Predicting the service performance of Internet applications is important in service selection, especially for video services. In order to design a predictor for forecasting video service performance in third-party application, two famous service providers in China, Iqiyi and Letv, are monitored and analyzed. The study highlights that the measured performance in the observation period is time-series data, and it has strong autocorrelation, which means it is predictable. In order to combine the temporal information and map the measured data to a proper feature space, the authors propose a predictor based on a Conditional Restricted Boltzmann Machine (CRBM), which can capture the potential temporal relationship of the historical information. Meanwhile, the measured data of different sources are combined to enhance the training process, which can enlarge the training size and avoid the over-fit problem. Experiments show that combining the measured results from different resolutions for a video can raise prediction performance, and the CRBM algorithm shows better prediction ability and more stable performance than the baseline algorithms.

To solve the problem of nonhomogeneous clutter suppression for moving target detection in High Frequency Surface Wave Radar (HFSWR), a novel nonhomogeneous clutter detector (NHD) is present in this paper. This novel NHD makes an analysis for the clutter constituents with single snapshot based on the over-determined linear equations in space-time adaptive processing (STAP) and distinguish the nonhomogeneous secondary data from the whole secondary data set through calculating the correlation coefficients of the secondary data.

An improved beamformer, which uses joint estimation of the reconstructed interference-plus-noise (IPN) covariance matrix and array steering vector (ASV), is proposed. It can mitigate the problem of performance degradation in situations where the desired signal exists in the sample covariance matrix and the steering vector pointing has large errors. In the proposed method, the covariance matrix is reconstructed by weighted sum of the exterior products of the interferences' ASV and their individual power to reject the desired signal component, the coefficients of which can be accurately estimated by the compressed sensing (CS) and total least squares (TLS) techniques. Moreover, according to the theorem of sequential vector space projection, the actual ASV is estimated from an intersection of two subspaces by applying the alternating projection algorithm. Simulation results are provided to demonstrate the performance of the proposed beamformer, which is clearly better than the existing robust adaptive beamformers.

Due to the fact that natural images are approximately sparse in Discrete Cosine Transform (DCT) or wavelet basis, the Compressive Sensing (CS) can be employed to decode both the host image and watermark with zero error, despite not knowing the host image. In this paper, Limited-Random Sequence (LRS) matrix is utilized to implement the blind CS detection, which benefits from zero error and lower complexity. The performance in Bit Error Rate (BER) and error-free detection probability confirms the validity and efficiency of the proposed scheme.

A robust adaptive beamforming algorithm is proposed based on the precise interference-plus-noise covariance matrix reconstruction and steering vector estimation of the desired signal, even existing large gain-phase errors. Firstly, the model of array mismatches is proposed with the first-order Taylor series expansion. Then, an iterative method is designed to jointly estimate calibration coefficients and steering vectors of the desired signal and interferences. Next, the powers of interferences and noise are estimated by solving a quadratic optimization question with the derived closed-form solution. At last, the actual interference-plus-noise covariance matrix can be reconstructed as a weighted sum of the steering vectors and the corresponding powers. Simulation results demonstrate the effectiveness and advancement of the proposed method.

In this paper, a novel switched-capacitor DC-DC converter with pulse density and width modulation (PDWM) is proposed with reduced output ripple at variable output voltages. While performing pulse density modulation (PDM), the proposed PDWM modulates the pulse width at the same time to reduce the output ripple with high power efficiency. The prototype chip was implemented using 65 nm CMOS process. The switched-capacitor DC-DC converter has 0.2-V to 0.47-V output voltage and delivers 0.25-mA to 10-mA output current from a 1-V input supply with a peak efficiency of 87%. Compared with the conventional PDM scheme, the proposed switched-capacitor DC-DC converter with PDWM reduces the output ripple by 57% in the low output voltage region with the efficiency penalty of 2%.

We create an efficient statistical pruning (SP) algorithm for fixed-complexity sphere decoder (FSD) by utilizing partial decision feedback detection (i.e., SP-FSD). Simulation results show that SP-FSD not only attains the near-optimal performance, but also achieves much lower complexity than the original FSD and its two lately-developed variants: the simplified FSD (SFSD) and the statistical threshold-based FSD (ST-FSD).

The Domain Name System (DNS) maps domain names to IP addresses. It is an important infrastructure in the Internet. Recently, DNS has experienced various security threats. DNS resolvers experience the security threats most frequently, since they interact with clients and they are the largest group of domain name servers. In order to eliminate security threats against DNS resolvers, it is essential to improve their “health status”. Since DNS resolvers' owners are not clear which DNS resolvers should be improved and how to improve “health status”, the evaluation of “health status” for DNS resolvers has become vital. In this paper, we emphasize five indicators describing “health status” for DNS resolvers, including security, integrity, availability, speed and stability. We also present nine metrics measuring the indicators. Based on the measurement of the metrics, we present a “health status” evaluation method with factor analysis. To validate our method, we measured and evaluated more than 30,000 DNS resolvers in China and Japan. The results showed that the proposed “health status” evaluation method could describe “health status” well. We also introduce instructions for evaluating a small number of DNS resolvers. And we discuss DNSSEC and its effects on resolution speed. At last, we make suggestions for inspecting and improving “health status” of DNS resolvers.

