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.

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.

The letter proposes a novel binary vulnerability analyzer for executable programs that is based on the Hidden Markov Model. A vulnerability instruction library (VIL) is primarily constructed by collecting binary frames located by double precision analysis. Executable programs are then converted into structurized code sequences with the VIL. The code sequences are essentially context-sensitive, which can be modeled by Hidden Markov Model (HMM). Finally, the HMM based vulnerability analyzer is built to recognize potential vulnerabilities of executable programs. Experimental results show the proposed approach achieves lower false positive/negative rate than latest static analyzers.

A novel and energy-efficient algorithm with Quality-of-Service (QoS) guarantee is proposed for cooperative spectrum sensing (CSS) with soft information fusion and hard information fusion. By weighting the sensing performance and the consumption of system resources in a utility function that is maximized with respect to the number of secondary users (SUs), it is shown that the optimal number of SUs is related to the price of these QoS requirements.

In the large-scale multi-UAV systems, the direct link may be invalid for two remote nodes on account of the constrained power or complex communication environment. Idle UAVs may work as relays between the sources and destinations to enhance communication quality. In this letter, we investigate the opportunistic relay selection for the UAVs dynamic network. On account of the time-varying channel states and the variable numbers of sources and relays, relay selection becomes much more difficult. In addition, information exchange among all nodes may bring much cost and it is difficult to implement in practice. Thus, we propose a decentralized relay selection approach based on mood-driven mechanism to combat the dynamic characteristics, aiming to maximize the total capacity of the network without information exchange. With the proposed approach, the sources can make decisions only according to their own current states and update states according to immediate rewards. Numerical results show that the proposed approach has attractive properties.

Blind beamforming plays an important role in multiple-input multiple-output (MIMO) Systems, radar, cognitive radio, and system identification. In this paper, we propose a new algorithm for multiple blind beamforming algorithm based on the least square constant modulus algorithm (LSCMA). The new method consists of the following three parts: (a) beamforming of one signal with LSCMA. (b) direction-of-arrival (DOA) estimation of the remaining signals by rooting the weight vector polynomial. (c) beamforming of the remaining signals with linear constraints minimum variance (LCMV) method. After the convergence of LSCMA, one signal is captured and the arrival angles of the remaining signals can be obtained by rooting the weight vector polynomial. Therefore, beamforming can be quickly established for the remaining signals using LCMV method. Simultaneously the DOA of the signals can also be obtained. Simulation results show the performance of the presented method.

In this paper the transfer function of a system with windowed current integration is discussed. This kind of integration is usually used in a sampling mixer and the current is generated by a transconductance amplifier (TA). The parasitic capacitance (Cp) and the output resistance of the TA (Ro,TA) before the sampling mixer heavily affect the performance. Calculations based on a model including the parasitic capacitance and the output resistance of the TA is carried out. Calculation results show that due to the parasitic capacitance, a notch at the sampling frequency appears, which is very harmful because it causes the gain near the sampling frequency to decrease greatly. The output resistance of the TA makes the depth of the notches shallow and decreases the gain near the sampling frequency. To suppress the effect of Cp and Ro,TA, an operational amplifier is introduced in parallel with the sampling capacitance (Cs). Simulation results show that there is a 17 dB gain increase while Cs is 1,pF, gm is 9,mS, N is 8 with a clock rate of 800,MHz.

This paper proposes a de-embedding method for on-chip S-parameter measurements at mm-wave frequency. The proposed method uses only two transmission lines with different length. In the proposed method, a parasitic-component model extracted from two transmission lines can be used for de-embedding for other-type DUTs like transistor, capacitor, inductor, etc. The experimental results show that the error in characteristic impedance between the different-length transmission lines is less than 0.7% above 40 GHz. The extracted pad model is also shown.

