A novel dynamic element matching (DEM) method, called binary-tree random DEM (BTR-DEM), is presented for a Nyquist-rate current-steering digital-to-analog converter (DAC). By increasing or decreasing the number of unit current sources randomly at the same time, the BTR-DEM encoding reduces switch transition glitches. A 5-bit current-steering DAC with the BTR-DEM technique is implemented in a 65-nm CMOS technology. The measured spurious free dynamic range (SFDR) attains 42 dB for a sample rate of 100 MHz and shows little dependence on signal frequency.

This work reports a novel power-rail electrostatic discharge (ESD) clamp circuit with parasitic bipolar-junction-transistor (BJT) and channel parallel shunt paths. The parallel shunt paths are formed by delivering a tiny ratio of drain voltage to the gate terminal of the clamp device in ESD events. Under such a mechanism, the proposed circuit achieves enhanced robustness over those of both gate-grounded NMOS (ggNMOS) and the referenced gate-coupled NMOS (gcNMOS). Besides, the proposed circuit also achieves improved fast power-up immunity over that of the referenced gcNMOS. All investigated designs are fabricated in a 65-nm CMOS process. Transmission-line-pulsing (TLP) and human-body-model (HBM) test results have both confirmed the performance enhancements of the proposed circuit. Finally, the validity of the achieved performance enhancements on other trigger circuits is essentially revealed in this work.

The traditional Markov prediction methods of the taxi destination rely only on the previous 2 to 3 GPS points. They negelect long-term dependencies within a taxi trajectory. We adopt a Recurrent Neural Network (RNN) to explore the long-term dependencies to predict the taxi destination as the multiple hidden layers of RNN can store these dependencies. However, the hidden layers of RNN are very sensitive to small perturbations to reduce the prediction accuracy when the amount of taxi trajectories is increasing. In order to improve the prediction accuracy of taxi destination and reduce the training time, we embed suprisal-driven zoneout (SDZ) to RNN, hence a taxi destination prediction method by regularized RNN with SDZ (TDPRS). SDZ can not only improve the robustness of TDPRS, but also reduce the training time by adopting partial update of parameters instead of a full update. Experiments with a Porto taxi trajectory data show that TDPRS improves the prediction accuracy by 12% compared to RNN prediction method in literature[4]. At the same time, the prediction time is reduced by 7%.

In this paper, implementation of a first-order active complex filter with variable parameter using operational transconductance amplifiers (OTAs) and grounded copacitors is presented. The proposed configurations can be used as s key building block to realize high-order active complex filters with variable parameter in cascade and leapfrog configuration. Experimental results which are in good agreement with theoretical responses are also given o demonstrate the feasibility of the proposed configurations.

In this paper a novel narrow-band bandpass filter with an output pair of analytic signals is presented. Since it is based on the complex analog filter, both synthesis and response characteristics of this filter are different from conventional bandpass filters. In the design of this filter, the frequency shift method is employed and the conventional lowpass to bandpass frequency transformation is not required. The analysis and examples show that the output signal pair of the proposed filter possesses same filtering characteristics and a 90 degree phase shifting characteristics in the passband. Therefore, the proposed filter will be used for a single sideband (SSB) signal generator without quadrature generator.

This letter presents a realization of active current-mode resonator with complex coefficients using CCIIs. The resonator can be used for cascade or leapfrog configuration of high-order bandpass filters with complex coefficients. For realizing the resonators, only the grounded capacitors and the grounded resistors as passive elements are required, therfore the resonator is suitable for the integrated circuit realization. The letter shows that the response error of the proposed circuit caused by nonideality of active components is more easily compensated than that of voltage-mode counterpart. Experimental result is used for verifying the feasibility of the proposed resonator.

Layout strategies including source edge to substrate space (SESS) and inserted substrate-pick stripes of gate-grounded NMOS(ggNMOS) are optimized in this work for on-chip electrostatic discharge (ESD) protection. In order to fully investigate influences of substrate resistors on triggering and conduction behaviors of ggNMOS, various devices are designed and fabricated in a 65-nm CMOS process. Direct current (DC), transmission-line-pulsing (TLP), human body model (HBM) and very-fast TLP (VF-TLP) tests are executed to fully characterize performance of fabricated ggNMOS. Test results reveal that an enlarged SESS parameter results in an earlier triggering behavior of ggNMOS, which presents a layout option for subtle adjustable triggering behaviors. Besides, inserted substrate-pick stripes are proved to have a bell-shape influence on the ESD robustness of ggNMOS and this bell-shape influence is valid in TLP, HBM and VF-TLP tests. Moreover, the most ESD-robust ggNMOS optimized under different inserted substrate-pick stripes always achieves a higher HBM level over the traditional ggNMOS at each concerned total device-width. Physical mechanisms of test results will be deeply discussed in this work.

In this paper, two universal building blocks for complex filter using CCIIs, CFCCIIs, grounded resistors and grounded capacitors are presented. These can be used to realize various complex bandpass filters with arbitrary order. The paper shows that the response error of the proposed circuit caused by nonideality of active components is more easily compensated than that of the conventional one employing op-amps, and that the sensitivities for all components are relatively small. Experimental results are used for verifying the validity of the proposed circuits.

In this study, we address the problem of salient region detection. Recently, saliency detection with contrast based approaches has shown to give promising results. However, different individual features exhibit different performance. In this paper, we show that the combination of color uniqueness and color spatial distribution is an effective way to detect saliency. A Color Adaptive Thresholding Watershed Fusion Segmentation (CAT-WFS) method is first given to retain boundary information and delete unnecessary details. Based on the segmentation, color uniqueness and color spatial distribution are defined separately. The color uniqueness denotes the color rareness of salient object, while the color spatial distribution represents the color attribute of the background. Aiming at highlighting the salient object and downplaying the background, we combine the two characters to generate the final saliency map. Experimental results demonstrate that the proposed algorithm outperforms existing salient object detection methods.

