In this paper, a novel beam selection transmit diversity (BSTD) scheme is proposed for the downlink transmission of frequency division duplex (FDD) based DS-CDMA system. As a combination of selection transmit diversity and steering vector based beamforming, the BSTD scheme provides diversity gain as well as reducing multiple access interference in downlink. Moreover, to have a better understanding, the performance of the BSTD is also compared with other schemes. The comparison results show that the BSTD would be a promising candidate for the downlink transmission if both performance and implementation complexity are considered.

The prosperous Internet communication technologies have led to e-commerce in mobile computing and made Web of Things become popular. Electronic payment is the most important part of e-commerce, so many electronic payment schemes have been proposed. However, most of proposed schemes cannot give change. Based on proxy blind signatures, an e-cash payment system is proposed in this paper to solve this problem. This system can not only provide change divisibility through Web of Things, but also provide anonymity, verifiability, unforgeability and double-spending owner track.

A 0.6-V voltage shifter and a 0.6-V clocked comparator are presented for sampling correlation-based impulse radio UWB receiver. The voltage shifter is used for a novel split swing level scheme-based CMOS transmission gate which can reduce the power consumption by four times. Compared to the conventional voltage shifter, the proposed voltage shifter can reduce the required capacitance area by half and eliminate the non-overlapping complementary clock generator. The proposed 0.6-V clocked comparator can operate at 100-MHz clock with the voltage shifter. To reduce the power consumption of the conventional continuous-time comparator based synchronization control unit, a novel clocked-comparator based control unit is presented, thereby achieving the lowest energy consumption of 3.9 pJ/bit in the correlation-based UWB receiver with the 0.5 ns timing step for data synchronization.

Feature extractor plays an important role in visual tracking, but most state-of-the-art methods employ the same feature representation in all scenes. Taking into account the diverseness, a tracker should choose different features according to the videos. In this work, we propose a novel feature adaptive correlation tracker, which decomposes the tracking task into translation and scale estimation. According to the luminance of the target, our approach automatically selects either hierarchical convolutional features or histogram of oriented gradient features in translation for varied scenarios. Furthermore, we employ a discriminative correlation filter to handle scale variations. Extensive experiments are performed on a large-scale benchmark challenging dataset. And the results show that the proposed algorithm outperforms state-of-the-art trackers in accuracy and robustness.

In this paper, the Linear Exponential Quadratic Gaussian with Loop Transfer Recovery (LEQG/LTR) methodology is employed for the design of high performance induction motor servo systems. In addition, we design a speed sensorless induction motor vector controlled driver with both the extended Kalman filter and the LEQG/LTR algorithm. The experimental realization of an induction servo system is given. Compared with the traditional PI and LQG/LTR methods, it can be seen that the system output sensitivity for parameter variations and the rising time for larger command input of the proposed method can be significantly reduced.

In this paper, we have proposed a new method for diffuse liver disease classification with sonogram, including the normal liver, hepatitis and cirrhosis, from a new point of view "scale. " The new system utilizes a multiscale analysis tool, called wavelet transforms, to analyze the ultrasonic liver images. A new set of features consisting of second order statistics derived from the wavelet transformed images is employed. From these features, we have found that the third scale is the representative scale for the classification of the considered liver diseases, and the horizontal wavelet transform can improve the representation of the corresponding features. Experimental results show that our method can achieve about 88% correct classification rate which is superior to other measures such as the co-occurrence matrices, the Fourier power spectrum, and the texture spectrum. This implies that our feature set can access the granularity from sonogram more effectively. It should be pointed out that our features are powerful for discriminating the normal livers from the cirrhosis because there is no misclassification samples between the normal liver and the cirrhosis sets. In addition, the experimental results also verify the usefulness of "scale" because our multiscale feature set can gain eighteen percent advantage over the direct use of the statistical features. This means that the wavelet transform at proper scales can effectively increase the distances among the statistical feature clusters of different liver diseases.

