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With the help of Rec. ITU-R P.1546 and geography information system, the interference analysis for the fixed wireless system and radar is presented based upon the frequency-distance rules with minimum coupling loss. To obtain the computational results, real geography information on the map was taken for the given area of 80x60[km]2, and field strength and path profile were illustrated for radar and fixed wireless system operating at 2.7 GHz, for convenience. In addition the interference effect of receiver has been also examined as function of radar beam direction including protection ratio and frequency dependent rejection. The developed interference analysis can be actually applied to assess interoperability for wireless systems in the VHF and UHF bands.

Smart home technologies have become, in the last few years, a very active topic of research. However, many scientists working in this field do not possess smart home infrastructures allowing them to conduct satisfactory experiments in a concrete environment with real data. To address this issue, this paper presents a new flexible 3D smart home infrastructure simulator, which is developed in Java specifically to help researchers working in the field of activity recognition. A set of pre-recorded scenarios, made with data extracted from clinical trials, will be included with the simulator in order to give a common foundation to test activity recognition algorithms. The goal is to release the SIMACT simulator with a visual scenario editor as an open source component that will benefit the whole smart home research community.

In former work [18], we propose a scheme to more efficiently preselect similar patches for the nonlocal means filter, based on the Patch-oriented two-dimensional principal component analysis (2DPCA) technique. Although the method can yield good results, the computational complexity remains high. For this reason, in this paper we proposed an improvement of the work, which is fast and directly employs features extracted by the patch-oriented 2DPCA to compute the weights. The new approach has been tested on a commonly-used standard test image database. The results demonstrate that our method can significantly improve the denoising effect.

The shortest path tree construction is essential in network routing, and the Dijkstra algorithm, which is a static routing algorithm, is widely used. When some links in a network have new weights, dynamic routing algorithms are more efficient than static routing algorithms. This is because the dynamic routing algorithms reduce redundancy by re-computing only the affected sections of the network with the changed links. However, the dynamic routing algorithms are not efficient in some situations and can increase computation time to create the shortest path tree. Therefore, hybrid routing algorithms, which reduce the total execution time of shortest path tree computation using the advantages of both static and dynamic routing algorithms, have been suggested recently. In this paper, a multi-path hybrid routing algorithm is presented that uses multi-path information to create the shortest path tree when some links have new weights. Comparisons with other routing algorithms, such as Dijkstra, Dynamic Dijkstra, and Hybrid Shortest Path Tree (HSPT), demonstrate that the multi-path hybrid routing algorithm provides better performance as the execution time decreases.

Stock market prediction is an important area of financial forecasting, which is of great interest to stock investors, stock traders and applied researchers. To determine the buy and sell time is one of the most important issues for investors in stock market. In this paper, a fuzzy approach using the famous candlestick method to stock market timing is investigated. Drawing candlesticks are very easy, but to make the system understand them is challenging. Using fuzzy logic first we try to make the system understand the candlestick formations and verify with what we can see and then we go on for forecasting the future trend using various candlestick techniques. The fuzzified candlesticks tend to represent the real market behavior more closely as done by humans. Here we have tried to develop a method by which fuzzification of candlesticks can be done and then by using the fuzzified candlesticks we can perform predictions of market movements with various candlestick patterns like Engulfing [2] and modified Engulfing (U-Turn) by establishing fuzzy rules. The type of real time data used here is the daily values of S&P CNX NIFTY 50 index used in National Stock Exchange of India for stock futures trading.

The purpose of the real-time monitoring of target or incident is to have a timely alarm, to find the invasion of effective goal is the core technology. Distance measuring of intrusive targets is important for indoor monitoring, according to the detected intrusion target image, calculating the distance between the target and the camera, it can provide the basis for the computer to determine the type of invasion, and finally provide information for the security alarm. In order to meet the needs of the indoor environment monitoring, the paper hereby presents a set of binocular vision-based motion detection system. The system is divided into two parts, one part is offline training, and another is online computation. The offline part consists of stereo calibration module and revised distance parameter module. The online part consists of data acquisition and storage module, moving target detection module, the corresponding point matching module and ranging module. In Stereo calibration module, the internal and external parameters of two cameras are obtained. In motion detection module, for dual-channel video capture respectively, the background subtraction algorithm is used for object detection, and a mixed Gaussian model is used as the adaptive background updating method. Then based on static camera and fixed position, objects in binocular images obtained by binocular camera will be coarse matched firstly, then will be fine matched using scale invariant features transform algorithm to find the accurate match points under this system. In the mean time, revised distance parameter module provides a correction parameter for the traditional binocular parallax distance formula. Finally, the distance between the target and the camera is calculated. Experiments show that this system can be very effective in extracting moving targets, getting match points then measuring distance, especially in distance measuring of obstructed targets.

