Three-dimensional (3-D) scattering center models use a finite number of point scatterers to efficiently represent complex radar target signature. Using the CLEAN algorithm, 3-D scattering center model is extracted from the inverse synthetic aperture radar (ISAR) image, which is generated based on the shooting and bouncing ray (SBR) technique. The conventional CLEAN extracts the strongest peak iteratively based on the assumption that the scattering centers are isolated. In a realistic target, however, both interference from the closely spaced points and additive noise distort the extraction process. This paper proposes a matched filter-based CLEAN algorithm to improve accuracy efficiently. Using the matched filtering of which impulse response is the known point spread function (PSF), a point most correlated with the PSF is extracted. Thus, the proposed method optimally enhances the accuracy in the presence of massive distortions. Numerical simulations using canonical and realistic targets demonstrate that the extraction accuracy is improved without loss of time-efficiency compared with the existing CLEAN algorithms.

Wireless Sensor Networks (WSNs) are randomly deployed in a hostile environment and left unattended. These networks are composed of small auto mouse sensor devices which can monitor target information and send it to the Base Station (BS) for action. The sensor nodes can easily be compromised by an adversary and the compromised nodes can be used to inject false vote or false report attacks. To counter these two kinds of attacks, the Probabilistic Voting-based Filtering Scheme (PVFS) was proposed by Li and Wu, which consists of three phases; 1) Key Initialization and assignment, 2) Report generation, and 3) En-route filtering. This scheme can be a successful countermeasure against these attacks, however, when one or more nodes are compromised, the re-distribution of keys is not handled. Therefore, after a sensor node or Cluster Head (CH) is compromised, the detection power and effectiveness of PVFS is reduced. This also results in adverse effects on the sensor network's lifetime. In this paper, we propose a Fuzzy Rule-based Key Redistribution Method (FRKM) to address the limitations of the PVFS. The experimental results confirm the effectiveness of the proposed method by improving the detection power by up to 13.75% when the key-redistribution period is not fixed. Moreover, the proposed method achieves an energy improvement of up to 9.2% over PVFS.

In this paper, we propose a new structure for a compact matched filter bank (MFB) for an optical zero-correlation zone (ZCZ) sequence set with Zcz=2z. The proposed MFB can reduces operation elements such as 2-input adders and delay elements. The number of 2-input adders decrease from O(N2) to O(N log2 N), delay elements decrease from O(N2) to O(N). In addition, the proposed MFBs for the sequence of length 32, 64, 128 and 256 with Zcz=2,4 and 8 are implemented on a field programmable gate array (FPGA). As a result, the numbers of logic elements (LEs) of the proposed MFBs for the sequences with Zcz=2 of length 32, 64, 128 and 256 are suppressed to about 76.2%, 84.2%, 89.7% and 93.4% compared to that of the conventional MFBs, respectively.

Based on the known binary and quaternary zero correlation zone (ZCZ) sequence sets, a class of 16-QAM sequence sets with ZCZ is presented, where the term “QAM sequence” means the sequence over the quadrature amplitude modulation (QAM) constellation. The sequence sets obtained by this method achieve an expansion in the number of 16-QAM sequence sets with ZCZ. The proposed sequence sets can be applied to quasi-synchronous code division multiple access (QS-CDMA) systems to eliminate the multiple access interference (MAI) and multipath interference (MPI) and improve the transmission data rate (TDR).

The combination of GPS measurements with the dynamic model via a Kalman filter or an extended Kalman filter, also known as GPS based reduced dynamic orbit determination (RDOD) techniques, have been widely used for accurate and real time navigation of satellites in low earth orbit (LEO). In previous studies, the GPS measurement noise variance is empirically taken as a constant, which is not reasonable because of insufficient prior information or dynamic environment. An improper estimate of the measurement noise may lead to poor performance or even divergence of the filter. In this letter, an adaptive extended Kalman filter (AEKF)-based approach using GPS dual-frequency pseudo-range measurements is presented, where the GPS pseudo-range measurement noise variance is adaptively estimated by the Carrier to Noise Ratio (C/N0) from the tracking loop of GPS receiver. The simulation results show that the proposed AEKF approach can achieve apparent improvements of the position accuracy and almost brings no extra computational burdens for satellite borne processor.

A Tikhonov regularized RLS algorithm with an exponential weighting factor, i.e., a leaky RLS (LRLS) algorithm was proposed by the author. A quadratic version of the LRLS algorithm also exists in the literature of adaptive filters. In this letter, a cubic version of the LRLS filter which is computationally efficient is proposed when the length of the adaptive filter is short. The proposed LRLS filter includes only a divide per iteration although its multiplications and additions increase in number. Simulation results show that the proposed LRLS filter is faster for its short length than the existing quadratic version of the LRLS filter.