Recently, a new light-weight version of the secure electronic transaction protocol was proposed. The protocol can achieve two goals. One goal is that the security level is the same as the SET protocol. The other goal is to reduce the computational time in message generation and verification, and reduce the communication overhead. However, the protocol has a weakness, which is that non-repudiation is acquired, but confidentiality is lost. In this paper, we point out the weakness of the protocol. We also propose an improvement to the protocol to overcome this weakness.

Various observation models have been introduced into the object tracking community, and combining them has become a promising direction. This paper proposes a novel approach for estimating the confidences of different observation models, and then effectively combining them in the particle filter framework. In our approach, spatial Likelihood distribution is represented by three simple but efficient parameters, reflecting the overall similarity, distribution sharpness and degree of multi peak. The balance of these three aspects leads to good estimation of confidences, which helps maintain the advantages of each observation model and further increases robustness to partial occlusion. Experiments on challenging video sequences demonstrate the effectiveness of our approach.

Video streaming uploading over vehicular ad hoc networks (VANETs) can support many interesting applications. Due to the high mobility and dynamic topology of VANETs, how to support video streaming using wireless communications between vehicles and road-side access points still remains an open issue. In this paper, we propose a geographical uploading scheme, called MPVUS, which uses the moving prediction to keep the stable forwarding and reduce the high link failure probability over VANETs. The scheme also decides the AP switch opportunity by traffic flow estimation, so as to adjust the forwarding direction timely to avoid the short-sighted switch decision. Simulation results demonstrate the effectiveness of our scheme, which can achieve good performance in terms of the start-up delay, playback interruption ratio and video frame distortion.

Recently, the small-world network model has been popular to describe a wide range of networks such as human social relations and networks formed by biological entities. The network model achieves a small diameter with relatively few links as measured by the ratio of clustering coefficient and the number of links. It is quite natural to consider email communication similar to social network patterns. Quite surprisingly, we find from our empirical study that local email networks follow a different type of network model that falls into the category of scale-free network. We propose new network models to describe such communication structure.

Noncoherent detectors for use in acquiring data-modulated direct-sequence spread-spectrum (DS/SS) signals are considered in this paper. Taking data modulation and timing uncertainty into account and using the generalized maximum likelihood (GML) or maximum likelihood (ML) detection approaches, we derive optimal detectors in the sense of Bayes or Neyman-Pearson and propose various suboptimal detectors. A simple systematic means for their realization is suggested and the numerical performance of these detectors is presented. We also compare their performance with that of the noncoherent combining (NC1) detector that had been proposed to serve the same need. Numerical results show that even the proposed suboptimal detectors can outperform the NC1 detector in most cases of interest.

How to represent images into highly compact binary codes is a critical issue in many computer vision tasks. Existing deep hashing methods typically focus on designing loss function by using pairwise or triplet labels. However, these methods ignore the attention mechanism in the human visual system. In this letter, we propose a novel Deep Attention Residual Hashing (DARH) method, which directly learns hash codes based on a simple pointwise classification loss function. Compared to previous methods, our method does not need to generate all possible pairwise or triplet labels from the training dataset. Specifically, we develop a new type of attention layer which can learn human eye fixation and significantly improves the representation ability of hash codes. In addition, we embedded the attention layer into the residual network to simultaneously learn discriminative image features and hash codes in an end-to-end manner. Extensive experiments on standard benchmarks demonstrate that our method preserves the instance-level similarity and outperforms state-of-the-art deep hashing methods in the image retrieval application.

Thinning and line extraction of binary images not only reduces data storage amount, automatically creates the adjacency and relativity between line and points but also provides applications for automatic inspection systems, pattern recognition systems and vectorization. Based on the features of construction drawings, new thinning and line extraction algorithms were proposed in this study. The experimental results showed that the proposed method has a higher reliability and produces better quality than the various existing methods.

