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.

Parametric resonance based solutions for sub-gigahertz radio frequency transceiver with 0.3V supply voltage are proposed in this paper. As an implementation example, a 0.3V 720µW variation-tolerant injection-locked frequency multiplier is developed in 90nm CMOS. It features a parametric resonance based multi-phase synthesis scheme, thereby achieving the lowest supply voltage with -110dBc@ 600kHz phase noise and 873MHz-1.008GHz locking range in state-of-the-art frequency synthesizers.

A new construction of shift sequences is proposed under the condition of P|L, and then the inter-group complementary (IGC) sequence sets are constructed based on the shift sequence. By adjusting the parameter q, two or three IGC sequence sets can be obtained. Compared with previous methods, the proposed construction can provide more sequence sets for both synchronous and asynchronous code-division multiple access communication systems.

A 315 MHz power-gated ultra low power transceiver for wireless sensor network is developed in 40 nm CMOS. The developed transceiver features an injection-locked frequency multiplier for carrier generation and a power-gated low noise amplifier with current second-reuse technique for receiver front-end. The injection-locked frequency multiplier implements frequency multiplication by edge-combining and thereby achieves 11 µW power consumption at 315 MHz. The proposed low noise amplifier achieves the lowest power consumption of 8.4 µW with 7.9 dB noise figure and 20.5 dB gain in state-of-the-art designs.

State-of-the-art background subtraction and foreground detection methods still face a variety of challenges, including illumination changes, camouflage, dynamic backgrounds, shadows, intermittent object motion. Detection of foreground elements via the robust principal component analysis (RPCA) method and its extensions based on low-rank and sparse structures have been conducted to achieve good performance in many scenes of the datasets, such as Changedetection.net (CDnet); however, the conventional RPCA method does not handle shadows well. To address this issue, we propose an approach that considers observed video data as the sum of three parts, namely a row-rank background, sparse moving objects and moving shadows. Next, we cast inequality constraints on the basic RPCA model and use an alternating direction method of multipliers framework combined with Rockafeller multipliers to derive a closed-form solution of the shadow matrix sub-problem. Our experiments have demonstrated that our method works effectively on challenging datasets that contain shadows.

A novel approach for detecting anomaly in visual surveillance system is proposed in this paper. It is composed of three parts: (a) a dense motion field and motion statistics method, (b) motion directional PCA for feature dimensionality reduction, (c) an improved one-class SVM for one-class classification. Experiments demonstrate the effectiveness of the proposed algorithm in detecting abnormal events in surveillance video, while keeping a low false alarm rate. Our scheme works well in complicated situations that common tracking or detection modules cannot handle.

Boosting over weak classifiers is widely used in pedestrian detection. As the number of weak classifiers is large, researchers always use a sampling method over weak classifiers before training. The sampling makes the boosting process harder to reach the fixed target. In this paper, we propose a partial derivative guidance for weak classifier mining method which can be used in conjunction with a boosting algorithm. Using weak classifier mining method makes the sampling less degraded in the performance. It has the same effect as testing more weak classifiers while using acceptable time. Experiments demonstrate that our algorithm can process quicker than [1] algorithm in both training and testing, without any performance decrease. The proposed algorithms is easily extending to any other boosting algorithms using a window-scanning style and HOG-like features.

In this paper, an alternative transform based on the correlation of the residual block is proposed for the improvement of the H.264/AVC coding efficiency. A discrete sine transform is used alternately with a discrete cosine transform in order to greatly compact the energy of the signal when the correlation coefficients of the signal are in the range of -0.5 to 0.5. Therefore, the discrete sine transform is suggested to be used in conjunction with the discrete cosine transform in H.264/AVC. The alternative transform selecting the optimal transform between two transforms by using rate-distortion optimization shows a coding gain compared with H.264/AVC. The proposed method achieves a PSNR gain of up to 1.0 dB compared to JM 10.2 at relatively high bitrates.

A wideband CMOS common-gate low-noise amplifier (LNA) with high linearity is proposed. The linearity is improved by dual cross-coupled feedback technique. A passive cross-coupled feedback removes the second-order harmonic feedback effect to the input-referred third-order intercept point (IIP3), which is known as one of the limitations for linearity enhancement using feedback. An active cross-coupled feedback, constituted by a voltage combiner and a feedback capacitor is employed to enhance loop gain, and acquire further linearity improvement. An enhanced LC-match input network and forward isolation of active cross-coupled feedback enable the proposed LNA with wideband input matching and flat gain performance. Fabricated in a 0.13 µm RF CMOS process, the LNA achieves a flat voltage gain of 13 dB, an NF of 2.6∼3.8 dB, and an IIP3 of 3.6∼4.9 dBm over a 3 dB bandwidth of 0.1∼1.3 GHz. It consumes only 3.2 mA from a 1.2 V supply and occupies an area of 480×418 um2. In contrast to those of reported wideband LNAs, the proposed LNA has the merit of low power consumption and high linearity.

In this paper, we propose a novel primary user detection scheme for spectrum sensing in cognitive radio. Inspired by the conventional signal classification approach, the spectrum sensing is translated into a classification problem. On the basis of feature-based classification, the spectral correlation of a second-order cyclostationary analysis is applied as the feature extraction method, whereas a stacked denoising autoencoders network is applied as the classifier. Two training methods for signal detection, interception-based detection and simulation-based detection, are considered, for different prior information and implementation conditions. In an interception-based detection method, inspired by the two-step sensing, we obtain training data from the interception of actual signals after a sophisticated sensing procedure, to achieve detection without priori information. In addition, benefiting from practical training data, this interception-based detection is superior under actual transmission environment conditions. The alternative, a simulation-based detection method utilizes some undisguised parameters of the primary user in the spectrum of interest. Owing to the diversified predetermined training data, simulation-based detection exhibits transcendental robustness against harsh noise environments, although it demands a more complicated classifier network structure. Additionally, for the above-described training methods, we discuss the classifier complexity over implementation conditions and the trade-off between robustness and detection performance. The simulation results show the advantages of the proposed method over conventional spectrum-sensing schemes.