In this paper, an optimal method is proposed to design sparse-coefficient notch filters with principal basic vectors in the column space of a matrix constituted with frequency samples. The proposed scheme can perform in two stages. At the first stage, the principal vectors can be determined in the least-squares sense. At the second stage, with some components of the principal vectors, the notch filter design is formulated as a linear optimization problem according to the desired specifications. Optimal results can form sparse coefficients of the notch filter by solving the linear optimization problem. The simulation results show that the proposed scheme can achieve better performance in designing a sparse-coefficient notch filter of small order compared with other methods such as the equiripple method, the orthogonal matching pursuit based scheme and the L1-norm based method.

In this letter, motivated by the research of Tian et al., two constructions of asymptotically optimal codebooks in regard to the Welch bound with additive and multiplicative characters are provided. The parameters of constructed codebooks are new, which are different from those in the letter of Tian et al.

This paper proposes a coarse-fine Time-to-Digital Converter (TDC), based on a Ring-Tapped Delay Line (RTDL). The TDC achieves the picosecond's level timing resolution and microsecond's level dynamic range at low cost. The TDC is composed of two coarse time measurement blocks, a time residue generator, and a fine time measurement block. In the coarse blocks, RTDL is constructed by redesigning the conventional Tapped Delay Line (TDL) in a ring structure. A 12-bit counter is employed in one of the two coarse blocks to count the cycle times of the signal traveling in the RTDL. In this way, the input range is increased up to 20.3µs without use of an external reference clock. Besides, the setup time of soft-edged D-flip-flops (SDFFs) adopted in RTDL is set to zero. The adjustable time residue generator picks up the time residue of the coarse block and propagates the residue to the fine block. In the fine block, we use a Vernier Ring Oscillator (VRO) with MOS capacitors to achieve a scalable timing resolution of 11.8ps (1 LSB). Experimental results show that the measured characteristic curve has high-level linearity; the measured DNL and INL are within ± 0.6 LSB and ± 1.5 LSB, respectively. When stimulated by constant interval input, the standard deviation of the system is below 0.35 LSB. The dead time of the proposed TDC is less than 650ps. When operating at 5 MSPS at 3.3V power supply, the power consumption of the chip is 21.5mW. Owing to the use of RTDL and VRO structures, the chip core area is only 0.35mm × 0.28mm in a 0.35µm CMOS process.

One critical "drawback" of the backpropagation algorithm is the local minima problem. We have noted that the local minima problem in the backpropagation algorithm is usually caused by update disharmony between weights connected to the hidden layer and the output layer. To solve this kind of local minima problem, we propose a modified error function with two terms. By adding one term to the conventional error function, the modified error function can harmonize the update of weights connected to the hidden layer and those connected to the output layer. Thus, it can avoid the local minima problem caused by such disharmony. Simulations on some benchmark problems and a real classification task have been performed to test the validity of the modified error function.

It is shown that simple preprocessing on the reference signals in multichannel feedforward ANC system can improve the convergence performance of the adaptive ANC algorithm. A fast and efficient blind preprocessing algorithm in frequency domain is proposed to reduce the computational complexity even that the reference sensors are located far from the noise sources. The permutation problem at different frequency bin is also addressed and solved by an independent vector analysis algorithm. The basic principle and performance comparison are given to verify our conclusion.

With the increase of data quantity, people have begun to attach importance to cloud storage. However, numerous security accidents occurred to cloud servers recently, thus triggering thought about the security of traditional single cloud. In other words, traditional single cloud can't ensure the privacy of users' data to a certain extent. To solve those security issues, multi-cloud systems which spread data over multiple cloud storage servers emerged. They employ a series of erasure codes and other keyless dispersal algorithms to achieve high-level security. But non-systematic codes like RS require relatively complex arithmetic, and systematic codes have relatively weaker security. In terms of keyless dispersal algorithms, they avoid key management issues but not suit to complete parallel optimization or deduplication which is important to limited cloud storage resources. So in this paper, we design a new kind of XOR-based non-systematic erasure codes - Privacy Protecting Codes (PPC) and a SIMD encoding algorithm for better performance. To achieve higher-level security, we put forward a novel deduplication-friendly dispersal algorithm called Hash Cyclic Encryption-PPC (HCE-PPC) which can achieve complete parallelization. With these new technologies, we present a multi-cloud storage system called CloudS. For better user experience and the tradeoffs between security and performance, CloudS provides multiple levels of security by various combinations of compression, encryption and coding schemes. We implement CloudS as a web application which doesn't require users to perform complicated operations on local.