Sparse code division multiple access (SCMA) is a non-orthogonal multiple access (NOMA) technology that can improve frequency band utilization and allow many users to share quite a few resource elements (REs). This paper uses the modulation of lattice theory to develop a systematic construction procedure for the design of SCMA codebooks under Gaussian channel environments that can achieve near-optimal designs, especially for cases that consider large-scale SCMA parameters. However, under the condition of large-scale SCMA parameters, the mother constellation (MC) points will overlap, which can be solved by the method of the partial dimensions transformation (PDT). More importantly, we consider the upper bounded error probability of the signal transmission in the AWGN channels, and design a codeword allocation method to reduce the inter symbol interference (ISI) on the same RE. Simulation results show that under different codebook sizes and different overload rates, using two different message passing algorithms (MPA) to verify, the codebook proposed in this paper has a bit error rate (BER) significantly better than the reference codebooks, moreover the convergence time does not exceed that of the reference codebooks.

Vehicle logo detection technology is one of the research directions in the application of intelligent transportation systems. It is an important extension of detection technology based on license plates and motorcycle types. A vehicle logo is characterized by uniqueness, conspicuousness, and diversity. Therefore, thorough research is important in theory and application. Although there are some related works for object detection, most of them cannot achieve real-time detection for different scenes. Meanwhile, some real-time detection methods of single-stage have performed poorly in the object detection of small sizes. In order to solve the problem that the training samples are scarce, our work in this paper is improved by constructing the data of a vehicle logo (VLD-45-S), multi-stage pre-training, multi-scale prediction, feature fusion between deeper with shallow layer, dimension clustering of the bounding box, and multi-scale detection training. On the basis of keeping speed, this article improves the detection precision of the vehicle logo. The generalization of the detection model and anti-interference capability in real scenes are optimized by data enrichment. Experimental results show that the accuracy and speed of the detection algorithm are improved for the object of small sizes.

The partial-period autocorrelation of sequences is an important performance measure of communication systems employing them, but it is notoriously difficult to be analyzed. In this paper, we propose an algorithm to design unimodular sequences with low partial-period autocorrelations via directly minimizing the partial-period integrated sidelobe level (PISL). The proposed algorithm is inspired by the monotonic minimizer for integrated sidelobe level (MISL) algorithm. Then an acceleration scheme is considered to further accelerate the algorithms. Numerical experiments show that the proposed algorithm can effectively generate sequences with lower partial-period peak sidelobe level (PPSL) compared with the well-known Zadoff-Chu sequences.

Existing trajectory prediction methods suffer from the “data sparsity” and neglect “time awareness”, which leads to low accuracy. Aiming to the problem, we propose a fast time-aware sparse trajectories prediction with tensor factorization method (TSTP-TF). Firstly, we do trajectory synthesis based on trajectory entropy and put synthesized trajectories into the original trajectory space. It resolves the sparse problem of trajectory data and makes the new trajectory space more reliable. Then, we introduce multidimensional tensor modeling into Markov model to add the time dimension. Tensor factorization is adopted to infer the missing regions transition probabilities to further solve the problem of data sparsity. Due to the scale of the tensor, we design a divide and conquer tensor factorization model to reduce memory consumption and speed up decomposition. Experiments with real dataset show that TSTP-TF improves prediction accuracy generally by as much as 9% and 2% compared to the Baseline algorithm and ESTP-MF algorithm, respectively.

The critical problem of the pseudo-noise (PN) code acquisition system is the contradiction between the acquisition performance and the calculation complexity. This paper presents a low cost correlator (LCC) structure that can search for two PN code phases in a single accumulation period by eliminating redundant computation. Compared with the part-parallel structure that is composed of two serial correlators (PARALLEL2), the proposed LCC structure has the same performance while saves about 22% chip area and 34% power consumption if uses the Carry-look-ahead (CLA) adder, 17% chip area and 25% power consumption if uses the Ripple-carry (RPL) adder.

An atomistic model for annealing simulation is presented. To well simulate both BED (Boron Enhanced Diffusion) and TED (Transient Enhanced Diffusion), the surface emission model, which describes the emission of point defects from surface during annealing, is implemented. The simulation is carried out for RTA annealing (1000 or 1050) after B implantation. The implantation energy varies from 0.5 keV to 13 keV. Agreements between simulation and SIMS data are achieved. Both BED and TED phenomena are characterized. The Enhancement of diffusion is discussed. The surface emission model is studied by simulation. The results shows that the surface emission has little effect on annealing of B 10 keV implantation while obvious effect on annealing of B 0.5 keV implantation. It indicates that the surface emission is much more necessary to simulate BED than TED.

Existing moving object's trajectory prediction algorithms suffer from the data sparsity problem, which affects the accuracy of the trajectory prediction. Aiming to the problem, we present an Entropy-based Sparse Trajectories Prediction method enhanced by Matrix Factorization (ESTP-MF). Firstly, we do trajectory synthesis based on trajectory entropy and put synthesized trajectories into the trajectory space. It can resolve the sparse problem of trajectory data and make the new trajectory space more reliable. Secondly, under the new trajectory space, we introduce matrix factorization into Markov models to improve the sparse trajectory prediction. It uses matrix factorization to infer transition probabilities of the missing regions in terms of corresponding existing elements in the transition probability matrix. It aims to further solve the problem of data sparsity. Experiments with a real trajectory dataset show that ESTP-MF generally improves prediction accuracy by as much as 6% and 4% compared to the SubSyn algorithm and STP-EE algorithm respectively.