This paper presents an extremely low-profile front-end configuration for a base station at quasi-millimeter wave band. It consists of integrated modules of patch antennas and substrate integrated waveguide filters using two printed circuit boards, and transmitter modules using compact GaAs pHEMT three-dimensional monolithic millimeter-wave integrated circuits. The transmitter modules are located around the integrated modules. This is because the proposed front-end configuration can attain extremely low profile, and band-pass filtering performance at quasi-millimeter wave band. As a demonstration of the proposed configuration, 26-GHz-band 4-by-4 elements front-end module is fabricated and tested. The fabricated module has the thickness of about 1 cm, while that offers the attenuation of more than 30 dB with 2 GHz offset from 26 GHz. The proposed configuration can provide base station that can be effective in offering sub-millimeter wave and millimeter-wave bands broadband services for 5G mobile communications systems.

In this paper, we propose filter-and-forward beamforming (FF-BF) for cognitive two-way relay networks in which secondary users employ an orthogonal frequency-division multiplexing (OFDM) system. Secondary transceivers communicate with each other through multiple relays to obtain BF gain as well as to suppress the interference between the primary and secondary users who share the same spectrum. We consider two FF-BF design methods to optimize the relay filter. The first method enhances the quality of service of the secondary network by maximizing the worst subcarrier signal-to-interference-plus-noise ratio (SINR) subject to transmit power constraints. The second method suppresses the interference from the secondary network to the primary network through the minimization of the relay transmission power subject to subcarrier SINR constraints. Simulation results show that the proposed FF-BF improves system performance in comparison to amplify-and-forward relay BF.

Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, good generalization ability, and ease of implementation. However, it has a linear output layer which may limit the capability of exploring the available information, since higher-order statistics of the signals are not taken into account. To address this, we propose a novel ELM architecture in which the linear output layer is replaced by a Volterra filter structure. Additionally, the principal component analysis (PCA) technique is used to reduce the number of effective signals transmitted to the output layer. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. Then we carry out performance evaluation and application analysis for the proposed architecture in the context of supervised classification and unsupervised equalization respectively, and the obtained results either on publicly available datasets or various channels, when compared to those produced by already proposed ELM versions and a state-of-the-art algorithm: support vector machine (SVM), highlight the adequacy and the advantages of the proposed architecture and characterize it as a promising tool to deal with signal processing tasks.

Mixed noise removal is a major problem in image processing. Different noises have different properties and it is required to use an appropriate removal method for each noise. Therefore, removal of mixed noise needs the combination of removal algorithms for each contained noise. We aim at the removal of the mixed noise composed of Additive White Gaussian Noise (AWGN) and Random-Valued Impulse Noise (RVIN). Many conventional methods cannot remove the mixed noise effectively and may lose image details. In this paper, we propose a new mixed noise removal method utilizing Direction Weighted Median filter (DWM filter) and Block Matching and 3D filtering method (BM3D). Although the combination of the DWM filter for RVIN and BM3D for AWGN removes almost all the mixed noise, it still loses some image details. We find the cause in the miss-detection of the image details as RVIN and solve the problem by re-detection with the difference of an input noisy image and the output by the combination. The re-detection process removes only salient noise which BM3D cannot remove and therefore preserves image details. These processes lead to the high performance removal of the mixed noise while preserving image details. Experimental results show our method obtains denoised images with clearer edges and textures than conventional methods.

This paper presents convex filter networks that are obtained from extensions of morphological filters. The proposed filter network consists of a convex and concave filter that are extensions of the dilation and erosion of mathematical morphology with the maxout activation function. Maxout can approximate arbitrary convex functions as piecewise linear functions, including the max function. The class of the convex function hence includes the morphological dilation and can be trained for specific image processing tasks. In this paper, the closing filter is extended to a convex-concave filter network with maxout. The convex-concave filter is trained by the stochastic gradient method for noise and mask removal. The examples of noise and mask removal show that the convex-concave filter can obtain a recovered image, whose quality is comparable to inpainting by using the total variation minimization with reduced computational cost without mask information of the corrupted pixels.

Magnetic Anomaly Detection (MAD) is a passive method for the detection of ferromagnetic objects. Currently, the performance of a MAD system is limited by the magnetic background noise that is non-stationary and shows self-similarity and long-range correlation. In this paper, we propose an empirical mode decomposition (EMD) trend filtering based energy detector for adaptively detecting the magnetic anomaly signal from the background noise. The input data is first detrended adaptively with the energy-ratio trend filtering approach. Then, the magnetic anomaly signal is detected using an energy detector. The proposed detector does not need any a priori knowledge about the target or assumptions regarding the background noise. Experiments also prove that the proposed detector shows a more stable performance than the existing undecimated discrete wavelet transform (UDWT) based energy detector.

In this letter, we propose a robust and fast tracking framework by combining local and global appearance models to cope with partial occlusion and pose variations. The global appearance model is represented by a correlation filter to efficiently estimate the movement of the target and the local appearance model is represented by local feature points to handle partial occlusion and scale variations. Then global and local appearance models are unified via the Bayesian inference in our tracking framework. We experimentally demonstrate the effectiveness of the proposed method in both terms of accuracy and time complexity, which takes 12ms per frame on average for benchmark datasets.

