This paper presents a new structure for noise and echo cancelers based on a combined fast abaptive algorithm. The main purpose of the new structure is to detect both the double-talk and the unknown path change. This goal is accomplished by using two adaptive filters. A main adaptive filter Fn, adjusted only in the non-double-talk period by the normalized LMS algorithm, is used for providing the canceler output. An auxiliary adaptive filter Ff, adjusted by the fast RLS algorithm, is used for detecting the double-talk and obtaining a near optimum tap-weight vector for Fn in the initialization period and whenever the unknown path has a sudden or fast change. The proposed structure is examined through computer simulation on a noise cancellation problem. Good cancellation performance and stable operation are obtained when signal is a speech corrupted by a white noise, a colored noise and another speech signal. Simulation results also show that the proposed structure is capable of distinguishing the near-end signal from the noise path change and quickly tracking this change.

The periodic disaster backup activity among geographically distributed multiple datacenters consumes huge network resources and therefore imposes a heavy burden on datacenters and transmission links. Previous work aims at least completion time, maximum utility or minimal cost, without consideration of load balance for limited network resources, likely to result in unfair distribution of backup load or significant impact on daily network services. In this paper, we propose a new progressive forwarding disaster backup strategy in the Software Defined Network scenarios to mitigate forwarding burdens on source datacenters and balance backup loads on backup datacenters and transmission links. We construct a new redundancy-aware time-expanded network model to divide time slots according to redundancy requirement, and propose role-switching method over time to utilize forwarding capability of backup datacenters. In every time slot, we leverage two-step optimization algorithm to realize capacity-constrained backup datacenter selection and fair backup load distribution. Simulations results prove that our strategy achieves good performance in load balance under the condition of guaranteeing transmission completion and backup redundancy.

In this letter, we give a generalized construction for sets of frequency-hopping sequences (FHSs) based on power-residue sequences. Our construction encompasses a known optimal construction and can generate new optimal sets of FHSs which simultaneously achieve the Peng-Fan bound and the Lempel-Greenberger bound.

This paper presents an accurate and systematic method to model the equivalent circuit of pulse generator in the electrical fast transients/burst test (EFT/B). Firstly, a new analytical expression is presented to express the generator's charging and discharging process under open-condition (1000-Ω), which all its coefficients are determined according to the output waveform specified by the manufacturer. And then, with adoption of the step source, the transfer function of the pulse forming network in Laplace domain is deduced, which is ready for the network synthesis. Based on above discussion, the parameterized method and the technique of constant-resistance are adopted for the network synthesis. Finally, the equivalent circuit is renormalized and improved to meet the specification under matching-condition (50-Ω). In this way, the equivalent circuit of EFT/B generator is obtained and can be adjusted conveniently to satisfy the different manufacturers. The PSPICE simulation with a certain load is validated by measurement.

This paper investigates the non-data-aided (NDA) carrier frequency estimation of amplitude and phase shift keying (APSK) signals. The true Cramer-Rao bound (CRB) for NDA frequency estimation of APSK signals are derived and evaluated numerically. Characteristic and jitter variance of NDA Luise and Reggiannini (L&R) frequency estimator are analyzed. Verified by Monte Carlo simulations, the analytical results are shown to be accurate for medium-to-high signal-to-noise ratio (SNR) values. Using the proposed closed-form expression, parameters of the algorithm are optimized efficiently to minimize the jitter variance.

In this paper, we propose a probabilistic approach to derive an approximate polyhedral description from range data. We first compare several least-squares-based methods for estimation of local normal vectors and select the most robust one based on a reasonable noise model of the range data. Second, we extract the stable planar regions from the range data by examining the distributions of the local normal vectors together with their spatial information in the 2D range image. Instead of segmenting the range data completely, we use only the geometries of the extracted stable planar regions to derive a polyhedral description of the range data. The curved surfaces in the range data are approximated by their extracted plane patches. With a probabilistic approach, the proposed method can be expected to be robust against the noise. Experimental results on real range data from different sources show the effectiveness of the proposed method.

A low complexity log-likelihood ratio (LLR) calculation for high-order amplitude phase shift keying (APSK) signals is proposed. Using proper constellation partitioning together with a look-up table, the number of terms for the comparison of Euclidean distances can be significantly reduced. Compared with the log-sum LLR approximation, the proposed method reduces the computational complexity by more than 65% and 75% for 16-APSK and 32-APSK signals, respectively, with very small bit error rate performance degradation.