Disassembly, as a principal reverse-engineering tool, is the process of recovering the equivalent assembly instructions of a program's machine code from its binary representation. However, when disassembling a firmware file, the disassembly process cannot be performed well if the image base is unknown. In this paper, we propose an innovative method to determine the image base of a firmware file with ARM/Thumb instruction set. First, based on the characteristics of the function entry table (FET) for an ARM processor, an algorithm called FIND-FET is proposed to identify the function entry tables. Second, by using the most common instructions of function prologue and FETs, the FIND-BASE algorithm is proposed to determine the candidate image base by counting the matched functions and then choose the one with maximal matched FETs as the final result. The algorithms are applied on some firmwares collected from the Internet, and results indicate that they can effectively find out the image base for the majority of example firmware files.

This paper investigates the data persistence problem with compressive sensing (CS) in wireless sensor networks (WSNs) where the sensed readings should be temporarily stored among the entire network in a distributed manner until gathered by a mobile sink. Since there is an energy-performance tradeoff, conventional CS-based schemes only focus on reducing the energy consumption or improving the CS construction performance. In this paper, we propose an efficient Compressive Sensing based Data Persistence (CSDP) scheme to achieve the optimum balance between energy consumption and reconstruction performance. Unlike most existing CS-based schemes which require packets visiting the entire network to reach the equilibrium distribution, in our proposed scheme information exchange is only performed among neighboring nodes. Therefore, such an approach will result in a non-uniform distribution of measurements, and the CS measurement matrix depends heavily on the node degree. The CS reconstruction performance and energy consumption are analyzed. Simulation results confirm that the proposed CSDP scheme consumes the least energy and computational overheads compared with other representative schemes, while almost without sacrificing the CS reconstruction performance.

This paper presents a new method for blind source separation by exploiting phase and frequency redundancy of cyclostationary signals in a complementary way. It requires a weaker separation condition than those methods which only exploit the phase diversity or the frequency diversity of the source signals. The separation criterion is to diagonalize a polynomial matrix whose coefficient matrices consist of the correlation and cyclic correlation matrices, at time delay τ= 0, of multiple measurements. An algorithm is proposed to perform the blind source separation. Computer simulation results illustrate the performance of the new algorithm in comparison with the existing ones.

We have comprehensively studied by numerical simulation high power transmission properties through single mode fiber for non-repeatered system application. We have clearly captured bit error rates (BERs) of digital coherent signal exhibit specific floor levels, depending on transmitter powers, due to fiber nonlinearity. If the maximum transmitter powers are defined as the powers at which BER floor levels are 1.0×10-2 without error correction, those are found to be approximately +20.4dBm, +14.8dBm and +10.6dBm, respectively, for single-channel 120Gbps DP-QPSK, DP-16QAM and DP-64QAM formats in large-core and low-loss single-mode silica fibers. In the simulations, we set fiber lengths over 100km, which is much longer than the effective fiber length, thus the results are applicable to any of long-length non-repeatered systems. We also show that the maximum transmitter powers gradually decrease in logarithmic feature with the increase of the number of DWDM channels. The channel number dependence is newly shown to be almost independent on the modulation format. The simulated results have been compared with extended Gaussian-Noise (GN) model with introducing adjustment parameters, not only to confirm the validity of the results but to explore possible new analytical modeling for non-repeatered systems.

This paper investigates the transmission capacity of open-loop spatial multiplexing with zero-forcing receivers in overlaid ad hoc networks. We first derive asymptotic closed-form expressions for the transmission capacity of two coexisting networks (a primary network vs. a secondary network). We then address a special case with equal numbers of transmit and receive antennas through exact analysis. Numerical results validate the accuracy of our expressions. Our findings show that the overall transmission capacity of coexisting networks will improve significantly over that of a single network if the primary network can tolerate a slight outage probability increase. This improvement can be further boosted if more streams are configured in the spatial multiplexing scheme; less improvement is achieved by placing more antennas at the receive side than the transmit side. However, when the stream number exceeds a certain limit, spatial multiplexing will produce negative effect for the overlaid network.