This paper presents a novel model in the field of image co-saliency detection. Previous works simply design low level handcrafted features or extract deep features based on image patches for co-saliency calculation, which neglect the entire object perception properties. Besides, they also neglect the problem of visual similar region's mismatching when designing co-saliency calculation model. To solve these problems, we propose a novel strategy by considering both local prediction and global refinement (LPGR). In the local prediction stage, we train a deep convolutional saliency detection network in an end-to-end manner which only use the fully convolutional layers for saliency map prediction to capture the entire object perception properties and reduce feature redundancy. In the global refinement stage, we construct a unified co-saliency refinement model by integrating global appearance similarity into a co-saliency diffusion function, realizing the propagation and optimization of local saliency values in the context of entire image group. To overcome the adverse effects of visual similar regions' mismatching, we innovatively incorporates the inter-images saliency spread constraint (ISC) term into our co-saliency calculation function. Experimental results on public datasets demonstrate consistent performance gains of the proposed model over the state-of-the-art methods.

This paper proposes a novel sub-architecture to optimize the data flow of REMUS-II (REconfigurable MUltimedia System 2), a dynamically coarse grain reconfigurable architecture. REMUS-II consists of a µPU (Micro-Processor Unit) and two RPUs (Reconfigurable Processor Unit), which are used to speeds up control-intensive tasks and data-intensive tasks respectively. The parallel computing capability and flexibility of REMUS-II makes itself an excellent candidate to process multimedia applications, which require a large amount of memory accesses. In this paper, we specifically optimize the data flow to deal with those performance-hazard and energy-hungry memory accessing in order to meet the bandwidth requirement of parallel computing. The RPU internal memory could work in multiple modes, like 2D-access mode and transformation mode, according to different multimedia access patterns. This novel design can improve the performance up to 26% compared to traditional on-chip memory. Meanwhile, the block buffer is implemented to optimize the off-chip data flow through reducing off-chip memory accesses, which reducing up to 43% compared to direct DDR access. Based on RTL simulation, REMUS-II can achieve 1080p@30 fps of H.264 High Profile@ Level 4 and High Level MPEG2 at 200 MHz clock frequency. The REMUS-II is implemented into 23.7 mm2 silicon on TSMC 65 nm logic process with a 400 MHz maximum working frequency.

Electromagnetic field analysis is a time-consuming process, and a method involving the use of an FPGA accelerator is one of the attractive ways to accelerate the analysis; the other method involve the use of CPU and GPU. In this paper, we propose an FPGA accelerator dedicated for a two-dimensional finite-difference time-domain (FDTD) method. This accelerator is based on a two-dimensional single instruction multiple data (SIMD) array architecture. Each processing element (PE) is composed of a six-stage pipeline that is optimized for the FDTD method. Moreover, driving signal generation and impedance termination are also implemented in the hardware. We demonstrate that our accelerator is 11 times faster than existing FPGA accelerators and 9 times faster than parallel computing on the NVIDIA Tesla C2075. As an application of the high-speed FDTD accelerator, the design optimization of a waveguide is shown.

Modeling visual attention 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 the modeling bottom-up visual attention. The main contributions are twofold: 1) We use a principal component analysis (PCA) to transform the RGB color space into three principal components, which intrinsically leads to an opponent representation of colors to ensure good saliency analysis. 2) A practicable framework for modeling visual attention is presented based on a region-level reliability analysis for each feature map. 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.

An L-2L through-line de-embedding method has been verified up to millimeter wave frequency. The parasitics of the pad can be modeled from the L-2L through-line. Measurement results of the transmission lines and transistors can be de-embedded by subtracting the parasitic matrix of the pad. Therefore, the de-embedding patterns, which is used for modeling active and passive devices, decrease greatly and the chip area also decreases. A one-stage amplifier is firstly implemented for helping verifying the de-embedding results. After that a four-stage 60 GHz amplifier has been fabricated in CMOS 65 nm process. Experimental results show that the four-stage amplifier realizes an input matching better than -10.5 dB and an output matching better than -13 dB at 61 GHz. A small signal power gain of 16.4 dB and a 1 dB output compression point of 4.6 dBm are obtained with a DC current consumption of 128 mA from a 1.2 V power supply. The chip size is 1.5 mm 0.85 mm.

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.