Cross-modal integration processes are essential for service robots to reliably perceive relevant parts of the partially known unstructured environment. We demonstrate how multimodal integration on different abstraction levels leads to reasonable behavior that would be difficult to achieve with unimodal approaches. Sensing and acting modalities are composed to multimodal robot skills via a fuzzy multisensor fusion approach. Single modalities constitute basic robot skills that can dynamically be composed to appropriate behavior by symbolic planning. Furthermore, multimodal integration is exploited to answer relevant queries about the partially known environment. All these approaches are successfully implemented and tested on our mobile service robot platform TASER.

Multimodal documents combining language and graphs are wide-spread in print media as well as in electronic media. One of the most important tasks to be solved in comprehending graph-text combinations is construction of causal chains among the meaning entities provided by modalities. In this study we focus on the role of annotation position and shape of graph lines in simple line graphs on causal attributions concerning the event presented by the annotation and the processes (i.e. increases and decreases) and states (no-changes) in the domain value of the graphs presented by the process-lines and state-lines. Based on the experimental investigation of readers’ inferences under different conditions, guidelines for the design of multimodal documents including text and statistical information graphics are suggested. One suggestion is that the position and the number of verbal annotations should be selected appropriately, another is that the graph line smoothing should be done cautiously.

The sparse representation-based classification algorithm has been used for human face recognition. But an image database was restricted to human frontal faces with only slight illumination and expression changes. Cropping and normalization of the face needs to be done beforehand. This paper uses a sparse representation-based algorithm for generic image classification with some intra-class variations and background clutter. A hierarchical framework based on the sparse representation is developed which flexibly combines different global and local features. Experiments with the hierarchical framework on 25 object categories selected from the Caltech101 dataset show that exploiting the advantage of local features with the hierarchical framework improves the classification performance and that the framework is robust to image occlusions, background clutter, and viewpoint changes.

This paper presents a method for locating text based on a simplified pulse coupled neural network (PCNN). The PCNN generates a firings map in a similar way to the human visual system with non-linear image processing. The PCNN is used to segment the original image into different planes and edges detected using both the PCNN firings map and a phase congruency detector. The different edges are integrated using an automatically adjusted weighting coefficient. Both the simplified PCNN and the phase congruency energy model in the frequency domain imitate the human visual system. This paper shows how to use PCNN by changing the compute space from the spatial domain to the frequency domain for solving the text location problem. The algorithm is a simplified PCNN edge-based (PCNNE) algorithm. Three comparison tests are used to evaluate the algorithm. Tests on large data sets show PCNNE efficiently detects texts with various colors, font sizes, positions, and uneven illumination. This method outperforms several traditional methods both in text detection rate and text detection accuracy.

A neural network fire detection method was developed using detection information for temperature, smoke density, and CO concentration to determine the probability of three representative fire conditions. The method overcomes the shortcomings of domestic fire alarm systems using single sensor information. Test results show that the identification error rates for fires, smoldering fires, and no fire are less than 5%, which greatly reduces leak-check rates and false alarms. This neural network fire alarm system can fuse a variety of sensor data and improve the ability of systems to adapt in the environment and accurately predict fires, which has great significance for life and property safety.

Cayley graphs have many good properties as models of communication networks. This study analyzes the reliability of the Cayley graph based on the dihedral graph. Graph theory and analyses show that almost all Cayley graphs of the dihedral graph D_2n are optimal super-λ. The number N_i(G) of cutsets of size i,λ≤i≤λ′ is given as

This paper presents a multilevel hypergraph partitioning method that balances constraints on not only the cell area but also the wire weight with a partition-based global placement algorithm that maximizes the wire density uniformity to control chemical-mechanical polishing (CMP) variations. The multilevel partitioning alternately uses two FM variants in the refinement stage to give a more uniform wire distribution. The global placement is based on a top-down recursive bisection framework. The partitioning algorithm is used in the bisectioning to impact the wire density uniformity. Tests show that, with a 10% constraint, the partitioning produces solutions with more balanced edge weights that are 837% better than from hMetis, 1039.1% better than MLPart, and 762.9% better than FM in terms of imbalance proportion and that this global placement algorithm improves ROOSTER with a more uniform wire distribution by 3.1% on average with an increased wire length of only 3.0%.

Mutual information (MI) is a basic concept in information theory. Therefore, estimates of the MI are fundamentally important in most information theory applications. This paper provides a new way of understanding and estimating the MI using the copula function. First, the entropy of the copula, named the copula entropy, is defined as a measure of the dependence uncertainty represented by the copula function and then the MI is shown to be equivalent to the negative copula entropy. With this equivalence, the MI can be estimated by first estimating the empirical copula and then estimating the entropy of the empirical copula. Thus, the MI estimate is an estimation of the entropy, which reduces the complexity and computational requirements. Tests show that the method is more effective than the traditional method.

As a promising approach to improve network reliability, proactive failure recovery (PFR) re-routes failure affected traffic to backup paths without waiting for the completion of IP routing convergence. However, the failure affected traffic may cause congestion if it is not carefully allocated over the backup paths according to their available capacity. A post failure traffic engineering (PostTE) scheme is proposed to balance the load in the PFR scheme. Loop-free backup paths are prepared in advance to cover all the potential single-link failures. The failure affected load is locally allocated to the backup paths through solving a linear programming (LP) problem. Most of the time, the maximum link utilization (MLU) of the network is minimized under both the failure and failure-free cases. For the tested education networks, the LP problem can be solved within milliseconds.

