International Journal Information and Communication Technology Research
Volume:2 Issue: 1, Winter 2010

In this paper, we propose a low-complexity model-based single-channel audio separation approach. The proposed method presents three certain advantages over previous 1) replacing commonly used linear masks like Wiener filtering by a proposed non-linear one, we show that it is possible to lower the crosstalk of the interfering source often occurring in a mask-based method while recovering the underlying signals from the observed mixture. Using nonlinear masks establishes a tradeoff between acceptable level of interference and low speech distortion, 2) as a post-processing stage, we use phase synchronization technique to enhance the perceptual quality of the re-synthesized signals, and 3) the proposed method is based on vector quantization (VQ) codebooks. Hence, the complexity is lower than previous GMM-based methods. Through extensive experiments, it is demonstrated that the proposed method can achieve a lower signal-to-distortion ratio (SDR). According to our listening experiments and according to the Mean Opinion Score (MOS) results, it is confirmed that the proposed method is able to recover separated outputs with a higher perceived signal quality.

Maximum Likelihood (ML) estimation of the frequency offset between the transmitter and the receiver from known transmitted preambles is the dominant technique for the estimation of Carrier Frequency Offset (CFO) in OFDM systems. A general formulation of ML detection problem for OFDM systems is provided in this paper and it is described how different ML techniques can be treated as special cases. In addition, major newly proposed ML techniques are compared in unified simulation framework in the presence of AWGN and their performance in term of estimation range and complexityis compared. Furthermore, we proposed two new preamble structures which have comparable complexity to the simplest available methods while their estimation ranges are fairly large, comparable to the largest achieved ranges.

In this paper, a novel planar hybrid multiband antenna formed by a loop-type antenna, a monopole slot antenna, and a coupled planar monopole antenna for the mobile phone is proposed. The loop-type antenna comprises a driven monopole and a coupled strip that printed on the other side of the substrate and short-circuited to the ground. The 0.5λ and 1.0λ mode of the loop-type antenna are excited. Therefore, the 0.5λ mode of the loop-type antenna forms a wide bandwidth of 326 MHz to cover the GSM850/900 operation and the 0.25λ mode of the monopole slot antenna, the 1.0λ mode of the loop-type antenna, and the 0.25λ mode of the coupled monopole antenna provide a wide bandwidth of 1181 MHz to cover the GPS/DCS/PCS/UMTS/WLAN2.4/WiMAX operation. Finally, the 0.5λ mode of the coupled monopole antenna forms a wide bandwidth to cover the WLAN5.2 operation. Moreover, the hybrid antenna occupies a compact area of 16.5 ´ 45 mm2 and it has good radiation characteristics over the operating bands.

The number of reported vulnerabilities is dramatically rising every year. In addition, the combination of different kinds of network devices, services and applications in a complex manner lead to increase the complexity of vulnerabilities. Increasing the number of vulnerabilities and their complications show the importance of vulnerability taxonomies which could provide a common language for defining vulnerabilities and help analyze and assess them. Both the advantages of using vulnerability taxonomies and the features of the taxonomies that have ever been suggested encouraged us to offer the new network vulnerability taxonomy. Our proposed taxonomy is a multi-dimensional and hierarchical taxonomy which classifies network vulnerabilities based on their location, cause and impact. These are three dimensions of our taxonomy. We use ITU-T X-805 security architecture to provide a comprehensive layered classification for the location dimension and also use common weakness enumeration (CWE) project to provide a complete layered classification for the cause dimension of the proposed taxonomy. Finally, we evaluate our taxonomy based on taxonomy requirements. In addition, to demonstrate the usefulness of our taxonomy, a case study applies the taxonomy to a number of network vulnerabilities. We also use this taxonomy to analyze network vulnerabilities. The result of our analysis is a matrix that demonstrates the distribution of network vulnerabilities based on their causes, locations and impacts. In addition to offering a taxonomy that is specific to network vulnerabilities and is beneficial for analyzing network vulnerabilities by covering almost all possible combinations of causes, locations, and impacts, we also introduce and consider network activities in the classification of location dimension for the first time.

As CMOS technology scales down, NoC (Network on Chip) gradually becomes the mainstream of on-chip communication. In this paper we present a methodology to design fault-tolerant routing algorithms for regular direct interconnection networks. It supports fully adaptive routing, does not degrade performance in the absence of faults, and supports a reasonably large number of faults without significantly degrading performance. Consequently, this work examines fault tolerant communication algorithms for use in the Communication Networks including NoC domain. Before two different flooding algorithms, a random walk algorithm and an Intermediate Node Algorithm have been investigated. The first three algorithms have an exceedingly high communication overhead and cause huge congestion in usual traffics. The fourth one which is Intermediate Node algorithm is a static fault-tolerant algorithm which focuses on the faults knowing in advance where they are located. We have developed a new dynamic algorithm based on intermediate node concept and stress value concept to overcome all of blind sides of mentioned algorithms. We have designed a switch/router base on this algorithm and simulated by MAX PLUS II tool and verified it on a mesh NoC in Xilinx environment.

In wireless communication channels fading phenomenon imposes serious limitations upon the system performance. Diversity combining is a well known fading compensation technique. In this paper we propose a diversity combining technique based on a nonlinear Hammerstein type filter to mitigate the destructive fading effect. In the present work, frequency selective Rayleigh fading channels in presence of additive white gaussian noise are considered and m-ary PAM modulation is employed. We first present a theoretical analysis to justify our proposed system. Then the system performance for different power delay profiles and different m-ary PAM modulations are investigated. Comparison of simulation results based on our proposed technique with the results obtained when linear equalizing filters are employed, shows that our technique leads to a considerably higher BER performance at higher SNRs. We also show that our method has a lower complexity than the linear structure. Also, a relative reliability factor for the system is defined and investigated.

The objective of this paper is to design a system to classify Persian speech acts. The driving vision for this work is to provide intelligent systems such as text to speech, machine translation, text summarization, etc. that are sensitive to the speech acts of the input texts and can pronounce the corresponding intonation correctly. Seven speech acts were considered and 3 classification methods including (1) Naive Bayes, (2) K-Nearest Neighbors (KNN), and (3) Tree learner were used. The performance of speech act classification was evaluated using these methods including 10-Fold Cross-Validation, 70-30 Random Sampling and Area under ROC. KNN with an accuracy of 72% was shown to be the best classifier for the classification of Persian speech acts. It was observed that the amount of labeled training data had an important role in the classification performance.