We present dynamic mode switching characteristic by using a 2 × 2 optical mode switch based on silicon waveguide. The configuration of optical mode switch is similar to MZI where the width of input and output ports are designed to permit the combining of the fundamental mode and the first order mode. We designed the symmetrical arms with phase shifter based on p-i-n structure in one arm to generate a π-phase difference between each arm. As a result, mode switching with the injection current of 60mA (5.7V) was successfully achieved with the mode crosstalk of -10dB at λ=1550nm. A minimum of less than 60ns and 40ns mode switching time for the fundamental mode to first order mode and first order mode to fundamental mode, was achieved respectively in this time.

This paper presents a programmable wideband low pass filter (LPF) with Continuous-Time (CT)/Discrete-Time (DT) hybrid architecture. Unlike the conventional DT LPF, the proposed LPF eliminates sample & hold circuits, enabling to expand available bandwidth. The transfer function and the influence of the circuit imperfection are derived from CT/DT hybrid analysis. A prototype has been fabricated in 40 nm CMOS process. The proposed LPF achieves 2.5 GHz bandwidth by wideband equalization, which offers capacitance ratio (Cratio) and clock frequency (fCK) programmability. The proposed LPF occupies only 0.048 mm2 of active area.

Bloom Filter is a bit array (a one-dimensional storage structure) that provides a compact representation for a set of data, which can be used to answer the membership query in an efficient manner with a small number of false positives. It has a lot of applications in many areas. In this paper, we extend the design of Bloom Filter by using a multi-dimensional matrix to replace the one-dimensional structure with three different implementations, namely OFFF, WOFF, FFF. We refer the extended Bloom Filter as Feng Filter. We show the false positive rates of our method. We compare the false positive rate of OFFF with that of the traditional one-dimensional Bloom Filter and show that under certain condition, OFFF has a lower false positive rate. Traditional Bloom Filter can be regarded as a special case of our Feng Filter.

This paper presents a novel identification method for hybrid dynamical system models, where parameters have stochastic and time-varying characteristics. The proposed parameter identification scheme is based on a modified implementation of particle filtering, together with a time-smoothing technique. Parameters of the identified model are considered as time-varying random variables. Parameters are identified independently at each time step, using the Bayesian inference implemented as an iterative particle filtering method. Parameters time dynamics are smoothed using a distribution based moving average technique. Modes of the hybrid system model are handled independently, allowing any type of nonlinear piecewise model to be identified. The proposed identification scheme has low computation burden, and it can be implemented for online use. Effectiveness of the scheme is verified by numerical experiments, and an application of the method is proposed: analysis of driving behavior through identified time-varying parameters.

A Wiener-based inpainting quality prediction method is presented in this paper. The proposed method is the first method that can predict inpainting quality both before and after the intensities have become missing even if their inpainting methods are unknown. Thus, when the target image does not include any missing areas, the proposed method estimates the importance of intensities for all pixels, and then we can know which areas should not be removed. Interestingly, since this measure can be also derived in the same manner for its corrupted image already including missing areas, the expected difficulty in reconstruction of these missing pixels is predicted, i.e., we can know which missing areas can be successfully reconstructed. The proposed method focuses on expected errors derived from the Wiener filter, which enables least-squares reconstruction, to predict the inpainting quality. The greatest advantage of the proposed method is that the same inpainting quality prediction scheme can be used in the above two different situations, and their results have common trends. Experimental results show that the inpainting quality predicted by the proposed method can be successfully used as a universal quality measure.

Multichannel speech enhancement systems (MSES') have been widely utilized for diverse types of speech interface applications. A state-of-the-art MSES primarily utilizes multichannel minima-controlled recursive averaging for noise estimations and a parameterized multichannel Wiener filter for noise reduction. Many MSES' are implemented in the frequency domain, but they are computationally burdensome due to the numerous complex matrix operations involved. In this paper, a novel MSES intended to reduce the computational complexity with improved performance is proposed. The proposed system is implemented in the mel-filterbank domain using a frequency-averaging technique. Through a performance evaluation, it is verified that the proposed mel-filterbank MSES achieves improvements in the perceptual speech quality with a reduced level of computation compared to a conventional MSES.

As a new generation voice service, Voice over LTE (VoLTE) has attracted worldwide attentions in both the academia and industry. Different from the traditional voice call based on circuit-switched (CS), VoLTE evolves into the packet-switched (PS) field, which has long been open to the public. Though designed rigorously, similar to VoIP services, VoLTE also suffers from SIP (Session Initiation Protocal) flooding attacks. Due to the high performance requirement, the SIP flooding attacks in VoLTE is more difficult to defend than that in traditional VoIP service. In this paper, enlightened by Counting Bloom Filter (CBF), we design a versatile CBF-like structure, PFilter, to detect the flooding anomalies. Compared with previous relevant works, our scheme gains advantages in many aspects including detection of low-rate flooding attack and stealthy flooding attack. Moreover, not only can our scheme detect the attacks with high accuracy, but also find out the attackers to ensure normal operation of VoLTE by eliminating their negative effects. Extensive experiments are performed to well evaluate the performance of the proposed scheme.