In this paper we present a cognitive architecture inspired on the biological functioning of the motor system in humans. To test the model, we built a robotic hand with a Lego Mindstorms™ kit. Then, through communication between the architecture and the robotic hand, the latter was able to perform the movement of the fingers, which therefore allowed it to perform grasping of some objects. In order to obtain these results, the architecture performed a conversion of the activation of motor neuron pools into specific degrees of servo motor movement. In this case, servo motors acted as muscles, and degrees of movement as exerted muscle force. Finally, this architecture will be integrated with high-order cognitive functions towards getting automatic motor commands generation, through planning and decision making mechanisms.

We discuss a new high performance computing service (HPCS) platform that has been developed to provide domain-neutral computing service under the governmental support from “EDucation-research Integration through Simulation On the Net” (EDISON) project. With a first focus on technical features, we not only present in-depth explanations of the implementation details, but also describe the strengths of the EDISON platform against the successful nanoHUB.org gateway. To validate the performance and utility of the platform, we provide benchmarking results for the resource virtualization framework, and prove the stability and promptness of the EDISON platform in processing simulation requests by analyzing several statistical datasets obtained from a three-month trial service in the initiative area of computational nanoelectronics. We firmly believe that this work provides a good opportunity for understanding the science gateway project ongoing for the first time in Republic of Korea, and that the technical details presented here can be served as an useful guideline for any potential designs of HPCS platforms.

The Lee complete ρ weight enumerator and the exact complete ρ weight enumerator over Mn×s(Fl+vFl+v2Fl)(v3=v) are defined, and the MacWilliams identities with respect to RT metric for these two weight enumerators of linear codes over Mn×s(Fl+vFl+v2Fl) are obtained, respectively. Finally, we give two examples to illustrate the obtained results.

In this paper, we propose a new matting algorithm using local and nonlocal neighbors. We assume that K nearest neighbors satisfy the color line model that RGB distribution of the neighbors is roughly linear and combine this assumption with the local color line model that RGB distribution of local neighbors is roughly linear. Our assumptions are appropriate for various regions such as those that are smooth, contain holes or have complex color. Experimental results show that the proposed method outperforms previous propagation-based matting methods. Further, it is competitive with sampling-based matting methods that require complex sampling or learning methods.

We theoretically analyze the performance of free-space optical (FSO) systems using cooperative-ARQ (C-ARQ), a joint scheme of automatic-repeat-request (ARQ) and cooperative diversity, over atmospheric turbulence channels. We also propose a modified C-ARQ (M-C-ARQ) scheme that allows relay nodes to store a copy of frames for the more efficient response to transmission failure so that both transmission delay and energy consumption can be improved. Using Markov chain-based analytical models for both schemes, the system performance is analytically studied in terms of frame-error rate, goodput and energy efficiency, which directly reflect the transmission delay and energy consumption. Numerical results confirm that the proposed schemes outperform conventional ones. In addition, we discuss cross-layer design strategies for selecting parameters in both physical and link layers in order to optimize the performance of FSO systems over different atmospheric turbulence conditions and channel distances.

An integrated 2×28Gb/s dual-channel duobinary driver IC is presented. Each channel has integrated coding blocks, transforming a non-return-to-zero input signal into a 3-level electrical duobinary signal to achieve an optical duobinary modulation. To the best of our knowledge this is the fastest modulator driver including on-chip duobinary encoding and precoding. Moreover, it only consumes 652mW per channel at a differential output swing of 6Vpp.

This paper presents a data gathering protocol for wireless sensor network applications that require high throughput and topology adaptability under the premises of uniform traffic and energy-rich environments. Insofar as high throughput is concerned, TDMA is more suitable than CSMA. However, traditional TDMA protocols require complex scheduling of transmission time slots. The scheduling burden is the primary barrier to topology adaptability. Under the premises of uniform traffic and energy-rich environments, this paper proposes a token-scheduled multi-channel TDMA protocol named TKN-TWN to ease the scheduling burden while exploiting the advantages of TDMA. TKN-TWN uses multiple tokens to arbitrate data transmission. Due to the simplified scheduling based on tokens, TKN-TWN is able to provide adaptability for topology changes. The contention-free TDMA and multi-channel communication afford TKN-TWN the leverage to sustain high throughput based on pipelined packet forwarding. TKN-TWN further associates the ownership of tokens with transmission slot assignment toward throughput optimization. We implement TKN-TWN on Tmote Sky with TinyOS 2.1.1 operating system. Experimental results in a deployed network consisting of 32 sensor nodes show that TKN-TWN is robust to network changes caused by occasional node failures. Evaluation also shows that TKN-TWN is able to provide throughput of 9.7KByte/s.

