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.

For base station antenna array systems with time-division-duplex (TDD) mode, downlink channel responses are equal to uplink channel responses if the duplexing time is small, thus it is often believed that TDD mode simplies downlink beamforming problem as uplink weights can be applied for downlink directly. In this letter, we show that for TDD DS-CDMA systems, even though uplink and downlink channel responses are equal, optimal uplink weights are no longer equal to the optimal downlink ones due to asynchronous property in uplink and synchronous property in downlink, as well as different data rate traffic and QoS requirements. Computer simulations show that for asymmetric traffic, if uplink weights are used for downlink directly, downlink system capacity is less than 50% of that with optimal downlink weights.

Mobility as a Service (MaaS) is a smart mobility model that integrates mobility services to deliver transportation needs through a single interface, offering users flexible and personalizd mobility. This paper presents a structural approach for developing a MaaS system architecture under Autonomous Transportation Systems (ATS), which is a new transition from the Intelligent Transportation Systems (ITS) with emerging technologies. Five primary components, including system elements, user needs, services, functions, and technologies, are defined to represent the system architecture. Based on the components, we introduce three architecture elements: functional architecture, logical architecture and physical architecture. Furthermore, this paper presents an evaluation process, links the architecture elements during the process and develops a three-layer structure for system performance evaluation. The proposed MaaS system architecture design can help the administration make services planning and implement planned services in an organized way, and support further technical deployment of mobility services.

Analog beamforming with broadband large-scale antenna arrays in millimeter wave (mmWave) multiple input multiple output (MIMO) systems faces the beam squint problem. In this paper, we first investigate the sensitivity of analog beamforming to subarray spatial separations in wideband mmWave systems using hybrid arrays, and propose optimized subarray separations. We then design improved analog beamforming after phase compensation based on Zadoff-Chu (ZC) sequence to flatten the frequency response of radio frequency (RF) equivalent channel. Considering a single-carrier frequency-domain equalization (SC-FDE) scheme at the receiver, we derive low-complexity linear zero-forcing (ZF) and minimum mean squared error (MMSE) equalizers in terms of output signal-to-noise ratio (SNR) after equalization. Simulation results show that the improved analog beamforming can effectively remove frequency-selective deep fading caused by beam squint, and achieve better bit-error-rate performance compared with the conventional analog beamforming.

In this paper, we investigate optimal design of millimeter-wave (mmWave) multiuser line-of-sight multiple-input-multiple-output (LOS MIMO) systems using hybrid arrays of subarrays based on hybrid block diagonalization (BD) precoding and combining scheme. By introducing a general 3D geometric channel model, the optimal subarray separation products of the transmitter and receiver for maximizing sum-rate is designed in terms of two regular configurations of adjacent subarrays and interleaved subarrays for different users, respectively. We analyze the sensitivity of the optimal design parameters on performance in terms of a deviation factor, and derive expressions for the eigenvalues of the multiuser equivalent LOS MIMO channel matrix, which are also valid for non-optimal design. Simulation results show that the interleaved subarrays can support longer distance communication than the adjacent subarrays given the appropriate fixed subarray deployment.

This paper describes a modified ICP registration system of facial point data with range-scanning equipment for medical Augmented Reality applications. The reference facial point data are extracted from the pre-stored CT images; the floating facial point data are captured from range-scanning equipment. A modified soft-shape-context ICP including an adaptive dual AK-D tree for searching the closest point and a modified shape-context objective function is used to register the floating data to reference data to provide the geometric relationship for a medical assistant system and pre-operative training. The adaptive dual AK-D tree searches the closest-point pair and discards insignificant control coupling points by an adaptive distance threshold on the distance between the two returned closest neighbor points which are searched by using AK-D tree search algorithm in two different partition orders. In the objective function of ICP, we utilize the modified soft-shape-context information which is one kind of projection information to enhance the robustness of the objective function. Experiment results of using touch and non-touch capture equipment to capture floating point data are performed to show the superiority of the proposed system.

The latest deep hashing methods perform hash codes learning and image feature learning simultaneously by using pairwise or triplet labels. However, generating all possible pairwise or triplet labels from the training dataset can quickly become intractable, where the majority of those samples may produce small costs, resulting in slow convergence. In this letter, we propose a novel deep discriminative supervised hashing method, called DDSH, which directly learns hash codes based on a new combined loss function. Compared to previous methods, our method can take full advantages of the annotated data in terms of pairwise similarity and image identities. 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. Remarkably, our 16-bits binary representation can surpass the performance of existing 48-bits binary representation, which demonstrates that our method can effectively improve the speed and precision of large scale image retrieval systems.

Feature extractor is an important component of a tracker and the convolutional neural networks (CNNs) have demonstrated excellent performance in visual tracking. However, the CNN features cannot perform well under conditions of low illumination. To address this issue, we propose a novel deep correlation tracker with backtracking, which consists of target translation, backtracking and scale estimation. We employ four correlation filters, one with a histogram of oriented gradient (HOG) descriptor and the other three with the CNN features to estimate the translation. In particular, we propose a backtracking algorithm to reconfirm the translation location. Comprehensive experiments are performed on a large-scale challenging benchmark dataset. And the results show that the proposed algorithm outperforms state-of-the-art methods in accuracy and robustness.

Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in Information Retrieval, Natural Language Processing, and Data Mining. Intensive studies have been conducted on the problem and significant progress has been made [1],[2]. This short paper gives an introduction to learning to rank, and it specifically explains the fundamental problems, existing approaches, and future work of learning to rank. Several learning to rank methods using SVM techniques are described in details.

A low power impulse radio ultra-wideband (IR-UWB) receiver for DC-960 MHz band is proposed in this paper. The proposed receiver employs multiple DC power-free charge-domain sampling correlators to eliminate the need for phase synchronization. To alleviate BER degradation due to an increased charge injection in a subtraction operation in the sampling correlator than that of an addition operation, a comparator with variable threshold (=offset) voltage is used, which enables an addition-only operation. The developed receiver fabricated in 1.2 V 65 nm CMOS achieves the lowest energy consumption of 17.6 pJ/bit at 100 Mbps in state-of-the-art correlation-based UWB receivers.

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.