Compressed sensing theory provides a new approach to acquire data as a sampling technique and makes sure that a sparse signal can be reconstructed from few measurements. The construction of compressed sensing matrices is a main problem in compressed sensing theory (CS). In this paper, the deterministic constructions of compressed sensing matrices based on affine singular linear space over finite fields are presented and a comparison is made with the compressed sensing matrices constructed by DeVore based on polynomials over finite fields. By choosing appropriate parameters, our sparse compressed sensing matrices are superior to the DeVore's matrices. Then we use a new formulation of support recovery to recover the support sets of signals with sparsity no more than k on account of binary compressed sensing matrices satisfying disjunct and inclusive properties.

To improve the robustness of the polarization modulation (PM) technique applied in dual-polarized satellite systems, a zero-forcing aided demodulation (ZFAD) method is proposed to eliminate the impairment to the PM from the depolarization effect (DE). The DE elimination is traditionally dependent on the pre-compensation method, which is based on the channel state information (CSI). While the distance between communication partners in satellite systems is so long that the CSI can not be always updated in time at the transmitter side. Therefore, the pre-compensation methods may not perform well. In the ZFAD method, the CSI is estimated at the receiver side and the zero forcing matrix is constructed to process the received signal before demodulating the PM signal. In this way, the DE is eliminated. In addition, we derive the received signal-to-noise ratio expression of the PC and ZFAD methods with the statistical channel model for a better comparison. Theoretical analysis and simulation results demonstrate the ZFAD method can eliminate the DE effect effectively and achieve a better symbol error rate performance than the pre-compensation method.

This paper investigates the secrecy energy efficiency maximization (SEEM) problem in a simultaneous wireless information and power transfer (SWIPT) system, wherein a legitimate user (LU) exploits the power splitting (PS) scheme for simultaneous information decoding (ID) and energy harvesting (EH). To prevent interference from eavesdroppers on the LU, artificial noise (AN) is incorporated into the confidential signal at the transmitter. We maximize the secrecy energy efficiency (SEE) by joining the power of the confidential signal, the AN power, and the PS ratio, while taking into account the minimum secrecy rate requirement of the LU, the required minimum harvested energy, the allowed maximum radio frequency transmission power, and the PS ratio. The formulated SEEM problem involves nonconvex fractional programming and is generally intractable. Our solution is Lagrangian relaxation method than can transform the original problem into a two-layer optimization problem. The outer layer problem is a single variable optimization problem with a Lagrange multiplier, which can be solved easily. Meanwhile, the inner layer one is fractional programming, which can be transformed into a subtractive form solved using the Dinkelbach method. A closed-form solution is derived for the power of the confidential signal. Simulation results verify the efficiency of the proposed SEEM algorithm and prove that AN-aided design is an effective method for improving system SEE.

Recently there has been increased attention to the causality among biomedical signals. The causality between brain structures involved in the generation of alpha activity is examined based on EEG signals acquired simultaneously in the frontal and occipital regions of the scalp. The concept of directed coherence (DC) is introduced as a means of resolving two-signal observations into the constituent components of original signals, the interaction between signals and the influence of one signal source on the other, through autoregressive modeling. The technique was applied to EEG recorded from 11 normal subjects with eyes closed. Through an analysis of the directed coherence, it was found that in both the left and right hemispheres, alpha rhythms with relatively low frequency had a significantly higher correlation in the frontal-occipital direction than in the opposite direction. In the upper alpha frequency band, a significantly higher DC was observed in the occipital-frontal direction, and the right-left DC in the occipital area was consistently higher. The activity of rhythms near 10 Hz was widespread. These results suggest that there is a difference in the genesis and the structure of information transmission in the lower and upper band, and for 10-Hz alpha waves.

It is the key concern for service providers that how a web service stands out among functionally similar services. QoS is a distinct and decisive factor in service selection among functionally similar services. Therefore, how to design services to meet customers' QoS requirements is an urgent problem for service providers. This paper proposes an approach using QFD (Quality Function Deployment) which is a quality methodology to transfer services' QoS requirements into services' design attribute characteristics. Fuzzy set is utilized to deal with subjective and vague assessments such as importance of QoS properties. TCI (Technical Competitive Index) is defined to compare the technical competitive capacity of a web service with those of other functionally similar services in the aspect of QoS. Optimization solutions of target values of service design attributes is determined by GA (Genetic Algorithm) in order to make the technical performance of the improved service higher than those of any other rival service products with the lowest improvement efforts. Finally, we evaluate candidate improvement solutions on cost-effectiveness. As the output of QFD process, the optimization targets and order of priority of service design attributes can be used as an important basis for developing and improving service products.

