سال:1393 | دوره: | شماره: |تعداد مقالات:8

With the explosion of learning materials available on personal learning environments (PLEs) in the recent years, it is difficult for learners to discover the most appropriate materials according to keyword searching method. Recommender systems (RSs) that are used to support activity of learners in PLE can deliver suitable material to learners. This technology suffers from the cold-start and sparsity problems. On the other hand, in most researches, less attention has been paid to latent features of products. For improving the quality of recommendations and alleviating sparsity problem, this research proposes a latent feature based recommendation approach. Since usually there isn’t adequate information about the observed features of learner and material, latent features are introduced for addressing sparsity problem. First preference matrix (PM) is used to model the interests of learner based on latent features of learning materials in a multidimensional information model. Then, we use genetic algorithm (GA) as a supervised learning task whose fitness function is the mean absolute error (MAE) of the RS. GA optimizes latent features weight for each learner based on his/her historical rating. The method outperforms the previous algorithms on accuracy measures and can alleviate the sparsity problem. The main contributions are optimization of latent features weight using genetic algorithm and alleviating the sparsity problem to improve the quality of recommendation.

The disparity introduced between In-phase and Quadrature components in a digital communication system receiver known as I/Q imbalance is a prime objective within the employment of direct conversion architectures. It reduces the performance of channel estimation and causes to receive the data symbol with errors. This imbalance phenomenon, at its lowest still can result very serious signal distortions at the reception of an OFDM multi-carrier system. In this manuscript, an algorithm based on neural network scenario, is proposed that deploys both Long Training Symbols (LTS) as well as data symbols, to jointly estimate the channel and to compensate parameters that are damaged by I/Q imbalanced receiver. In this algorithm, we have a tradeoff between these parameters. I.e. when the minimum CG mean value is required, the minimum CG mean value could be chosen without others noticing it, but in usual case we have to take into account other parameters too, the limited values for the aimed parameters must be known. It uses the first iterations to train the system to reach the suitable value of GC without error floor. In this present article, it is assumed that the correlation between subcarriers is low and a few numbers of training and data symbols are used. The simulation results show that the proposed algorithm can compensate the high I/Q imbalance values and estimate channel frequency response more accurately compared with to date existing methods.

The superiority of multiple-input multiple-output (MIMO) radars over conventional radars has been recently shown in many aspects. These radars consist of many transmitters and receivers located far from each other. In this scenario, the MIMO radar is able to observe the targets from different directions. One of the advantages of these radars is exploitation of Doppler frequencies from different transmitter-target-receiver paths. The extracted Doppler frequencies can be used for estimation of target velocity vector so that, the radar can be able to track the targets by use of its velocity vector with reasonable accuracy. In this paper, two different processing systems are considered for MIMO radars. First one is the pulse Doppler system, and the second one is continuous wave (CW) system without range processing. The measurement of the velocity vector of the target and its counterpart errors are taken into account. Also, the extended Kalman target tracking by using its velocity vector is considered. Besides, its performance is compared with those of MIMO target tracking without using the velocity vector and conventional radars. The simulation results show that the MIMO radars using velocity vector have superior performance over other above-mentioned radars in fast maneuvering target tracking. Since the range processing is ignored in CW MIMO radar systems, the complexity of this system is much lower than that of Pulse Doppler MIMO radar system, but has lower performance in tracking fast maneuvering target.

This paper presents the method for detection and tracking airplanes which can be observed visually in low distances from sensors. They are used widely for some reasons such as military or unmanned aerial vehicle (UAV) because of their ability to hide from radar signals, however they can be detected and viewed by human eyes. Vision based methods are low cost and robust against jamming signals. Therefore, it is mandatory to have some visual approaches to detect airplanes. By this way, we propose spectral density for airplane detection and KLT algorithm for tracking. This approach is a hybrid of two distinct methods which have been presented by researchers and used widely in detection or tracking specific objects. To have accurate detection, image intensity would be adjusted adaptively. Correct detected airplanes would be achievable by eliminating some long optical flow trajectory in image frames. The proposed method would be analyzed and evaluated by comparison with state of the art approaches. The experimental results show the power of our approach in detection of multiple airplanes unless they become too small in presence of other objects and multiple airplanes. We make some test by implementing our approach on an useful database presented by some researchers.

