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.

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.

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.

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.

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 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.

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.

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.

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.