The existing literature focuses on the applications of fast filter bank due to its excellent frequency responses with low complexity. However, the topic is not addressed related to the general transfer function expressions of the corresponding subfilters for a specific channel. To do this, in this paper, general closed-form transfer function expressions for fast filter bank are derived. Firstly, the cascaded structure of fast filter bank is modelled by a binary tree, with which the index of the subfilter at each stage within the channel can be determined. Then the transfer functions for the two outputs of a subfilter are expressed in a unified form. Finally, the general closed-form transfer functions for the channel and its corresponding subfilters are obtained by variables replacement if the prototype lowpass filters for the stages are given. Analytical results and simulations verify the general expressions. With such closed-form expressions lend themselves easily to analysis and direct computation of the transfer functions and the frequency responses without the structure graph.

In software defined radio systems, a channelizer plays an important role in extracting the desired signals from a wideband signal. Compared to the conventional methods, the proposed scheme provides a solution to design a digital channelizer extracting the multiple subband signals at different center frequencies with low complexity. To do this, this paper formulates the problem as an optimization problem, which minimizes the required multiplications number subject to the constraints of the ripple in the passbands and the stopbands for single channel and combined multiple channels. In addition, a solution to solve the optimization problem is also presented and the corresponding structure is demonstrated. Simulation results show that the proposed scheme requires smaller number of the multiplications than other conventional methods. Moreover, unlike other methods, this structure can process signals with different bandwidths at different center frequencies simultaneously only by changing the status of the corresponding multiplexers without hardware reimplementation.

In this letter, as a generalization of Heng's constructions in the paper [9], a construction of codebooks, which meets the Welch bound asymptotically, is proposed. The parameters of codebooks presented in this paper are new in some cases.

This paper describes a new learning method for Multiple-Value Logic (MVL) networks using the local search method. It is a "non-back-propagation" learning method which constructs a layered MVL network based on canonical realization of MVL functions, defines an error measure between the actual output value and teacher's value and updates a randomly selected parameter of the MVL network if and only if the updating results in a decrease of the error measure. The learning capability of the MVL network is confirmed by simulations on a large number of 2-variable 4-valued problems and 2-variable 16-valued problems. The simulation results show that the method performs satisfactorily and exhibits good properties for those relatively small problems.

In the letter, two classes of optimal codebooks and asymptotically optimal codebooks in regard to the Levenshtein bound are presented, which are based on mutually unbiased bases (MUB) and approximately mutually unbiased bases (AMUB), respectively.

In this letter, as a generalization of Luo et al.'s constructions, a construction of codebook, which meets the Welch bound asymptotically, is proposed. The parameters of codebook presented in this paper are new in some cases.

Mutually unbiased bases (MUBs) are widely used in quantum information processing and play an important role in quantum cryptography, quantum state tomography and communications. It’s difficult to construct MUBs and remains unknown whether complete MUBs exist for any non prime power. Therefore, researchers have proposed the solution to construct approximately mutually unbiased bases (AMUBs) by weakening the inner product conditions. This paper constructs q AMUBs of ℂq, (q + 1) AMUBs of ℂq-1 and q AMUBs of ℂq-1 by using character sums over Galois rings and finite fields, where q is a power of a prime. The first construction of q AMUBs of ℂq is new which illustrates K AMUBs of ℂK can be achieved. The second and third constructions in this paper include the partial results about AMUBs constructed by W. Wang et al. in [9].

A method of learning for multi-layer artificial neural networks is proposed. The learning model is designed to provide an effective means of escape from the Backpropagation local minima. The system is shown to escape from the Backpropagation local minima and be of much faster convergence than simulated annealing techniques by simulations on the exclusive-or problem and the Arabic numerals recognition problem.

A novel scheme to implement a filter bank multicarrier/offset quadrature amplitude modulation (FBMC/OQAM) transmission system is proposed. This is achieved by replacing the existing polyphase filter banks based on FFT/IFFT with fast filter bank (FFB) in order to utilize its good properties such as cascaded structure and high frequency selectivity with a comparable complexity as FFT/IFFT. Although this topic is not addressed in the literature, the impulse response of the prototype filter for each stage within FFB can still be obtained by using an optimization technique, which is used to minimize the distortion caused by intersymbol and interchannel interferences (ISI and ICI) of the proposed FBMC/OQAM transmission system, subject to allowable ripples in the passband and stopband. As a result, the relationship between two-path prototype filters in each subfilter should be modified with a general form accordingly. Simulations show that the number of multiplications required by the proposed scheme is smaller than that needed by the polyphase filter banks solution based on FFT/IFFT. Furthermore, the suitability of the design of prototype filters and the validation can be also supported by the results.