A single solid-state magnetic sensor can be used to measure a current by sensing the field near the conductor in a non-contact way. In order to improve the accuracy of the measuring system, magnetic sensor arrays have been introduced in the current measurement around the conductor. An analytical algorithm based on Discrete Fourier Transform (DFT) is presented in this paper, which can separate the effects of the field generated by the current under measurement from the interference fields. A general mathematical model of the interference analysis is set up, which can be used for both DC and AC current measurement and has no restriction on the shape and number of the current conductors. Numerical simulations associated with preliminary experimental results confirm the validity of the approach.

In this paper, we present a Double-Anchoring Based Tone Mapping (DABTM) algorithm for displaying high dynamic range (HDR) images. First, two anchoring values are obtained using the double-anchoring theory. Second, we use the two values to formulate the compressing operator, which can achieve the aim of tone mapping directly. A new method based on accelerated K-means for the decomposition of HDR images into groups (frameworks) is proposed. Most importantly, a group of piecewise-overlap linear functions is put forward to define the belongingness of pixels to their locating frameworks. Experiments show that our algorithm is capable of achieving dynamic range compression, while preserving fine details and avoiding common artifacts such as gradient reversals, halos, or loss of local contrast.

We present the concept of key-rotatable and security-updatable homomorphic encryption (KR-SU-HE) scheme, which is defined as a class of public-key homomorphic encryption in which the keys and the security of any ciphertext can be rotated and updated while still keeping the underlying plaintext intact and unrevealed. After formalising the syntax and security notions for KR-SU-HE schemes, we build a concrete scheme based on the Learning With Errors assumption. We then perform several careful implementations and optimizations to show that our proposed scheme is efficiently practical.

The numerical properties of the recursive least squares (RLS) algorithm and its fast versions have been extensively studied. However, very few investigations are reported concerning the numerical behavior of the predictor based least squares (PLS) algorithms that provide the same least squares solutions as the RLS algorithm. This paper presents a comparative study on the numerical performances of the RLS and the backward PLS (BPLS) algorithms. Theoretical analysis of three main instability sources reported in the literature, including the overrange of the conversion factor, the loss of symmetry and the loss of positive definiteness of the inverse correlation matrix, has been done under a finite-precision arithmetic. Simulation results have confirmed the validity of our analysis. The results show that three main instability sources encountered in the RLS algorithm do not exist in the BPLS algorithm. Consequently, the BPLS algorithm provides a much more stable and robust numerical performance compared with the RLS algorithm.

The numerical property of the recursive least squares (RLS) algorithm has been extensively, studied. However, very few investigations are reported concerning the numerical behavior of the predictor-based least squares (PLS) algorithms which provide the same least squares solutions as the RLS algorithm. In Ref. [9], we gave a comparative study on the numerical performances of the RLS and the backward PLS (BPLS) algorithms. It was shown that the numerical property of the BPLS algorithm is much superior to that of the RLS algorithm under a finite-precision arithmetic because several main instability sources encountered in the RLS algorithm do not appear in the BPLS algorithm. This paper theoretically shows the stability of the BPLS algorithm by error propagation analysis. Since the time-variant nature of the BPLS algorithm, we prove the stability of the BPLS algorithm by using the method as shown in Ref. [6]. The expectation of the transition matrix in the BPLS algorithm is analyzed and its eigenvalues are shown to have values within the unit circle. Therefore we can say that the BPLS algorithm is numerically stable.

We build a privacy-preserving system of linear regression protecting both input data secrecy and output privacy. Our system achieves those goals simultaneously via a novel combination of homomorphic encryption and differential privacy dedicated to linear regression and its variants (ridge, LASSO). Our system is proved scalable over cloud servers, and its efficiency is extensively checked by careful experiments.

A flexible noise de-embedding method for on-wafer microwave measurements of silicon MOSFETs is presented in this study. We use the open, short, and thru dummy structures to subtract the parasitic effects from the probe pads and interconnects of a fixtured MOS transistor. The thru standard are used to extract the interconnect parameters for subtracting the interconnect parasitics in gate, drain, and source terminals of the MOSFET. The parasitics of the dangling leg in the source terminal are also modeled and taken into account in the noise de-embedding procedure. The MOS transistors and de-embedding dummy structures were fabricated in a standard CMOS process and characterized up to 20 GHz. Compared with the conventional de-embedding methods, the proposed technique is accurate and area-efficient.