In this paper, a 16-phases 20 MHz to 110MHz low jitter delay locked loop, DLL, is proposed in a 0.35 mm CMOS process. A sensitive open loop phase detector, PD, is introduced based on a novel idea to simply detect small phase differences between reference clock and generated delayed signals. High sensitivity, besides the simplicity reduces the dead zone of PD and gives a better jitter on output generated clock signals, consequently. A new strategy of common mode setting is utilized on differential delay elements which no longer introduce extra parasitics on output nodes and brings the duty cycle of generated clock signals near to 50 percent. Also, small amplitude differential clock is carefully transferred inside the circuit to considerably suppress the noise effect of supply voltage. Post-Layout simulation results confirm the RMS jitter of less than 6.7 ps at 20 MHz and 2 ps at 100 MHz input clock frequency when the 3.3 Volts supply voltage is subject to 75 mVolts peak-to-peak noise disturbances. Total power consumption reaches from 7.5 mW to 16.5mW when the operating frequency increases from 20MHz to 100MHz. The proposed low-jitter DLL can be implemented in small active area, around 380 mm × 210 mm including the clock generation circuit, which is proper to be repeatedly used inside the chip.

This research shows the influence of using multi-core architecture to reduce the execution time and thus increase performance of some software fault tolerance techniques. According to superiority of N-version Programming and Consensus Recovery Block techniques in comparison with other software fault tolerance techniques, implementations were performed based on these two methods. Finally, the comparison between the two methods listed above showed that the Consensus Recovery Block is more reliable. Therefore, in order to improve the performance of this technique, we propose a technique named Improved Consensus Recovery Block technique. In this research, satellite motion system which known as a scientific computing system is consider as a base for our experiments. Because of existing any error in calculation of system may result in defeat in system totally, it shouldn’t contains any error. Also the execution time of system must be acceptable. In our proposed technique, not only performance is higher than the performance of consensus recovery block technique, but also the reliability of our proposed technique is equal to the reliability of consensus recovery block technique. The improvement of performance is based on multi-core architecture where each version of software key units is executed by one core. As a result, by parallel execution of versions, execution time is reduced and performance is improved.

Every year thousands of the elderly suffer serious damages such as articular fractures, broken bones and even death due to their fall. Automatic detection of the abnormal walking in people, especially such accidents as the falls in the elderly, based on image processing techniques and computer vision can help develop an efficient system that its implementation in various contexts enables us to monitor people’s movements. This paper proposes a new algorithm, which drawing on fuzzy rules in classification of movements as well as the implementation of the motion estimation, allows the rapid processing of the input data. At the testing stage, a large number of video frames received from CASIA, CAVAIR databases and the samples of the elderly’s falls in Sabzevar’s Mother Nursing Home containing the falls of the elderly were used. The results show that the mean absolute percent error (MAPE), root-mean-square deviation (RMSD) and standard deviation error (SDE) were at an acceptable level. The main shortcoming of other systems is that the elderly need to wear bulky clothes and in case they forget to do so, they will not be able to declare their situation at the time of the fall. Compared to the similar techniques, the implementation of the proposed system in nursing homes and residential areas allow the real time and intelligent monitoring of the people.

Real time face recognition systems have several limitations such as collecting features. One training sample per target means less feature extraction techniques are available to use. To obtain an acceptable accuracy, most of face recognition algorithms need more than one training sample per target. In these applications, accuracy of recognition dramatically reduces for the case of one training sample per target face image because of head rotation and variation in illumination state. In this paper, a new hybrid face recognition method by using single image per person is proposed, which is robust against illumination variations. To achieve robustness against head variations, a rotation detection and compensation stage is added. This method is called Weighted Graphs and PCA (WGPCA). It uses harmony of face components to extract and normalize features, and genetic algorithm with a training set is used to learn the most useful features and real-valued weights associated to individual attributes in the features. The k-nearest neighbor algorithm is applied to classify new faces based on their weighted features from the templates of the training set. Each template contains the corrected distances (Graphs) of different points on the face components and the results of Principal Component Analysis (PCA) applied to the output of face detection rectangle. The proposed hybrid algorithm is trained using MATLAB software to determine best features and their associated weights and is then implemented by using delphi XE2 programming environment to recognize faces in real time. The main advantage of this algorithm is the capability of recognizing the face by only one picture in real time. The obtained results of the proposed technique on FERET database show that the accuracy and effectiveness of the proposed algorithm.