Divide-and-conquer methods for FPGA placement algorithms including partition-based and cluster-based algorithms have shown the importance of good quality-runtime trade-off. This paper describes a cluster-based FPGA placement algorithm targeted to a new commercial hierarchical FPGA device. The algorithm is based on a Markov clustering algorithm that defines a sequence of stochastic matrices operating on a generating matrix from the input FPGA circuit netlist. The core of the algorithm tightly couples a Markov clustering process with a multilevel placement process. Tests show its excellent adaptability to hierarchical FPGAs. The average wirelength results produced by the algorithm are 22.3% shorter than the results produced by the current hierarchical FPGA placer.

Information is an important issue in financial markets since information advantage leads to increased profits. This paper investigates whether those with less risk aversion or more risk aversion would like to be informed when there is private information which can be acquired for a certain cost. The CARA utility function and normal return assumptions were used to get the closed form solution of the absolute risk aversion coefficient which is indifferent to whether a person wants to be informed, which shows that the less risk aversion investor would like to be informed. The results further show that in financial markets, those people with lower risk aversion (for instance, institutional investors) are more favorable to information and price discovery of risky assets, which is consistent with intuition as well as empirical studies.

This paper describes a low-power low-cost 24-bit Σ-Δ digital-to-analog converter (DAC) for portable digital-audio applications. The interpolation filter uses a no-multiplier scheme to implement the arithmetic units and reading-writing common storage scheme for the delay-line to significantly reduce the die area. A 15-level quantizer, third-order, single-stage Σ-Δ modulator is employed to reduce the passband quantization noise, relax the out-of-band filtering requirements, and enhance immunity to clock jitter. A data weighted averaging algorithm is used to mitigate the nonlinearity caused by capacitor mismatch. A direct charge transfer switched-capacitor low-pass filter (DCT-SC LPF) is used to reconstruct the analog signal to reduce the kT/C noise and capacitor mismatch effect with a small increase of the power dissipation. The chip was fabricated in the SMIC 0.13 μm 1P5M CMOS process. The cell area of the digital part is 0.056 mm² and the total area of the analog part is 0.34 mm². The supply voltage is 1.2 V for the digital circuit and 3.3 V for the analog circuit. The power consumption of the analog part is 3.5 mW. The audio DAC achieves a 100 dB dynamic range and an 84 dB peak signal-to-noise-plus-distortion ratio over a 20 kHz passband. The results show that these performances are good enough for high quality portable audio applications.

This paper presents an in-depth analysis of the interference strength and required guardband width between coexistent users for distributed orthogonal frequency division multiple access (OFDMA). In dynamic spectrum access networks, the cross-band interference between spectrally adjacent users is considered harmful with frequency guardbands inserted between spectrum blocks to eliminate the interference. However, the strength of the cross-band interference depends heavily on the user heterogeneity in different OFDM configurations. The cross-band interference due to the three user heterogeneity artifacts of power heterogeneity, sampling rate heterogeneity, and symbol length heterogeneity is investigated to determine the required guardband width. Analytical and simulation results show that the greater user heterogeneity requires larger guardbands with the sampling rate heterogeneity having the greatest effect. These results can be used to assist the design of spectrum allocation strategies.

Directional speech enhancement of signals from microphone arrays is an effective way to improve speech recognition for cochlear implant users. The strict implant size limitation results in a short distance between microphones. The fractional delay problem due to the short distance between microphones is solved by a maximal flat (Maxflat) finite impulse response (FIR) filter, using the Maxflat error criteria at a low frequency containing most of the speech information and energy. The fractional Maxflat FIR filter approximates the ideal digital fractional filter at the magnitude response, phase response, and phase delay characteristics, and is also very low order. The results demonstrate that the Maxflat FIR filter accurately and effectively solves the fractional digital delay and is very suitable for real-time speech processing in practical cochlear implant products.

An English speech recognition system was implemented on a chip, called speech system-on-chip (SoC). The SoC included an application specific integrated circuit with a vector accelerator to improve performance. The sub-word model based on a continuous density hidden Markov model recognition algorithm ran on a very cheap speech chip. The algorithm was a two-stage fixed-width beam-search baseline system with a variable beam-width pruning strategy and a frame-synchronous word-level pruning strategy to significantly reduce the recognition time. Tests show that this method reduces the recognition time nearly 6 fold and the memory size nearly 2 fold compared to the original system, with less than 1% accuracy degradation for a 600 word recognition task and recognition accuracy rate of about 98%.

This paper describes a quantum switching architecture for nearest neighbor coupling. An efficient quantum shear sorting (QSS) algorithm is used to reduce the number of time steps. For the QSS algorithm, the running complexity of the quantum switching architecture is polynomial in time with the nearest neighbor coupling and the implementation is less complex. The result shows that improved switching is extremely simple to implement using existing quantum computer candidates.

A CMOS triode transconductor was developed with common mode feedback suitable for operating in low-voltage and low-power applications. The design is based on a body-driven input stage with feedback loops to extend both the signal input range and the tuning capability. The effective transconductance of the body-driven triode stage is increased using a partial positive feedback technique which also partially solves the problem introduced by the small transconductance. This design uses the UMC 0.18 μm CMOS process. Simulations show the transconductor operated with 1 V supply voltage has less than −55 dB total harmonic distortions (THD) in the complete tuning range (0 V ≤ V_cont ≤ 0.43 V) for a 1 MHz 0.8 V_p-p differential input. The power consumption is 70 μW for a 0.43 V control voltage.