We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.

The binary exponential back-off mechanism is one of the basic elements that constitute the IEEE 802.11 protocol. The models of the back-off mechanism have been developed with the assumption that collisions occur only due to nodes within the carrier sensing range and the collision probability is constant in steady-state. However, the transmission collisions can occur due to hidden nodes and these tend to occur consecutively, contrary to the collisions due to nodes within the carrier sensing range. Consecutive collisions increase the back-off time exponentially, resulting in less frequent transmission attempts. Ignoring this collision characteristic in modeling the back-off mechanism can produce large errors in the performance analysis of networks. In this paper, we model the back-off process as a Markov renewal process by taking into account such consecutive collisions due to hidden nodes, and then compare this result with NS2 simulation results. According to the simulation results, the proposed model reduces the relative error in the attempt probability by more than 90% in the grid topology. We also propose a new collision model for a simple network considering consecutive collisions due to hidden nodes, and analyze the network under saturated traffic condition using the proposed models. The attempt and collision probabilities are estimated with high accuracy.

In this letter, a closed form of the sum rate for multiple random beamforming is derived. A numerical evaluation verifies the tightness of the proposed solution to sample average of the sum rate.

The optimization of nonlinear feedback fuzzy system using the product-sum-gravity method is described in this paper. The fuzzy control discussed here is the nonlinear feedback control in which the feedback laws are determined by IF-THEN type fuzzy production rules through product-sum-gravity method. To prove existence of optimal control, we applied compactness of a set of membership functions in L2 space and continuity of the approximate reasoning, and prepared some propositions concerning product-sum-gravity method. By considering fuzzy optimal control problems as problems of finding the minimum (maximum) value of the integral cost (benefit) function on an appropriate set of membership functions, the existence of fuzzy optimal control is shown.

Three-dimensional integrated circuits (3-D ICs), i.e., stacked dies, can alleviate the interconnect problem coming with the decreasing feature size and increasing integration density, and promise a solution to heterogenous integration. The vertical connection, which is generally implemented by the through-the-silicon via, is a key technology for 3-D ICs. In this paper, given 3-D circuit placement or floorplan results with white space reserved between blocks for inter-layer interconnections, we proposed methods for assigning inter-layer signal via locations. Introducing a grid structure on the chip, the inter-layer via assignment of two-layer chips can be optimally solved by a convex-cost max-flow formulation with signal via congestion optimized. As for 3-D ICs with three or more layers, the inter-layer signal via assignment is modeled as an integral min-cost multi-commodity flow problem, which is solved by a heuristic method based on the lagrangian relaxation. Relaxing the capacity constraints in the grids, we transfer the min-cost multi-commodity flow problem to a sequence of lagrangian sub-problems, which are solved by finding a sequence of shortest paths. The complexity of solving a lagrangian sub-problem is O(nntng2), where nnt is the number of nets and ng is the number of grids on one chip layer. The experimental results demonstrated the effectiveness of the method.

Noise removal in engineering drawing is an important operation performed before other image analysis tasks. Many algorithms have been developed to remove salt-and-pepper noise from document images. Cleaning algorithms should remove noise while keeping the real part of the image unchanged. Some algorithms have disadvantages in cleaning operation that leads to removing of weak features such as short thin lines. Others leave the image with hairy noise attached to image objects. In this article a noise removal procedure called TrackAndMayDel (TAMD) is developed to enhance the noise removal of salt-and-pepper noise in binary images of engineering drawings. The procedure could be integrated with third party algorithms' logic to enhance their ability to remove noise by investigating the structure of pixels that are part of weak features. It can be integrated with other algorithms as a post-processing step to remove noise remaining in the image such as hairy noise attached with graphical elements. An algorithm is proposed by incorporating TAMD in a third party algorithm. Real scanned images from GREC'03 contest are used in the experiment. The images are corrupted by salt-and-pepper noise at 10%, 15%, and 20% levels. An objective performance measure that correlates with human vision as well as MSE and PSNR are used in this experiment. Performance evaluation of the introduced algorithm shows better-quality images compared to other algorithms.