In this paper, a 0.18-V input three-stage charge pump circuit applying forward body bias is proposed for energy harvesting applications. In the developed charge pump, all the MOSFETs are forward body biased by using the inter-stage/output voltages. By applying the proposed charge pump as the startup in the boost converter, the kick-up input voltage of the boost converter is reduced to 0.18 V. To verify the circuit characteristics, the conventional zero body bias charge pump and the proposed forward body bias charge pump were fabricated with 65 nm CMOS process. The measured output current of the proposed charge pump under 0.18-V input voltage is increased by 170% comparing to the conventional one at the output voltage of 0.5 V. In addition, the boost converter successfully boosts the 0.18-V input to higher than 0.65-V output.

Laser speech detection uses a non-contact Laser Doppler Vibrometry (LDV)-based acoustic sensor to obtain speech signals by precisely measuring voice-generated surface vibrations. Over long distances, however, the detected signal is very weak and full of speckle noise. To enhance the quality and intelligibility of the detected signal, we designed a two-sided Linear Prediction Coding (LPC)-based locator and interpolator to detect and replace speckle noise. We first studied the characteristics of speckle noise in detected signals and developed a binary-state statistical model for speckle noise generation. A two-sided LPC-based locator was then designed to locate the polluted samples, composed of an inverse decorrelator, nonlinear filter and threshold estimator. This greatly improves the detectability of speckle noise and avoids false/missed detection by improving the noise-to-signal-ratio (NSR). Finally, samples from both sides of the speckle noise were used to estimate the parameters of the interpolator and to code samples for replacing the polluted samples. Real-world speckle noise removal experiments and simulation-based comparative experiments were conducted and the results show that the proposed method is better able to locate speckle noise in laser detected speech and highly effective at replacing it.

In this paper, Digital Low Dropout Regulator (LDO) is proposed to provide the low noise and tunable power supply voltage to the 0.5-V near-threshold logic circuits. Because the conventional LDO feedback-controlled by the operational amplifier fail to operate at 0.5 V, the digital LDO eliminates all analog circuits and is controlled by digital circuits, which enables the 0.5-V operation. The developed digital LDO in 65 nm CMOS achieved the 0.5-V input voltage and 0.45-V output voltage with 98.7% current efficiency and 2.7-µA quiescent current at 200-µA load current. Both the input voltage and the quiescent current are the lowest values in the published LDO's, which indicates the good energy efficiency of the digital LDO at 0.5-V operation.

Rotate magnetic field can be used for ranging, especially the environment where electronic filed suffers a deep fading and attenuation, such as drilling underground. However, magnetic field is still affected by the ferromagnetic materials, e.g., oil casing pipe. The measurement error is not endurable for single measurement. In this paper, the Geometric Predicted Unscented Kalman Filtering (GP-UKF) algorithm is developed for rotate magnetic ranging system underground. With GP-UKF, the Root Mean Square Error (RMSE) can be suppressed. It is really important in a long range detection by magnetic field, i.e., more than 50 meters.

Critical nodes identification is of great significance in protecting power grids. Network efficiency can be used as an evaluation index to identify the critical nodes and is an indicator to quantify how efficiently a network exchanges information and transmits energy. Since power grid is a heterogeneous network and can be decomposed into small functionally-independent grids, the concept of the Giant Component does not apply to power grids. In this paper, we first model the power grid as the directed graph and define the Giant Efficiency sub-Graph (GEsG). The GEsG is the functionally-independent unit of the network where electric energy can be transmitted from a generation node (i.e., power plants) to some demand nodes (i.e., transmission stations and distribution stations) via the shortest path. Secondly, we propose an algorithm to evaluate the importance of nodes by calculating their critical degree, results of which can be used to identify critical nodes in heterogeneous networks. Thirdly, we define node efficiency loss to verify the accuracy of critical nodes identification (CNI) algorithm and compare the results that GEsG and Giant Component are separately used as assessment criteria for computing the node efficiency loss. Experiments prove the accuracy and efficiency of our CNI algorithm and show that the GEsG can better reflect heterogeneous characteristics and power transmission of power grids than the Giant Component. Our investigation leads to a counterintuitive finding that the most important critical nodes may not be the generation nodes but some demand nodes.

This letter proposes a novel modulation classification method for overlapped sources named LRGP involving multinomial logistic regression (MLR) and multi-gene genetic programming (MGGP). MGGP based feature engineering is conducted to transform the cumulants of the received signals into highly discriminative features and a MLR based classifier is trained to identify the combination of the modulation formats of the overlapped sources instead of signal separation. Extensive simulations demonstrate that LRGP yields superior performance compared with existing methods.

In small-aperture high frequency surface wave radar, the main-lobe clutter all can be seen as a more severe space spread clutter under the influence of the smaller array aperture. It compromises the detection performance of moving vessels, especially when the target is submerged in the clutter. To tackle this issue, an improved spread clutter estimated canceller, combining spread clutter estimated canceller, adaptive selection strategy of the optimal training samples and rotating spatial beam method, is presented to suppress main-lobe clutter in both angle domain and range domain. According to the experimental results, the proposed algorithm is shown to have far superior clutter suppression performance based on the real data.