By exploiting the inherent sparsity of wireless propagation channels, the theory of compressive sensing (CS) provides us with novel technologies to estimate the channel state information (CSI) that require considerably fewer samples than traditional pilot-aided estimation methods. In this paper, we describe the block-sparse structure of the fast time-varying channel and apply the model-based CS (MCS) for channel estimation in orthogonal frequency division multiplexing (OFDM) systems. By exploiting the structured sparsity, the proposed MCS-based method can further compress the channel information, thereby allowing a more efficient and precise estimation of the CSI compared with conventional CS-based approaches. Furthermore, a specific pilot arrangement is tailored for the proposed estimation scheme. This so-called random grouped pilot pattern can not only effectively protect the measurements from the inter-carrier interference (ICI) caused by Doppler spreading but can also enable the measurement matrix to meet the conditions required for MCS with relatively high probability. Simulation results demonstrate that our method has good performance at high Doppler frequencies.

Most existing outlier detection algorithms only utilized location of trajectory points and neglected some important factors such as speed, acceleration, and corner. To address this problem, we present a Trajectory Outlier Detection algorithm based on Multi-Factors (TODMF). TODMF is improved in terms of distance-based outlier detection algorithms. It combines multi-factors into outlier detection to find more meaningful trajectory outliers. We resort to Canonical Correlation Analysis (CCA) to optimize the number of factors when determining what factors will be considered. Finally, the experiments with real trajectory data sets show that TODMF performs efficiently and effectively when applied to the problem of trajectory outlier detection.

This paper proposes a novel multimodal interactive surgical simulator that incorporates haptic, olfactory, as well as traditional vision feedback. A scent diffuser was developed to produce odors when errors occur. Haptic device was used to provide the sense of touch to the user. The preliminary results show that adding smell as an aid to the simulation enhanced the memory retention that lead to better performance.

This paper proposes a new address method to reduce the address power consumption in an AC plasma panel display (AC-PDP). We apply an overlap scan method, in which the scan pulse overlaps with those of the previous scan time and the next scan time. The overlap scan method decreases the address voltage and consequently reduces the address power consumption. However, the drawback of this method is the narrow address voltage margin. This occurs because the maximum address voltage decreases much more than the minimum address voltage does. In order to increase the address voltage margin, we apply a two-step address voltage waveform, in the overlap scan method. In this case, the maximum address voltage increases; however, the minimum address voltage is almost the same. This leads to a wide address voltage margin. Moreover, the two-step address voltage waveform reduces the address power consumption, because the address voltage rises and falls in two steps using an energy recovery capacitor. Consequently, the experimental results show that the new address method reduces the address power consumption by 19.6,Wh (58%) when compared with the conventional method.

We present the adaptation of the acoustic models of hidden Markov models (HMMs) to the target speaker and noise environment using bilinear models. Acoustic models trained from various speakers and noise conditions are decomposed to build the bases that capture the interaction between the two factors. The model for the target speaker and noise is represented as a product of bases and two weight vectors. In experiments using the AURORA4 corpus, the bilinear model outperforms the linear model.

The use of cooperative nodes is effective for enhancing the reliability of wireless data transmission between a source and a destination by means of transmit diversity effect. However, in its application to wireless multi-hop networks, how to form cooperative node candidates and how to select multiple cooperative nodes out of them have not been well investigated. In this paper, we propose a multiple cooperative node selection method based on a criterion composed of “quality” and “angle” metrics, which can select and order adequate cooperative nodes. Computer simulation results show that the proposed method can effectively reduce the packet error rate without any knowledge on node location.

A smoothing method for minimum phone error linear regression (MPELR) is proposed in this paper. We show that the objective function for minimum phone error (MPE) can be combined with a prior mean distribution. When the prior mean distribution is based on maximum likelihood (ML) estimates, the proposed method is the same as the previous smoothing technique for MPELR. Instead of ML estimates, maximum a posteriori (MAP) parameter estimate is used to define the mode of prior mean distribution to improve the performance of MPELR. Experiments on a large vocabulary speech recognition task show that the proposed method can obtain 8.4% relative reduction in word error rate when the amount of data is limited, while retaining the same asymptotic performance as conventional MPELR. When compared with discriminative maximum a posteriori linear regression (DMAPLR), the proposed method shows improvement except for the case of limited adaptation data for supervised adaptation.

