TTFF (Time-To-First-Fix) is an important indicator of GPS receiver performance, and must be reduced as much as possible. Bit synchronization is the pre-condition of positioning, which affects TTFF. The frequency error leads to power loss, which makes it difficult to find the bit edge. The conventional bit synchronization methods only work well when there is no or very small frequency error. The bit synchronization process is generally carried out after the pull-in stage, where the carrier loop is already stable. In this paper, a new bit synchronization method based on frequency compensation is proposed. Through compensating the frequency error, the new method reduces the signal power loss caused by the accumulation of coherent integration. The performances of the new method in different frequency error scenarios are compared. The parameters in the proposed method are analyzed and optimized to reduce the computational complexity. Simulation results show that the new method has good performance when the frequency error is less than 25Hz. Test results show that the new method can tolerate dynamic frequency errors, and it is possible to move the bit synchronization to the pull-in process to reduce the TTFF.

The frequency response of log-Gabor function matches well the frequency response of primate visual neurons. In this letter, motion-salient regions are extracted based on the 2D log-Gabor wavelet transform of the spatio-temporal form of actions. A supervised classification technique is then used to classify the actions. The proposed method is robust to the irregular segmentation of actors. Moreover, the 2D log-Gabor wavelet permits more compact representation of actions than the recent neurobiological models using Gabor wavelet.

This paper discusses non-ideal issues in a fully passive noise shaping successive approximation register analog-to-digital converter. The fully passive noise shaping techniques are realized by switches and capacitors without operational amplifiers to be scalable and power efficient. However, some non-ideal issues, such as parasitic capacitance, comparator noise, thermal noise, will affect the performance of the noise shaping and then degrade the final achievable resolution. This paper analyzes the effects of the main non-ideal issues and provides the design reference for fully passive noise shaping techniques. The analysis is based on 2nd order fully passive noise shaping SAR ADC with an 8-bit architecture and an OSR of 4.

A MB-OFDM UWB transmitter with on-chip transformer and LO leakage calibration for WiMedia bandgroup 1 is presented. The measurements show a gain-flatness of 1 dB, an LOLRR of -53 dBc/-43 dBc (wi/o cali), an EVM of 2.2% with a power consumption of 22 mW and an area of 1.26 mm2.

In 2006, Miranda et al. proposed an anonymity scheme to achieve peers' anonymity in Peer-to-Peer (P2P) reputation systems. In this paper, we show that this scheme can not achieve peers' anonymity in two cases. We also propose an improvement which solves the problem and improves the degree of anonymity.

The compressive sensing (CS) theory has been recognized as a promising technique to achieve the target localization in wireless sensor networks. However, most of the existing works require the prior knowledge of transmitting powers of targets, which is not conformed to the case that the information of targets is completely unknown. To address such a problem, in this paper, we propose a novel CS-based approach for multiple target localization and power estimation. It is achieved by formulating the locations and transmitting powers of targets as a sparse vector in the discrete spatial domain and the received signal strengths (RSSs) of targets are taken to recover the sparse vector. The key point of CS-based localization is the sensing matrix, which is constructed by collecting RSSs from RF emitters in our approach, avoiding the disadvantage of using the radio propagation model. Moreover, since the collection of RSSs to construct the sensing matrix is tedious and time-consuming, we propose a CS-based method for reconstructing the sensing matrix from only a small number of RSS measurements. It is achieved by exploiting the CS theory and designing an difference matrix to reveal the sparsity of the sensing matrix. Finally, simulation results demonstrate the effectiveness and robustness of our localization and power estimation approach.

As an emerging and promising technique, device-free localization (DFL) has drawn considerable attention in recent years. By exploiting the inherent spatial sparsity of target localization, the compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements. In practical scenarios, a prior knowledge about target locations is usually available, which can be obtained by coarse localization or tracking techniques. Among existing CS-based DFL approaches, however, few works consider the utilization of prior knowledge. To make use of the prior knowledge that is partly or erroneous, this paper proposes a novel faulty prior knowledge aided multi-target device-free localization (FPK-DFL) method. It first incorporates the faulty prior knowledge into a three-layer hierarchical prior model. Then, it estimates location vector and learns model parameters under a variational Bayesian inference (VBI) framework. Simulation results show that the proposed method can improve the localization accuracy by taking advantage of the faulty prior knowledge.