Flexible Job shop scheduling is very important in production management and combinatorial optimization. It is NP-hard problem and consists of two sub-problems: sequencing and assignment. Multiobjective Flexible Job-Shop Scheduling Problems (MFJSSP) is formulated as three-objective problem which minimizes completion time (makespan), critical machine workload and total work load of all machines. In this paper a Multiobjective Artificial Immune Algorithm (MAIA) for FJSSP is presented. The proposed algorithm increases the speed of convergence and diversity of population. Kacem and Bradimart data are used to evaluate the effectiveness of MAIA. The experimental results show a better performance in comparison to other approaches.

Industrial diagnosis is a domain where problems are recurrent and therefore previous documented solution can be successfully reused. Different methodologies may be implemented for a given diagnosis domain, one of the appropriate, appears to be Case-Based Reasoning (CBR). A CBR system is a combination of processes and knowledge called “knowledge containers“, that allows to preserve and to exploit previous experiences. Its reasoning power can be improved through the use of general knowledge about the domain in question. CBR systems combining case specific knowledge with general domain knowledge models are called knowledge intensive CBR (KI-CBR). The present work aims to develop a CBR application for fault diagnosis of steam turbines that integrates a domain knowledge modeling in an ontological form. This system is view as a KI-CBR system based on domain ontology, built around jCOLIBRI a well-known framework to design KI-CBR systems.

Network-on-Chip (NoC) is the interconnection platform that answers the requirements of the modern on-Chip design. Small optimizations in NoC router architecture can show a significant improvement in the overall performance of NoC based systems. Power consumption, area overhead and the entire NoC performance is influenced by the router buffers. Resource sharing for on-chip network is critical to reduce the chip area and power consumption.An area efficient implementation of a routing node for a NoC is presented. Of the four components of routing node, the input block (mainly consisting of buffers) and scheduler have been modified to save area requirements. The other two components of the routing node take up negligible area in comparison. The use of custom SRAM in place of synthesizable flip flops in the input block has resulted in a saving of over 26% of the silicon area and power optimization is 65% when operated at 16 ns clock. Clock gating is an important high-level technique for reducing the power consumption of a design. Clock gating reduces the clock network power dissipation, relaxes the datapath timing, and reduces routing congestion by eliminating feedback multiplexer loops. For designs that have large multi-bit registers, clock gating can save power and reduce the number of gates in the design. In our design case, it has been further observed that the power optimization with clock gating techniques at RTL level saves 67.38%, Gate Level 67.29% & Power Driven around 68.79% of power while 30.38 %, 27.85 % &31.21% silicon area respectively have been saved.

Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original firefly algorithm (FA) is used to solve the UCAV path planning problem. Furthermore, a new modified firefly algorithm (MFA) is proposed to solve the UCAV path planning problem, and a modification is applied to exchange information between top fireflies during the process of the light intensity updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic FA. The realization procedure for original FA and this improved meta-heuristic approach MFA is also presented. To prove the performance of this proposed meta-heuristic method, MFA was compared with FA and other population-based optimization methods, such as, ACO, BBO, DE, ES, GA, PBIL, PSO and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other model.

In this paper, we present a work consisting of conceiving and realizing a decision making support system for the clinical decision. Being based on Case-Based Reasoning (CBR) the system will highlight the impact of this technique in the clinical decision making domain. The experience has its importance in medicine, and the nature of CBR has motivated us to use it for the diagnosis and the therapy of a very grave respiratory disease bound to tobacco, for instance the Chronic Obstructive Pulmonary Disease (COPD). Our approach proposes to apply a CBR process by modeling the adaptation phase with an independent component: an expert system using forward chaining. For the retrieval phase, some heuristics have been traced for estimating the similarity on missing data and symbolic descriptors. Preliminary experimentations show that the expert system models well the adaptation task and gives satisfactory answers to the user. For the retrieval phase, our heuristic functions have proved a good impact on the results.