Recently, we proposed a weak-form discretization scheme to derive second-order difference equations from the governing equation of the scattering problem. In this paper, under the scope of the proposed scheme, numerical expressions for the waveguide boundary conditions are derived as perfectly absorbing conditions for input and output ports. The waveguide boundary conditions play an important role in extracting the quasi-localized wave as an eigenstate with a complex eigenvalue. The wave-number dependence of the resonance curve in Fano resonance is reproduced by using a semi-analytic model that is developed on the basis of the phase change relevant to the S-matrix. The reproduction confirms that the eigenstate with a complex eigenvalue does cause the observed Fano resonance.

Cognitive radio is one of the most promising wireless technologies and has been recognized as a new way to improve the spectral efficiency of wireless networks. In a cognitive radio network, secondary users exchange control information for network coordination such as transmitter-receiver handshakes, for sharing spectrum sensing results, for neighbor discovery, to maintain connectivity, and so on. Spectrum utilization and resource optimizations thus rely on information exchange among secondary users. Normally, secondary users exchange the control information via a predefined channel, called a common control channel (CCC). Most of the medium access control (MAC) protocols for cognitive radio networks were designed by assuming the existence of a CCC, and further assuming that it was available for every secondary user. However, the main drawback of using a static CCC is it is susceptible to primary user activities since the channel can be occupied by primary users at any time. In this paper, we propose a MAC protocol for cognitive radio networks with dynamic control channel assignment, called DYN-MAC. In DYN-MAC, a control channel is dynamically assigned based on spectrum availability. Thus, it can tolerate primary user activities. DYN-MAC also supports collision free network-wide broadcasting and addresses other major problems such as primary/secondary user hidden terminal problems.

This paper proposes the assignment of resource blocks (RBs) to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) in a multi-user OFDM system. This system ranks the users according to the channel state information (CSI) for RB assignment. In our proposed technique, an RB is assigned to either the first- or second-ranked mobile station (MS) to minimize the PAPR of the OFDM signal. While this process reduces the PAPR, the throughput is also reduced because of the user diversity gain loss. A PAPR-throughput tradeoff is then established. Theoretical analyses and computer simulations confirm that when the number of MSs becomes large, the PAPR-throughput tradeoff is eased because of the minimal effect of the diversity gain loss. Therefore, significant PAPR reduction is achieved with only a slight degradation in the throughput.

Curve extension is a useful function in shape modeling for cyberworlds, while a Ball B-spline Curve (BBSC) has its advantages in representing freeform tubular objects. In this paper, an extension algorithm for the BBSC with G2-continuity is investigated. We apply the extending method of B-Spline curves to the skeleton of the BBSC through generalizing a minimal strain energy method from 2D to 3D. And the initial value of the G2-continuity parameter for the skeleton is selected by minimizing the approximate energy function which is a problem with O(1) time complexity. The corresponding radius function of the control ball points is determined through applying the G2-continuity conditions for the skeleton to the scalar function. In order to ensure the radii of the control ball points are positive, we make a decision about the range of the G2-continuity parameter for the radius and then determine it by minimizing the strain energy in the affected area. Some experiments comparing our method with other methods are given. And at the same time, we present the advantage of our method in modeling flexibility from the aspects of the skeleton and radius. The results illustrate our method for extending the BBSC is effective.

Metaheuristic methods have been studied for combinational optimization problems for some time. Recently, a Consultant-Guided Search (CGS) has been proposed as a metaheuristic method for the Traveling Salesperson Problem (TSP). This approach is an algorithm in which a virtual person called a client creates a solution based on consultation with a virtual person called a consultant. In this research, we propose a parallel algorithm which uses the Ant Colony System (ACS) to create a solution with a consultant in a Consultant-Guided Search, and calculate an approximation solution for the TSP. Finally, we execute a computer experiment using the benchmark problems (TSPLIB). Our algorithm provides a solution with less than 2% error rate for problem instances using less than 2000 cities.

We give some attacks on the DBL hash modes MDC-4 and MJH. Our preimage attack on the MDC-4 hash function requires the time complexity O(23n/2) for the block length n of the underlying block cipher, which significantly improves the previous results. Our collision attack on the MJH hash function has a time complexity less than 2124 for n=128. Our preimage attack on the the MJH compression function finds a preimage with the time complexity of 2n. It is converted to a preimage attack on the hash function with the time complexity of O(23n/2). As far as we know, any cryptanalytic result for MJH has not been published before. Our results are helpful for understanding the security of the hash modes together with their security proofs.