A 60-GHz CMOS transmitter with on-chip antenna for high-speed short-range wireless interconnections is presented. The radiation gain of the on-chip antenna is doubled using helium-3 ion irradiation technique. The transmitter core is composed of a resistive-feedback RF amplifier, a double-balanced passive mixer, and an injection-locked oscillator. The wideband and power-saving design of the transmitter core guarantees the low-power and high-data-rate characteristic. The transmitter fabricated in a 65-nm CMOS process achieves 5-Gb/s data rate with an EVM performance of $-$12 dB for BPSK modulation at a distance of 1,mm. The whole transmitter consumes 17,mW from a 1.2-V supply and occupies a core area of 0.64,mm$^{2}$ including the on-chip antenna. The gain-enhanced antenna together with the wideband and power-saving design of the transmitter provides a low-power low-cost full on-chip solution for the short-range high-data-rate wireless communication.

Many basic tasks in Wireless Sensor Networks (WSNs) rely heavily on the availability and accuracy of target locations. Since the number of targets is usually limited, localization benefits from Compressed Sensing (CS) in the sense that measurements can be greatly reduced. Though some CS-based localization schemes have been proposed, all of these solutions make an assumption that all targets are located on a pre-sampled and fixed grid, and perform poorly when some targets are located off the grid. To address this problem, we develop an adaptive dictionary algorithm where the grid is adaptively adjusted. To achieve this, we formulate localization as a joint parameter estimation and sparse signal recovery problem. Additionally, we transform the problem into a tractable convex optimization problem by using Taylor approximation. Finally, the block coordinate descent method is leveraged to iteratively optimize over the parameters and sparse signal. After iterations, the measurements can be linearly represented by a sparse signal which indicates the number and locations of targets. Extensive simulation results show that the proposed adaptive dictionary algorithm provides better performance than state-of-the-art fixed dictionary algorithms.

The compressive sensing has been applied to develop an effective framework for simultaneously localizing multiple targets in wireless sensor networks. Nevertheless, existing methods implicitly use analog measurements, which have infinite bit precision. In this letter, we focus on off-grid target localization using quantized measurements with only several bits. To address this, we propose a novel localization framework for jointly estimating target locations and dealing with quantization errors, based on the novel application of the variational Bayesian Expectation-Maximization methodology. Simulation results highlight its superior performance.

Unmanned aerial vehicle mounted base stations (UAV-BSs) can provide wireless cellular service to ground users in a variety of scenarios. The efficient deployment of such UAV-BSs while optimizing the coverage area is one of the key challenges. We investigate the deployment of UAV-BS to maximize the coverage of ground users, and further analyzes the impact of the deployment of UAV-BS on the fairness of ground users. In this paper, we first calculated the location of the UAV-BS according to the QoS requirements of the ground users, and then the fairness of ground users is taken into account by calculating three different fairness indexes. The performance of two genetic algorithms, namely Standard Genetic Algorithm (SGA) and Multi-Population Genetic Algorithm (MPGA) are compared to solve the optimization problem of UAV-BS deployment. The simulations are presented showing that the performance of the two algorithms, and the fairness performance of the ground users is also given.

This paper presents the evaluation of the L-2L de-embedding method with misalignment of the contact position. The issues of misalignment of the contact position are investigated. The analysis of misalignment in the L-2L de-embedding procedure is performed. Two comparisons are carried out to verify the error of the L-2L de-embedding method. The calculation percent error in quality factor of the transmission line becomes up to 9.0%, while the transistor S-parameter error percentage becomes up to 21% at 60 GHz in the experimental results. The results show that the measurement errors, caused by the misalignment of the contact position, should be considered carefully.

This letter proposes a neurobiological approach for action recognition. In this approach, actions are represented by a visual-neuron feature (VNF) based on a quantitative model of object representation in the primate visual cortex. A supervised classification technique is then used to classify the actions. The proposed VNF is invariant to affine translation and scaling of moving objects while maintaining action specificity. Moreover, it is robust to the deformation of actors. Experiments on publicly available action datasets demonstrate the proposed approach outperforms conventional action recognition models based on computer-vision features.