Complex bandpass ΔΣAD modulators can provide superior performance to a pair of real bandpass ΔΣAD modulators of the same order. They process just input I and Q signals, not image signals, and AD conversion can be realized with low power dissipation, so that they are desirable for such low-IF receiver applications. This paper proposes a new architecture for complex bandpass Δ ΣAD modulators with cross-noise-coupled topology, which effectively raises the order of the complex modulator and achieves higher SQNDR (Signal to Quantization Noise and Distortion Ratio) with low power dissipation. By providing the cross-coupled quantization noise injection to internal I and Q paths, noise coupling between two quantizers can be realized in complex form, which enhances the order of noise shaping in complex domain, and provides a higher-order NTF using a lower-order loop filter in the complex ΔΣAD modulator. Proposed higher-order modulator can be realized just by adding some passive capacitors and switches, the additional integrator circuit composed of an operational amplifier is not necessary, and the performance of the complex modulator can be effectively raised without more power dissipation. We have performed simulation with MATLAB to verify the effectiveness of the proposed architecture. The simulation results show that the proposed architecture can achieve the realization of higher-order enhancement, and improve SQNDR of the complex bandpass ΔΣAD modulator.

Unstructured overlay networks are widely adopted in large-scale and heterogeneous peer-to-peer (P2P) systems for their scalability and flexibility. A distinct feature of such systems is that they randomly route messages e.g., by flooding or random walk. In such systems, the number of messages and tasks carrying by those messages each peer receives is greatly affected by the number of the peer's incoming links. The objective of this paper is to build controllable degree-weighted networks in which the expected number of incoming links of each peer is proportional to its weight which is a local parameter. In such a network, a peer can control the number of those randomly disseminated messages and tasks it receives by adjust it weight. In addition, in order to bound the construction overhead for highly biased networks, we restrict all peers to have the same number of outgoing links. The objective network is constructed by local topology transformations that peers periodically exchange outgoing links with each other. A framework, which includes 81 different protocols by combination of exchange rules, is presented and evaluated by simulation. The simulation result shows that two of them can generate the networks having similar properties with the objective network. This work first achieves the weight-proportional degree control under the out-regular network model.

In this paper, we propose a distance-based howling canceller with high speech quality. We have developed a distance-based howling canceller that uses only distance information by noticing the property that howling occurs according to the distance between a loudspeaker and a microphone. This method estimates the distance by transmitting a pilot signal from the loudspeaker to the microphone. Multiple frequency candidates for each howling are computed from the estimated distance and eliminated by cascading notch filters that have nulls at them. However degradation of speech quality occurs at the howling canceller output. The first cause is a shot noise occurrence at the beginning and end of the pilot signal transmission due to the discontinuous change of the amplitude. We thus develop a new pilot signal that is robust against ambient noises. We can then reduce the shot noise effect by taking the amplitude small. The second one is a speech degradation caused from overlapped stopbands of the notch filters. We thus derive a condition on the bandwidths so that stopbands do not overlap, and propose an adaptive bandwidth scheme which changes the bandwidth according to the distance.

Sensor networks have promising applications such as battlefield surveillance, biological detection, and emergency navigation, etc. Crucial problems in sensor networks are energy-efficiency and collision avoidance in wireless communication. To deal with the problems, we consider a self-stabilizing solution to the construction of k disjoint sense-sleep trees, where range adjustment and the use of GPS are allowed. Each root is determined by its identifier and is distinguished by its color, the identification of a tree. Using a dominating k-partition rule, each non-root node first determines a color irrelevant to the root. Then, the non-root node determines a parent node that is equally colored with minimal distance. If there is no appropriate parent, the range is extended or shrunk until the nearest parent is determined. Finally, we perform a simulation.