Reducing communication complexity is a viable approach to multilevel logic synthesis. A communication complexity based approach was proposed previously. In the previous works, only disjoint input decomposition was considered. However, for certain types of circuits, the circuit size can be reduced by using overlapped decomposition. In this paper, we consider overlapped decompositions. Some design issues for overlapped decompositions such as detecting globals" and deriving subfunctions are addressed. Moreover, the Decomposition Don't Cares (DDC) is considered for improving the decomposed results. By using these techniques together, the area and delay of circuits can be further minimized.

In this paper authorization-limited transformation-free proxy cryptosystems (AL-TFP systems) are studied. It is a modification of the original proxy cryptosystem introduced by Mambo et al.[8] in which a ciphertext transformation by the original decryptor is necessary, and also a modification of the delegated decryption system proposed by Mu et al.[10]. In both systems proposed in [8] and [10], the original decryptors have to trust their proxies completely. The AL-TFP system allows the proxy decryptor to do decryption directly without any ciphertext transformation from the original decryptor, so that it can release the original decryptor more efficiently from a large amount of decrypting operations. Moreover, the original decryptor's privacy can be protected efficiently because the authority of proxy decryptor is limited to his duty and valid period. An active identity-based and a directory-based AL-TFP systems from pairings are proposed. Furthermore, an application of directory-based AL-TFP system to electronic commerce is also described. The securities of our schemes introduced are based on the BDH assumption.

This paper presents a deep neural network (DNN) based symbol detection and modulation classification detector (SDMCD) for mixed blind signals detection. Unlike conventional methods that employ symbol detection after modulation classification, the proposed SDMCD can perform symbol recovery and modulation identification simultaneously. A cumulant and moment feature vector is presented in conjunction with a low complexity sparse autoencoder architecture to complete mixed signals detection. Numerical results show that SDMCD scheme has remarkable symbol error rate performance and modulation classification accuracy for various modulation formats in AWGN and Rayleigh fading channels. Furthermore, the proposed detector has robust performance under the impact of frequency and phase offsets.

In this paper, we propose an accurate and scalable S-parameter de-embedding method for RF/microwave on-wafer characterization of silicon MOSFETs. Based on cascade configurations, this method utilizes planar open, short, and thru standards to estimate the effects of surrounding parasitic networks on a MOS transistor. The bulk-shielded open and short standards are used to simulate and de-embed the probe-pad parasitics. The thru standard are used to extract the interconnect parameters for subtracting the interconnect parasitics in gate and drain terminals of the MOSFET. To further eliminate the parasitics of dangling leg in source terminal of the MOSFET, we also introduce the microwave and multi-port network analysis to accomplish the two-port-to-three-port transformation for S-parameters. The MOSFET and its corresponding de-embedding standards were fabricated in a standard CMOS process and characterized up to 40 GHz. The scalability of the open, short, and thru standards is demonstrated and the performance of the proposed de-embedding procedure is validated by comparison with several de-embedding techniques.

An arc model has been applied in this paper to study the fundamental interruption environment of a 550 kV SF6 single-break tank circuit breaker. The full differential model takes into account of all important physical mechanisms and is implemented into a commercial Computational Fluid Dynamics (CFD) package, PHOENICS. The model takes a magneto-hydro-dynamics (MHD) approach and the governing equations are solved using the Finite Volume Method (FVM). Through the simulation, the flow velocity vector and mach number for capacitive current switching and short-circuit current breaking are analyzed, and flow dynamic characteristics are obtained. The simulation can provide helpful reference for the design of 550 kV SF6 single-break tank circuit breaker.

At present, the generative adversarial network (GAN) plays an important role in learning tasks. The basic idea of a GAN is to train the discriminator and generator simultaneously. A GAN-based inverse tone mapping method can generate high dynamic range (HDR) images corresponding to a scene according to multiple image sequences of a scene with different exposures. However, subsequent tone mapping algorithm processing is needed to display it on a general device. This paper proposes an end-to-end multi-exposure image fusion algorithm based on a relative GAN (called RaGAN-EF), which can fuse multiple image sequences with different exposures directly to generate a high-quality image that can be displayed on a general device without further processing. The RaGAN is used to design the loss function, which can retain more details in the source images. In addition, the number of input image sequences of multi-exposure image fusion algorithms is often uncertain, which limits the application of many existing GANs. This paper proposes a convolutional layer with weights shared between channels, which can solve the problem of variable input length. Experimental results demonstrate that the proposed method performs better in terms of both objective evaluation and visual quality.