITION USING STEREO LEAF IMAGE USING GRAY-LEVEL CO-OCCURRENCE MATRIX

Adequate knowledge, such as information about the unique characteristics of each plant, is necessary to identify plant. Researchers have made plant recogni tion based on leaf characteristics. The leaf image- based plant recognition in view of different angles is a new challenge. In this study, the research on the p lant recognition was conducted based on leaf images resulted from 3D stereo camera. The 3D images are very influential in the development of computer vision t heory, which can provide more detailed information of an object. One of the information that can be obtai ned is about the position of the object in its imag e with the background as well as of the camera. One of the ways used to obtain such information is to calcula te the disparity. However, this method will only tell the position of the object compared to other objects wi thout that of range. Sum Absolute Different (SAD) is a method that can be used to find the disparity value. The SAD method does not require heavy computations and long process. Before calculating the disparity, all the images should be previously segmented. The objective of this segmentation is to separate all the objects from the background. Furthermore, filtering and polynomial transformation at the results of disparity is necess ary to improve the quality of resultant images. Furthermor e, 22 features were extracted using GLCM features (second order statistics) of images resulted from disparity improvement. The highest accuracy of match in the recognition of plant varieties was obtained at 50 cm distance a nd in the recognition of three plant varieties was 83.3%.

Acyclic Minimality by Construction---Almost

Acyclic phase-type distributions are phase-type distributions with triangular matrix representations. They constitute a versatile modelling tool in many circumstances. The size of their matrix representation has a strong influence on computational efforts needed when analyzing these distributions. This representation, however, is not unique, and two representations of the same distribution can differ drastically in size. This paper explores an effective algorithm to reduce the size of the matrix representation without altering the distribution. This proceeds via a symbolic representation of the Laplace-Stieltjes transform of the phase-type distribution. The algorithm is of cubic complexity in the size of the given representation. We clarify the circumstances under which the algorithm returns a minimal representation, and discuss in how far it can keep representations minimal when they are constructed via any of three operations---convolution, minimum, and maximum. A case study modelling delay propagation in railway networks demonstrates the practicality of the approach.

Optic disc and cup segmentation by automatic thresholding with morphological operation for glaucoma evaluation

This research proposes a robust method for disc localization and cup segmentation that incorporates masking to avoid misclassifying areas as well as forming the structure of the cup based on edge detection. Our method has been evaluated using two fundus image datasets, namely: D-I and D-II comprising of 60 and 38 images, respectively. The proposed method of disc localization achieves an average

Stationary Object Detection for Vision-Based Smart Monitoring System

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