Binary Decision Diagram (in short BDD) is an efficient data structure which has been used widely in computer science and engineering. BDD-based attack in key stream cryptanalysis is one of the best forms of attack in its category. In this paper, we propose a new key stream attack which is based on ZDD (Zero-suppressed BDD). We show how a ZDD-based key stream attack is more efficient in time and space complexity over its BDD-based variant against the E0 type of the Bluetooth security mechanism. We implemented it by using the CUDD - Colorado University Decision Diagram package. Experimental results show great improvements. We have also derived a mathematical proof, which shows that it is better than the BDDbased attack method even for the worst case analysis.

Median filters are commonly used for removal of the impulse noise from images. De-noising is a preliminary step in online processing of images, thus hardware implementation of median filters is of great interest. Hence, many methods, mostly based on sorting the pixels, have been developed to implement median filters. Utilizing vast amount of hardware resources and not being fast are the two main disadvantages of these methods. In this paper a method for filtering images is proposed to reduce the needed hardware elements. A modular pipelined median filter unit is first modeled and then the designed module is used in a parallel structure. Since the image is applied in rows and in a parallel manner, the amount of necessary hardware elements is reduced in comparison with other hardware implementation methods. Also, image filtering speed has increased. Implementation results show that the proposed method has advantageous speed and efficiency.

High Efficiency Video Coding (HEVC) is considered the last standard for video compression with about 50% additional compression compared to the previous standard i. e. H264/AVC, while maintaining image quality. The significant increase in performance of this standard has been achieved with high computational complexity. In this standard, the intra prediction unit is one of the parts that although it greatly improves performance, it significantly increases its computational complexity due to the increase in the number of prediction modes. In this paper, a method has been proposed to reduce the number of intra prediction modes in HEVC by which the computational complexity of compression at this stage can be reduced as much as possible. The proposed method determines the predominant mode of 4 × 4 blocks, and the details in each larger block, and accordingly, by applying the appropriate filters and selecting the most likely mode, the number of candidate modes to select the best mode is reduced. The simulation results showed that on average the proposed method can reduce the compression time by 45% while increasing the bit rate by 0. 69%.

This research puts forward the rough finite state automata which have been represented by two variants of Binary Decision Diagram (BDD) called ROBDD and ZBDD, for networked system diagnosis. Using the suggested data structures can help us overcome the combinatorial explosion which usually occurs in system diagnosis. In implementations and analysis of our experimental results, we used CUDD-Colorado University Decision Diagram package. A mathematical proof for the claimed complexity is provided which shows that ZBDD representation has superiority in space and time complexity to ROBDD representation.v

This paper is about multiplicity in curriculum theory. First, Paper explained the place of curriculum theory between other disciplines. Then, paper presented the characteristics and structure of theory and theorizing in curriculum. Since explanation of nature and diversity in curriculum theory and reasons, argument is that some sorts of reduction in concept of curriculum theory and theorizing and also assumption of the curriculum as a complexity construct, causing the plurality of voices in the curriculum theories, leading to a wide range of what called ‘ theory’ that on one hand reaches to the control-based approaches in the technical orientation camp, and on the other hand to the emancipatory-based views, metaphor and allegory are in reconceptualists are community. Finally, examples of conceptual reductionism and how to deal with complexity has provided, and has concluded that conceptual and complexity reduction, accompanying with celebration ofcomplexity, have provided the context for theoretical generation incurriculum.

One of the main reasons for switching to the next generation of communication systems is the demand of increasing capacity and network connections. This goal can be achieved using massive multiple input-multiple output (massive-MIMO) systems in combination with Non-orthogonal multiple access (NOMA) technique. NOMA technology uses the successive interference cancellation (SIC) receiver to detect user’ s signals which imposes an additional complexity on the system. In this paper, we proposed two methods to reduce the system complexity. The proposed method despite imperfect channel state information (CSI) in the receiver, there is not significantly reduction in the system performance. Since the computation of matrices inverse has a high computational complexity, we used the Neumann series approximation method and the Gauss-Seidel decomposition method to compute matrices inverse in the SIC receiver. Simulation results are provided at the end of the paper in terms of bit error rate (BER) at the receiver which show, these methods have lower computational complexity in comparison with the traditional methods while they cause a slight performance reduction in the SIC receiver. Also, we examined the increasing and decreasing value of imperfect channel state information in the system performance which shows the increasing value of imperfect channel state information, cause a slight performance reduction in SIC receiver.

Cooperative positioning utilizes information received from all the nodes in a network to estimate the position of a target node. This requires high amount of data exchange and data processing in high density networks. This paper proposes a low computational complexity algorithm to select a number of nodes among all possible nodes to cooperate in position finding. Position of nodes are estimated using both the distances between the target node and its cooperated nodes and also the information shared by these nodes. The nodes selection algorithm is proposed according to the Cramer-Rao Lower Bound, which considers the precision of distance measurements, the geometry of nodes and the uncertainty in the information shared by nodes. This fast computing algorithm reduces required computations without significantly decreasing the position estimation performance.

Inertial Navigation System (INS) is one of the navigation systems widely used in various land-based, aerial, and marine applications. Among all types of INS, Microelectromechanical System (MEMS)-based INS can be widely utilized, owing to their low cost, lightweight, and small size. However, due to the manufacturing technology, MEMS-based INS suffers from deterministic and stochastic errors, which increase positioning errors over time. In this paper, a new effective noise reduction method is proposed that can provide more accurate outputs of MEMS-based inertial sensors. The intelligent method in this paper is a combined denoising method that combines Wavelet Transform (WT), Permutation Entropy (PE), Support Vector Regression (SVR), and Genetic Algorithm (GA). Firstly, WT is employed to obtain a time-frequency representation of raw data. Secondly, a four-element feature vector is formed. These four features are (1) amplitude of frequency, (2) its ratio to mean of amplitudes of all frequencies, (3) location of frequency in time-frequency representation, and (4) judgment on behaviors of frequency that is obtained by utilizing PE. Thirdly, based on the feature vector, the GA-SVR algorithm predicts amplitudes of all frequencies in the time-frequency representation of the denoised signal. Finally, by employing inverse WT the denoised signal is obtained. In this work, the outputs of the Inertial Measurement Unit (IMU) in ADIS16407 sensor, as a low-cost and MEMS-based INS, have been utilized for data collection. The proposed method has been compared with other noise reduction methods and the achieved results verify superior improvement than other methods.

Background & Aims: Tic disorders are a group of conditions with onset in the developmental period.The disorders produce impairments of functioning. The goal of this study was to investigate the effectiveness of habit reversal techniques training (HRT) on reduction of intensity, frequency, complexity and interference of tourette syndrome and motor tics.Materials & Methods: This study was conducted on three boys, with tourette syndrome (1 boy) and motor tic (2 boys) who were selected by available sampling from Psychiatric Clinic Bozorgmehr in Tabriz city. Each of the participants received habit reversal techniques training. Patients participated in 8 treatment sessions of HRT for 4 weeks. The resent study was performed in framework of empirical plan case study using single base line plan. Yale Global Tic Severity Scale (YGTSS, 1989) was used to evaluate participants.Results: The results of the present study showed that the effectiveness of habit reversal technique training reduced the intensity, frequency, complexity and interference of tourette syndrome and motor tics. Follow-up for one month after the end of treatment showed the persistence of these results.Conclusion: Accordingly, children with tic disorders and their families can learn behavioral techniques to reduce and improve the collection of the tic disorders substantially. Habit reversal training technique is effective in treatment of tic disorders.