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High-Ievel synthesis accelerates the process of design space exploration in which various transformations and optimizations can be applied to the high-level description. In this paper, a new method has been proposed to improve the high-level synthesis process for non-rectangular multi-dimensional nested loops using reshaping and vectorization techniques. As the high-level descriptions with non-rectangular iteration spaces do not lend themselves well to efficient high-level synthesis process, our method proposes a reshaping technique to convert nonrectangular iteration spaces with certain inter-iteration dependencies to the rectangular ones. Furthermore, the proposed method suggests a vectorization technique to let the different iterations be executed simultaneously in a manner which does not violate inter-iteration dependencies. Finally, this paper combines the proposed reshaping and vectorization techniques to a hybrid method which supports both 2D and 3D perfect/imperfect nested loops and can be extended for the nested loops of dimensions more than three. According to the experimental results, the proposed hybrid method shows average speed-up of 51.9%, 50.1%, and 15.9% in comparison with the state-of-the-art methods for the pipelined perfect, the pipelined imperfect, and the pipelined 3D nested loops, respectively.

In this paper, the channel estimation problem in full duplex (FD) point-to-point wireless communication systems is investigated. Because of the existence of two interfering and communication channels, this problem is fundamentally different from the conventional channel estimation problems in half duplex (HD) communication systems. Here, we propose a blind channel estimator that simultaneously estimates the channel parameters of the FD system without requiring time division duplex. A major problem for blind estimators is the identifiability of the parameters. Therefore, thorough analysis is presented to determine the identifiable parameters in this estimation problem. It will be shown that a completely blind estimator utilizing a symmetric modulation set suffers from phase ambiguity. For this reason, only the channel magnitudes can be estimated blindly. It is further presented that if the modulation set is not centered around the origin then this phase ambiguity can be resolved.

Dynamic or adaptive thresholding strategy is of high interest in pattern recognition, signal and image processing. In this article a powerful method using a combination of multilevel thresholding algorithm, bee algorithm (BA), and hierarchical evolutionary algorithm (HEA) is proposed for segmentation of magnetic resonance images (MRIs). The HEA can be viewed as a modified variant of basic genetic algorithm (GA). The proposed method is based on the BA and, in fact, is an unsupervised clustering method depending on an automatic multilevel thresholding approach. One advantage of the proposed method is that the number of clusters in the given image does not require to be known previously. The results show that the accuracy of the proposed algorithm is very excellent (about 97%).

In this article a novel and effective method for identification of modulation type in digital communication signals at different signal-to-noise ratios (SNRs) is proposed. This method is based on the optimization of an adaptive neuro-fuzzy inference system (ANFIS) and includes three major modules, namely, feature extraction, classification and optimization. In the feature extraction module, a novel combination of the higher order moments (up to eight), higher order cumulants (up to eight) and spectral characteristics are proposed as the most effective features. The ANFIS is considered as a classifier. In the training phase of the ANFIS, the vector of radius plays a very important role in improving the recognition accuracy. Therefore, in the optimization module, cuckoo optimization algorithm (COA) is proposed for optimization of the classifier. Experimental results clearly indicate that the proposed hybrid intelligent method has a high classification accuracy to discriminate between different types of digital signals even at very low SNRs.

Geometric dilution of precision (GDOP) is a powerful, simple and widely used measure for assessing the effectiveness of potential measurements to specify the precision and accuracy of the data received from global positioning system (GPS) satellites. The most correct method to classify or approximate the GPS GDOP is to use inverse matrix on all the combinations and choosing the lowest one, but inversing a matrix puts a lot of computational burden on the navigator's processor. This approach however is a time-consuming task. To overcome the problem, basic back propagation neural network (BPNN) was used. Since the BPNN is too slow for practical problems, including the GPS GDOP classification, in this paper several methods, namely, resilient back propagation (RBP) to train a NN, naive Bayes classifier, Fisher's linear discriminant (FLD) and k-nearest neighbor (KNN) for classification of the GPS GDOP are proposed. Simulation results show that these methods are much more efficient to classify the GPS GDOP data than previous methods.

Wireless Sensor Network (WSN) has gained tremendous research attention recently due to their extensive applications. As sensor nodes being battery operated, many researchers have made attempts to prolong the lifespan of the WSN by reducing the-per node energy consumption and efficiently utilizing the sensor nodes. However, in the tradition WSNs, nodes were homogeneous and hence could not take full advantage of the presence of heterogeneity in the network. To solve this problem in this paper, we propose Geographical and power based clustering algorithm (GPCA): a heterogeneous-aware clustering protocol, which has significant impact on the entire energy dissipation of WSNs. In GPCA, a Virtual Header (VH) transfers data to the nearest VH, and the nearest VH forwards the data to sink node. In this way, the energy dissipation of the entire network is reduced because of the transmitting distance between VHs and the sink that is greatly shortened. Also, a large number of nodes are self-organized by a distributed cluster formation technique. Moreover, a randomized technique is used to rotate the local cluster-heads base on power label in order to evenly distribute the energy load among the sensors in the network. GPCA uses geographical position to enable scalability and robustness for dynamic networks. By using simulation, the proposed GPCA scheme shows superior performance over the current energy-efficient schemes in terms of network lifespan, Energy dissipation and number of alive nodes.

In this paper the design and development of microstrip disk resonator for measuring the amount of Napropamide in water is presented. Different type of disk resonator coated with poly pyrrol donated by single-wall carbon nanotubes is simulated using CST software. From simulation results, the resonant frequency of the disk resonator is changed form 7.4 GHz to 6.8 GHz after applying napropamide to the top of resonator. This technology has the capability to construct network sensor systems to measure the amount of napropamide in shielded environment in future research.

Satellite broadcasting has been considered lately as a promising media for IP streaming due to their wide coverage and high reliable bandwidth. Long propagation delay in GEO satellite system will affect the adaptive algorithms in DVBS-2/RCS systems. Long feedback delay will cause improper physical layer selection of modulation and coding (ModCod). This paper aims to provide an algorithm for physical layer selector in DVB-S2/RCS satellite systems. This algorithm will makeless ModCod switching of the adaptive coding and modulation (ACM) usingreduced number of ModCod combinations. We conclude that the number of ModCods in ACM algorithm can be relatively adjusted to maximize DVB-S2 system stability.

Image thresholding is an important technique for image processing and pattern recognition. Several thresholding techniques have been proposed in the literature. In this paper for segmentation of magnetic resonance images, a novel method using a combination of the multilevel thresholding algorithm and the hierarchical evolutionary algorithm (HEA) is proposed. The HEA can be viewed as a variant of conventional genetic algorithms. The proposed technique is based on the participle swarm optimization (PSO) and, in fact, is an unsupervised clustering method based on an automatic multilevel thresholding approach. One advantage of the proposed method is that the number of clusters in the given image does not need to be known in advance. We evaluate and validate performance of the proposed method using simulation studies. The simulation results show that the accuracy of the proposed method is about 96%.