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A new fast and very accurate algorithm for target detection in PCL (Passive Coherent Location) radars is presented. This algorithm in noisy environment that SNR is low as -45dB operates, with an error less than 20 percent. Presented algorithm is capable for target detection by few samples of signals and obtains real-time processing in passive radars. © 2006 IEEE  

A new approach in multiple target detection in PCL (Passive Coherent Location) radars based on TV and Radio ambiguity function processing is presented. Fast computation and high Accuracy are the presented algorithm capabilities. Presented algorithm is a new and simple method which could provide perfect detection in noisy environment up to SNR= -30 dB  

This letter presents a novel method based on sparsity, to solve the problem of deinterleaving pulse trains. The proposed method models the problem of deinterleaving pulse trains as an underdetermined system of linear equations. After determining the mixing matrix, we find sparsest solution of an underdetermined system of linear equations using basis pursuit denoising. This method is superior to previous ones in a number of aspects. First, spurious and missing pulses would not cause any performance reduction in the algorithm. Second, the algorithm works well despite the type of pulse repetition interval modulation that is used. Third, the proposed method is able to separate similar... 

In this paper, we present a method to dynamically observe two important variables of a Gas Metal Arc Welding (GMAW) process, i.e. arc voltage and arc length. To do this, we use Kalman filter to estimate these two variables in a high level noisy environment of GMAW process both in open and closed loop modes  

Although exemplar based approaches have shown good accuracy in classification problems, some limitations are observed in the accuracy of exemplar based automatic speech recognition (ASR) applications. The main limitation of these algorithms is their high computational complexity which makes them difficult to extend to ASR applications. In this paper, an N-best class selector is introduced based on sparse representation (SR) and a tree search strategy. In this approach, the classification is fulfilled in three steps. At first, the set of similar training samples for the specific test sample is selected by k-dimensional (KD) tree search algorithm. Then, an SR based N-best class selector is... 

Augmented Reality (AR), Automatic Speech Recognition (ASR) and Text-to-Speech Synthesis (TTS) can be used to help people with disabilities. In this paper, we combine these technologies to make a new system for helping deaf people. This system can take the narrator's speech and convert it into a readable text and show it directly on AR display. To improve the accuracy of the system, we use Audio-Visual Speech Recognition (AVSR) as a backup for the ASR engine in noisy environments. In addition, we use the TTS system to make our system more usable for deaf people. The results of testing the system show that its accuracy is over 85 percent on average in different places. Also, the result of a... 

Recently, many researches show Augmented Reality (AR) and Automatic Speech Recognition (ASR) can help people with disabilities. In this paper we implement an innovative system for helping deaf people by combining AR, ASR, and AVSR technologies. This system can instantly take narrator's speech and converts it into readable text and shows it directly on AR display. We show that our system's accuracy becomes over 85 percent on average, by using different ASR engines near using an AVSR engine in different noisy environments. We also show in a survey that more than 90 percent of deaf people on average need such system as assistant in portable devices, near using only text or only sign-language... 

In this paper, particle swarm optimization (PSO) is applied to synchronize chaotic systems in presence of parameter uncertainties and measurement noise. Particle swarm optimization is an evolutionary algorithm which is introduced by Kennedy and Eberhart. This algorithm is inspired by birds flocking. Optimization algorithms can be applied to control by defining an appropriate cost function that guarantees stability of system. In presence of environment noise and parameter uncertainty, robustness plays a crucial role in succeed of controller. Since PSO needs only rudimentary information about the system, it can be a suitable algorithm for this case. Simulation results confirm that the proposed... 

Some sources transmit periodic and quasi periodic sparse pulse trains in the environment and a number of sensors might receive them through a single channel simultaneously. It is usually our interest to know which pulse belongs to which source. This identification process has wide applications in communications, radar system, medical applications, and neural systems. Blind source separation (BSS) is one solution for this problem. This paper proposed a geometrical approach to solve BSS problem when observations are whitened data and are obtained from the linear mixtures of 2-valued sparse signals (such as sparse pulse trains). In other words, the proposed approach aims to estimate a rotation... 

In this paper a novel method, called PC-PMC, is proposed to improve the performance of automatic speech recognition systems in noisy environments. This method is based on the parallel model combination (PMC) technique and uses the Cepstral Mean Subtraction (CMS) normalization ability and Principal Component Analysis (PCA) compression and decorrelation capabilities. It takes the advantages of both additive noise compensation of PMC and convolutive noise removal ability of CMS and PCA. The first problem to be solved in the realizing of PC-PMC is that PMC algorithm requires invertible modules in the front-end of the system while CMS normalization is not an invertible process. Also, it is... 