The IEEE802.11b WLAN standard employs direct-sequence/spread-spectrum (DS/SS) modulation. With a fractional sampling RAKE receiver, it is possible to achieve diversity and reduce the BER in DS/SS communication. In order to realize the diversity through fractional sampling, the impulse response of the channel must be estimated. In this paper, a channel estimation scheme for a RAKE receiver with fractional sampling in IEEE802.11b WLAN system is investigated through a computer simulation and an experiment. In order to estimate the impulse response of the channel, a pseudo-inverse matrix with a threshold is employed. Numerical results indicate that the channel can be estimated with an optimum threshold in both the simulation and the experiment.

We describe a channel-vector quantization scheme that is suitable for multiple stream transmission per user in zero-forcing beamforming (ZFBF) multiuser multiple-input and multiple output (MU-MIMO) systems with finite rate feedback. Multiple subsets of a channel matrix are quantized to vectors from random vector codebooks for finite rate feedback. The quantization vectors with an angle difference that is closer to orthogonal are then selected and their indexes are fed back to the transmitter. Simulation results demonstrate that the proposed scheme achieves a better average throughput than that serving a single stream per user when the number of active users is smaller than the number of transmit antennas and that it provides an average throughput close to that serving a single stream per user when the number of users is equal to the number of transmit antennas.

This paper proposes a pilot-based channel state information (CSI) feedback from a terminal to a base station (BS), considering the terminal's co-channel interference in a time-division duplex/multi-input multi-output system. In the proposed method, the terminal determines a precoding matrix according to the terminal's co-channel interference characteristics and transmits the precoded pilot signals on uplink. Using the responses of the precoded pilot signals, the BS determines appropriate weight vectors for downlink transmit beams considering the terminal's interference characteristics. Furthermore, the BS can predict the terminal's output signal-to-interference-plus-noise power ratio (SINR) for the downlink data stream. Numerical results show that the BS can achieve efficient transmission and accurate SINR prediction using the proposed CSI feedback.

Communication-free Petri nets provide a net semantics for Basic Parallel Processes, which form a subclass of Milner's Calculus of Communicating Systems (CCS) a process calculus for the description and algebraic manipulation of concurrent communicating systems. It is known that the reachability problem for communication-free Petri nets is NP-complete. Lacking the synchronization mechanism, the expressive power of communication-free Petri nets is somewhat limited. It is therefore importance to see whether the power of communication-free Petri nets can be enhanced without sacrificing their analytical capabilities. As a first step towards this line of research, in this paper our main concern is to investigate, from the decidability/complexity viewpoint, the reachability problem for a number of variants of communication-free Petri nets, including communication-free Petri nets augmented with 'static priorities,' 'dynamic priorities,' 'states,' 'inhibitor arcs,' and 'timing constraints.'

It is a tedious and subjective task to measure severity of a dysarthria by manually evaluating his/her speech using available standard assessment methods based on human perception. This paper presents an automated approach to assess speech quality of a dysarthric speaker with cerebral palsy. With the consideration of two complementary factors, speech consistency and speech distinction, a speech quality indicator called speech clarity index (Ψ) is proposed as a measure of the speaker's ability to produce consistent speech signal for a certain word and distinguished speech signal for different words. As an application, it can be used to assess speech quality and forecast speech recognition rate of speech made by an individual dysarthric speaker before actual exhaustive implementation of an automatic speech recognition system for the speaker. The effectiveness of Ψ as a speech recognition rate predictor is evaluated by rank-order inconsistency, correlation coefficient, and root-mean-square of difference. The evaluations had been done by comparing its predicted recognition rates with ones predicted by the standard methods called the articulatory and intelligibility tests based on the two recognition systems (HMM and ANN). The results show that Ψ is a promising indicator for predicting recognition rate of dysarthric speech. All experiments had been done on speech corpus composed of speech data from eight normal speakers and eight dysarthric speakers.