Correlation filter-based approaches achieve competitive results in visual tracking, but the traditional correlation tracking methods failed in mining the color information of the videos. To address this issue, we propose a novel tracker combined with color features in a correlation filter framework, which extracts not only gray but also color information as the feature maps to compute the maximum response location via multi-channel correlation filters. In particular, we modify the label function of the conventional classifier to improve positioning accuracy and employ a discriminative correlation filter to handle scale variations. Experiments are performed on 35 challenging benchmark color sequences. And the results clearly show that our method outperforms state-of-the-art tracking approaches while operating in real-time.

A novel DC-to-960 MHz impulse radio ultra-wideband (IR-UWB) transceiver based on threshold detection technique is developed. It features a digital pulse-shaping transmitter, a DC power-free pulse discriminator and an error-recovery phase-frequency detector. The developed transceiver in 90 nm CMOS achieves the lowest energy consumption of 2.2 pJ/bit transmitter and 1.9 pJ/bit receiver at 100 Mbps in the UWB transceivers.

The spectrum sensing of the orthogonal frequency division multiplexing (OFDM) system in cognitive radio (CR) has always been challenging, especially for user terminals that utilize the full-duplex (FD) mode. We herein propose an advanced FD spectrum-sensing scheme that can be successfully performed even when severe self-interference is encountered from the user terminal. Based on the “classification-converted sensing” framework, the cyclostationary periodogram generated by OFDM pilots is exhibited in the form of images. These images are subsequently plugged into convolutional neural networks (CNNs) for classifications owing to the CNN's strength in image recognition. More importantly, to realize spectrum sensing against residual self-interference, noise pollution, and channel fading, we used adversarial training, where a CR-specific, modified training database was proposed. We analyzed the performances exhibited by the different architectures of the CNN and the different resolutions of the input image to balance the detection performance with computing capability. We proposed a design plan of the signal structure for the CR transmitting terminal that can fit into the proposed spectrum-sensing scheme while benefiting from its own transmission. The simulation results prove that our method has excellent sensing capability for the FD system; furthermore, our method achieves a higher detection accuracy than the conventional method.

Object detection and tracking is one of the most important research topics in pattern recognition and the basis of many computer vision systems. Many accomplishments in this field have been achieved recently. Some specific objects, such as human face and vehicles, can already be detected in various applications. However, tracking objects with large variances in color, texture and local shape (such as pedestrians) is still a challenging topic in this field. To solve this problem, a pedestrian tracking scheme is proposed in this paper, including online training for pedestrian-detector. Simulation and analysis of the results shows that, the proposal method could deal with illumination change, pose change and occlusion problem and any combination thereof.

We propose a simple technique to form miniature optical circuits using microfibers embedded into a low refractive index matrix. As an example we demonstrate a silica microfiber knot resonator embedded in a fluoroacrylate polymer. Fabrication issues and initial experimental results are reported. We also present simulations aimed at understanding the current limitations to the Q-factor and the role of the embedding polymer refractive index on the Q-factor of future resonators. It is anticipated that using commercially available polymers high Q-factor resonators with radii as small as 100 micrometers can be made by this technique.

In this paper, a block implementation of high-speed IIR adaptive noise canceller is proposed. First, the block difference equation of an IIR filter is derived by the difference equation for high-speed signal processing. It is shown that the computational complexity for updating the coefficients of IIR adaptive filter can be reduced by using the relations between the elements of coefficient matrices of block difference equation. Secondly, the block implementation of IIR adaptive noise canceller is proposed in which the convergence rate is increased by successively adjusting filter Q-factors. Finally, the usefulness of proposed block implementation is verified by the computer simulations.

In this paper we present a 0.25–1.0 V, 0.1–200 MHz, 25632, 65 nm SRAM macro. The main design techniques include a bitline leakage prediction scheme and a non-trimmed non-strobed sense amplifier to deal with process and runtime variations and data dependence.