We define a new feature called Teager Energy to Energy and mathematically show that it provides distinguished values for pure tone and white noise signals. We then employ the Teager Energy to Energy feature to propose an efficient procedure for voice activity detection and use simulation results to evaluate its performance in different noisy environments. Furthermore, we experimentally demonstrate the performance of the proposed voice activity detection technique in a real-time voice processing embedded system. Experimental and simulation results show that the introduced procedure provides more reliable results with a reasonable amount of computational complexity in comparison with its... 

While pitch detection has been the focal subject of numerous research efforts for several decades, it is still a challenging task in noisy conditions. In this article, we propose a method to improve the pitch detection accuracy of conventional pitch detection methods. The proposed pitch detection process starts with using the pitch value estimated by a conventional pitch detection method. Then, it extracts pitch candidates according to the most probable types of errors in the initial estimation of high-pitch and low-pitch frames classified by a Deep Convolutional Neural Network (DCNN). Next, a restrained selection procedure is run to find the true pitch value from the set of pitch... 

Multiple target detection in conventional PCL (Passive Coherent Location) radar systems by means of Ambiguity Function Processing and without any powerful algorithm is impossible. Presented Gradual Clean Algorithm (GCA) is a new and simple method, which could provide the perfect detection in noisy environment and heavy clutter condition up to SNR=-15 dB and SCR=-100 dB (clutter is assumed as a distributed target with large RCS). Determining the number of real targets and keeping the false alarm probability approximately constant are the presented algorithm capabilities  

The problem of time delay estimation (TDE) in the presence of zero mean white Gaussian noise is addressed. A novel and efficient algorithm for obtaining Conditional Maximum Likelihood (CML) estimates of delays, attenuation factors and the number of paths from a transmitter to a receiver is introduced. This approach is quite suitable for the cases in which time delay difference is less than the duration of signal autocorrelation  

Vacuum arc is one of the most important failure factors of the vacuum tubes. The amount of delivered energy from the high-voltage dc power supply to the vacuum tube is an important issue during the vacuum arc in the tube. Vacuum arc acts as a short-circuit fault (SCF) at the power supply output. The majority of converters use a single current sensor to measure only the converter output current for detecting the SCF. However, the sensor may provide unreliable data because of the noise effect. Application of a low-pass filter reduces the noise effect. Regarding the delay of the low-pass filter, the interval of arc detection increases and more energy is delivered to the tube. In this article, a... 

The challenge of finding an optimized and reliable path dates back to emersion of mobile robots. Several approaches have been developed that have partially answered this need. Satisfying results in previous implementations has led to an increased utilization of sampling-based motion planning algorithms in recent years, especially in high degrees of freedom (DOF), fast evolving environments. Another advantage of these algorithms is their probabilistic completeness that guarantees delivery of a path in sufficient time, if one exists. On the other hand, sampling based motion planners leave no comment on safety of the planned path. This paper suggests biasing the Rapidly-exploring Random Trees... 

Adaptive Noise Cancellation (ANC) is an effective dual-channel technique for background noise reduction. Due to the presence of uncorrelated noise components at the two inputs in vehicular environments, ANC does not provide sufficient background noise reduction. To alleviate this problem, a complementary linear filter is added to ANC structure. Filter coefficients are determined to make the enhanced signal an MMSE estimation of speech signal. Therefore, the ANC structure is modified to a dual-channel Wiener structure. We prove that this structure is identical to the LMS type ANC which is followed by a Wiener post-filter. A new method is proposed for the noise spectrum estimation in the... 

The problem of extracting low-dimensional structure from high-dimensional data arises in many applications such as machine learning, statistical pattern recognition, wireless sensor networks, and data compression. If the data is restricted to a lower dimensional subspace, then simple algorithms using linear projections can find the subspace and consequently estimate its dimensionality. However, if the data lies on a low-dimensional but nonlinear space (e.g., manifolds), then its structure may be highly nonlinear and, hence, linear methods are doomed to fail. In this paper, we introduce a new technique for dimensionality reduction based on point-wise operators. More precisely, let $ {bf A} n... 

Artificial Immune System (AIS) models have outstanding properties, such as learning, adaptivity and robustness, which make them suitable for learning in dynamic and noisy environments such as the web. In this study, we tend to apply AIS for tracking evolving patterns of web usage data. The definition of the similarity of web sessions has an important impact on the quality of discovered patterns. Many prevalent web usage mining approaches ignore the sequential nature of web navigations for defining similarity between sessions. We propose the use of a new web sessions' similarity measure for investigating the usage data from web access log files. In this similarity measure, in addition to the...