The performance of speech translation systems combining automatic speech recognition (ASR) and machine translation (MT) systems is degraded by redundant and irrelevant information caused by speaker disfluency and recognition errors. This paper proposes a new approach to translating speech recognition results through speech consolidation, which removes ASR errors and disfluencies and extracts meaningful phrases. A consolidation approach is spun off from speech summarization by word extraction from ASR 1-best. We extended the consolidation approach for confusion network (CN) and tested the performance using TED speech and confirmed the consolidation results preserved more meaningful phrases in comparison with the original ASR results. We applied the consolidation technique to speech translation. To test the performance of consolidation-based speech translation, Chinese broadcast news (BN) speech in RT04 were recognized, consolidated and then translated. The speech translation results via consolidation cannot be directly compared with gold standards in which all words in speech are translated because consolidation-based translations are partial translations. We would like to propose a new evaluation framework for partial translation by comparing them with the most similar set of words extracted from a word network created by merging gradual summarizations of the gold standard translation. The performance of consolidation-based MT results was evaluated using BLEU. We also propose Information Preservation Accuracy (IPAccy) and Meaning Preservation Accuracy (MPAccy) to evaluate consolidation and consolidation-based MT. We confirmed that consolidation contributed to the performance of speech translation.

The usable throughput of an IEEE 802.11 system for an application is much less than the raw bandwidth. Although 802.11b has a theoretical maximum of 11 Mbps, more than half of the bandwidth is consumed by overhead leaving at most 5 Mbps of usable bandwidth. Considering this characteristic, this paper proposes and analyzes a real-time distributed scheduling scheme based on the existing IEEE 802.11 wireless ad-hoc networks, using USC/ISI's Power Aware Sensing Tracking and Analysis (PASTA) hardware platform. We compared the distributed real-time scheduling scheme with the real-time polling scheme to meet deadline, and compared a measured real bandwidth with a theoretical result. The theoretical and experimental results show that the distributed scheduling scheme can guarantee real-time traffic and enhances the performance up to 74% compared with polling scheme.

For statistical language model training, target domain matched corpora are required. However, training corpora sometimes include both target domain matched and unmatched sentences. In such a case, training set selection is effective for both reducing model size and improving model performance. In this paper, training set selection method for statistical language model training is described. The method provides two advantages for training a language model. One is its capacity to improve the language model performance, and the other is its capacity to reduce computational loads for the language model. The method has four steps. 1) Sentence clustering is applied to all available corpora. 2) Language models are trained on each cluster. 3) Perplexity on the development set is calculated using the language models. 4) For the final language model training, we use the clusters whose language models yield low perplexities. The experimental results indicate that the language model trained on the data selected by our method gives lower perplexity on an open test set than a language model trained on all available corpora.

We present a recovery scheme based on Self-protected Spanning Tree (SST), which recovers from failure all by itself. In the recovery scheme, the links are assigned birthdays to denote the order in which they are to be considered for adding to the SST. The recovery mechanism, named Birthday-based Link Replacing Mechanism (BLRM), is able to transform a SST into a new spanning tree by replacing some tree links with some non-tree links of the same birthday, which ensures the network connectivity after any single link or node failure. First, we theoretically prove that the SST-based recovery scheme can be applied to arbitrary two-edge connected or two connected networks. Then, the recovery time of BLRM is analyzed and evaluated using Ethernet, and the simulation results demonstrate the effectiveness of BLRM in achieving fast recovery. Also, we point out that BLRM provides a novel load balancing mechanism by fast changing the topology of the SST.

Liquid-phase detection of biological targets utilizing magnetic marker and superconducting quantum interference device (SQUID) magnetometer is shown. In this method, magnetic markers are coupled to the biological targets, and the binding reaction between them is detected by measuring the magnetic signal from the bound markers. Detection can be done in the liquid phase, i.e., we can detect only the bound markers even in the presence of unbound (free) markers. Since the detection principle is based on the different magnetic properties between the free and bound markers, we clarified the Brownian relaxation of the free markers and the Neel relaxation of the bound markers. Usefulness of the present method is demonstrated from the detection of the biological targets, such as biotin-coated polymer beads, IgE and Candida albicans.