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In this paper we propose a quadratic dynamic programming algorithm applied in software testing domain, more specific in the test case selection decision making. We addressed a specific problem in software testing: running a subset of test cases from the whole set of available test cases in a limited time frame with the goal of maximizing the chances of finding potential defects. We employed both objective methods as the dynamic programming algorithm and subjective and empiric human decision as defining the selection and prioritization criteria. The proposed solution is suited for medium to large projects where in the worst-case scenarios the memory space complexity of the proposed algorithm does not exceed the order of GBytes. The proposed optimization algorithm is presented in pseudocode along with the dynamic programming recurrence formula and potential selection criteria as currently used in the industry.

Living organisms have developed and optimized ingenious defense strategies based on positional entropy. One of the most significant examples in this respect is known as protean behavior, where a prey animal under threat performs unpredictable zig-zag movements in order to confuse, delay or escape the predator. This kind of defensive behavior can inspire efficient strategies for patrolling robots evolving in the presence of adversaries. The main goal of our proposed bioinspired method is to implement the protean behavior by altering the reference path of the robot with sudden and erratic direction changes without endangering the robot’s overall mission. By this, a foe intending to target and destroy the mobile robot from a distance has less time for acquiring and retaining the proper sight alignment. The method uses the chaotic dynamics of the 2D Arnold’s cat map as a primary source of positional entropy and transfers this feature to every reference path segment using the kinematic relative motion concept. The effectiveness of this novel biologically inspired method is validated through extensive and realistic simulation case studies.

In the last two decades, Interactive White Boards (IWBs) have been widely available as a pedagogic tool. The usability of these boards for teaching disciplines where complex drawings are needed, we consider debatable in multiple regards. In a previous study, we proposed an alternative to the IWBs as a blackboard augmented with a minimum of necessary digital elements. The current study continues our previous research on hybrid design tools, analyzing the limitations of the developed hybrid system regarding the perceived quality of the images being repeatedly captured, annotated, and reprojected onto the board. We validated the hybrid system by evaluating the quality of the projected and reprojected images over a blackboard, using both objective measurements and subjective human perception in extensive and realistic case studies. Based on the results achieved in the current research, we conclude that the proposed hybrid system provides good quality support for teaching disciplines that require complex drawings and board interaction.

In this paper we present useful methodologies in analyzing program code written in C programming language in order to detect source code clones between two or multiple files. For detecting student plagiarism in C code, we implemented a three phase clone detection algorithm based on the AST (Abstract Syntax Tree) approach. Starting from the state of the art in this domain, we make a short description and comparison between the proposed methods and finally we present a case study upon student assignments for the Programming Techniques classes. In the end, we conclude pointing the future directions of development and improvement in order to obtain a better clone detection tool.

Wireless sensor networks represent a fascinating domain, with a wide area of applications in various environments. For this reason, in the world of wireless networks professionals, there is a major interest concerning the security issues in network communication and data flow between a sensor network and other types of intelligent devices. This paper proposes a new intrusion detection system approach for assuring the security of a wireless sensor network against malicious attacks. Our method relies on the huge potential provided by the mixture between software modules developed in low-level or high-level software languages with modules developed in powerful numerical-oriented software (Matlab/Simulink) to increase the accuracy of data processing. The main result of this paper is the implementation know-how for an intelligent mixed system that use both hardware and software resources for proofing a wireless sensor network.

In the last decade, wireless sensor network (WSN) domain had benefit from a huge development effort and a major technological boom. These wireless networks equipped with tiny sensors can be used in a wide area of applications and various environments. This is one of the reasons that conducted to a continuous concern for improving the communication and the data flow between a WSN and other types of intelligent devices. In this paper we propose a new concept of the complex software development, including Matlab/Simulink modules, for intelligent distributed systems developed around WSN and a base computer. The system as a whole deals with network real-time data by using complex numerical mechanisms and reacts based on specific rules.

Because of numerous possible causes involved, it isn't easy for general physicians to identify the precise reason of the psychiatric diseases and to decide the correct treatment. Bayesian networks are recognized as efficient graphical models with significant capabilities for investigating biomedical data either to obtain relationships between variables, either for medical predictions. Our paper provides a Bayesian network-based analysis of psychiatric patient data, which have been gathered from a Romanian specialized clinic during a couple of years. The development of this Bayesian network led us to the identification of most significant factors that affect some important diseases and their correlations.

With the continuous development of the wireless technology, securing wireless sensor networks became more and more a crucial but also a demanding task. In this paper we propose a combined strategy that is meant to discover malicious nodes within a wireless sensor network and to expel them from the network using a self-destruction node technique. Basically, we will compare every sensor reading with its estimated value provided by an autoregressive predictor. In case the difference between the two values is bigger then a chosen threshold, the sensor node becomes suspicious and a decision block is activated. If this block decides that the node is malicious, a self-destruction procedure will be started against that specific node.

This paper proposes a novel concept and the related architecture for scalable and flexible lighting control in cities. The goal is to minimize the overall energy consumption without affecting safety. We are proposing a two-level control strategy based on qualitative processing. The lower level Zone Lighting Coordination Unit adjusts the lighting intensity of each lamp post through fuzzy logic. The parameters of the fuzzy controller are set by the upper level Lighting Coordination Unit, which uses a knowledge based mechanism. A case study illustrates the two-level control concept.

In this paper we propose a strategy based on past/present values provided by each sensor of a network for detecting their malicious activity. Basically, we will compare at each moment the sensor's output with its estimated value computed by an autoregressive predictor. In case the difference between the two values is higher then a chosen threshold, the sensor node becomes suspicious and a decision block is activated.

A new model of vehicles and pedestrians behavior for complex crossroads road traffic control is developed here. The crossroad is equipped with a wireless sensor network as traffic detector reporting real time traffic events used in the modeling phase. The crossroad model is designed and implemented regarding the three traffic lights colors for each lane entering the junction and permits usage of complex test cases independent of the number of lanes.

Chaos-based cryptography emerged in the early 1990s as an innovative application of nonlinear dynamics in the chaotic regime. Even if in theory chaotic dynamics was thought to evolve into a new revolution in cryptography, in real-life an efficient and reliable chaos-based cryptosystem didn't emerge. The main but not the only reason is the dynamic degradation of digital chaotic systems, a subject that became very popular in the last few years. This paper presents a new theoretical background related to this issue that proves the inefficiency of chaos-based encryption algorithms. Even more, in one of the two relevant case studies presented, another myth is demolished: the analog encryption base on synchronized chaos.