Salient Region Extraction provides an alternative methodology to image description in many applications such as adaptive content delivery and image retrieval. In this paper, we propose a robust approach to extracting the salient region based on bottom-up visual attention. The main contributions are twofold: 1) Instead of the feature parallel integration, the proposed saliencies are derived by serial processing between texture and color features. Hence, the proposed approach intrinsically provides an alternative methodology to model attention with low implementation complexity. 2) A constructive approach is proposed for rendering an image by a non-linear intensity mapping, which can efficiently eliminate high contrast noise regions in the image. And then the salient map can be robustly generated for a variety of nature images. Experiments show that the proposed algorithm is effective and can characterize the human perception well.

At mm-wave frequency, the layout of CMOS transistors has a larger effect on the device performance than ever before in low frequency. In this work, the distance between the gate and drain contact (Dgd) has been enlarged to obtain a better maximum available gain (MAG). By using the asymmetric-layout transistor, a 0.6 dB MAG improvement is realized when Dgd changes from 60 nm to 200 nm. A four-stage common-source low noise amplifier is implemented in a 65 nm CMOS process. A measured peak power gain of 24 dB is achieved with a power dissipation of 30 mW from a 1.2-V power supply. An 18 dB variable gain is also realized by adjusting the bias voltage. The measured 3-dB bandwidth is about 17 GHz from 51 GHz to 68 GHz, and noise figure (NF) is from 4.0 dB to 7.6 dB.

The noise performance of common source and cascode topology 60 GHz LNAs is analyzed and verified. The analysis result shows that the noise performance of the cascode topology is degraded at high frequency due to the inter-stage node capacitance. The analysis result is verified by experimental results. A three-stage LNA employing two noise-matched CS stages and a cascode stage is proposed. For comparison a conventional two-stage cascode LNA is also been studied with the measurement-based model. The measured results of the proposed LNA show that an input and output matching of less than -10 dB, a maximum gain of 9.7 dB and a noise figure (NF) of 3.2 dB are obtained with a power consumption of 30 mW from a 1.2-V supply voltage. Compared to the conventional cascode LNA, an improvement of 2.3-dB for NF and 1.9-dB for power gain are realized. Both the proposed and conventional LNAs are implemented in 65 nm CMOS process.

The equivalent anisotropic material parameters of metal dummy fills in a CMOS chip were extracted through an eigenmode analysis of a unit-cell of a space filled with metal dummies. The validity of the parameters was confirmed by comparing the S-parameters of a parallel-plate waveguide with the metal dummy fills and their effective material properties. The validity of the effective material properties was also confirmed by using them in a simulation of an on-chip dipole antenna.

A method based on covariance differencing for a uniform linear array is proposed to counter the problem of direction finding of narrowband signals under a colored noise environment. By assuming a Hermitian symmetric Toeplitz matrix for the unknown noise, the array covariance matrix is transformed into a centrohermitian matrix in an appropriate way allowing the noise component to be eliminated. The modified covariance differencing algorithm provides accurate direction of arrival (DOA) estimation when the incident signals are uncorrelated or just two of the signals are coherent. If there are more than two coherent signals, the presented method combined with spatial smoothing (SS) scheme can be used. Unlike the original method, the new approach dispenses the need to determine the true angles and the phantom angles. Simulation results demonstrate the effectiveness of presented algorithm.

The effect of wavelength detuning on the device performance of identical-epitaxial-layer (IEL) electroabsorption (EA) modulator integrated distributed feedback (DFB) lasers is studied in detail. Based on the lasing behavior of integrated devices with different amount of wavelength detuning and the photocurrent spectra under different reverse biases, the optimal wavelength detuning is experimentally determined to be around 30-40 nm for our IEL integrated devices. By adopting gain-coupled DFB laser section, integrated devices with optimal wavelength detuning have demonstrated excellent single mode performances. The extinction ratio is measured to be greater than 15 dB at -3 V, and the modulation bandwidth is around 8 GHz.

A monolithic frequency synthesizer with wide tuning range, low phase noise and spurs was realized in 0.13,$mu$m CMOS technology. It consists of an analog PLL, a harmonic-rejection mixer and injection-locked frequency doublers to cover the whole 6--18,GHz frequency range. To achieve a low phase noise performance, a sub-sampling PLL with non-dividers was employed. The synthesizer can achieve phase noise $-$113.7,dBc/Hz@100,kHz in the best case and the reference spur is below $-$60,dBc. The core of the synthesizer consumes about 110,mA*1.2,V.