In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.

We experimentally investigated the impact of the mode filtering technique on the performances of pulse amplification in a fiber with a large core diameter. The technique was applied to a femtosecond pulse amplifier, and was based on a large area double-clad Yb-doped fiber. The mode filtering enabled selective excitation of the lowest transverse mode with minimal contamination of higher order modes. The output pulses with 110 fs duration, > 30 nJ pulse energy (> 3 W average power), and clean spatial/temporal profiles were successfully generated. Benefits of this technique are also discussed.

This letter presents performance analysis in the high signal-to-noise ratio (SNR) region for matched filter (MF) precoding in single cell Massive MIMO systems. The outage probability function is derived in closed form, and the data rate of each user is also given. We have also presented asymptotic analysis in terms of data rate for MF when the number of users and the number of antennas grow without bounds. The expressions of these analytical results are rather simple and are thus convenient for overall performance evaluation. The simulation results show that the analysis are very accurate.

In this Letter, a new iris recognition approach based on local Gabor orientation feature is proposed. On one hand, the iris feature extraction method using the traditional Gabor filters can cause time-consuming and high-feature dimension. On the other hand, we can find that the changes of original iris texture in angle and radial directions are more obvious than the other directions by observing the iris images. These changes in the preprocessed iris images are mainly reflected in vertical and horizontal directions. Therefore, the local directional Gabor filters are constructed to extract the horizontal and vertical texture characteristics of iris. First, the iris images are preprocessed by iris and eyelash location, iris segmentation, normalization and zooming. After analyzing the variety of iris texture and 2D-Gabor filters, we construct the local directional Gabor filters to extract the local Gabor features of iris. Then, the Gabor & Fisher features are obtained by Linear Discriminant Analysis (LDA). Finally, the nearest neighbor method is used to recognize the iris. Experimental results show that the proposed method has better iris recognition performance with less feature dimension and calculation time.

Ultra high definition (UHD) imaging systems have attracted much attention as a next generation television (TV) broadcasting service and video streaming service. However, the state of the art video coding standards including H.265/HEVC has not enough compression rate for streaming, broadcasting and storing UHD. Existing coding standard such as H.265/HEVC normaly use RGB-YCbCr color transform before compressing RGB color image since that procedure can decorrelate color components well. However, there is room for improvement on the coding efficiency for color image based on an observation that the luminance and chrominance components changes in same locations. This observation inspired us to propose a new post-processing method for compressed images by using weighted least square (WLS) filter with coded luminance component as a guide image, for refining the edges of chrominance components. Since the computational cost of WLS tends to superlinearly increase with increasing image size, it is difficult to apply it to UHD images. To overcome this problem, we propose slightly overlapped block partitioning and a new variant of WLS (constrained WLS, CWLS). Experimental results of objective quality comparison and subjective assessment test using 4K images show that our proposed method can outperform the conventional method and reduce the bit amount for chrominance component drastically with preserving the subjective quality.

In this paper, we propose Affine Combination Lattice Algorithm (ACLA) as a new lattice-based adaptive notch filtering algorithm. The ACLA makes use of the affine combination of Regalia's Simplified Lattice Algorithm (SLA) and Lattice Gradient Algorithm (LGA). It is proved that the ACLA has faster convergence speed than the conventional lattice-based algorithms. We conduct this proof by means of theoretical analysis of the mean update term. Specifically, we show that the mean update term of the ACLA is always larger than that of the conventional algorithms. Simulation examples demonstrate the validity of this analytical result and the utility of the ACLA. In addition, we also derive the step-size bound for the ACLA. Furthermore, we show that this step-size bound is characterized by the gradient of the mean update term.

To eliminate CAMs from the load/store queues, several techniques to detect memory access order violation with hash filters composed of RAMs have been proposed. This paper proposes a technique with parallel counting Bloom filters (PCBF). A Bloom filter has extremely low false positive rates owing to multiple hash functions. Although some existing researches claim the use of Bloom filters, none of them make mention to multiple hash functions. This paper also addresses the problem relevant to the variety of access sizes of load/store instructions. The evaluation results show that our technique, with only 2720-bit Bloom filters, achieves a relative IPC of 99.0% while the area and power consumption are greatly reduced to 14.3% and 22.0% compared to a conventional model with CAMs. The filter is much smaller than usual branch predictors.

In this paper, a design method for the infinite impulse response (IIR) filters using the particle swarm optimization (PSO) is developed. It is well-known that the updating in the PSO tends to stagnate around local minimums due to a strong search directivity. Recently, the asynchronous digenetic PSO with nonlinear dissipative term (N-AD-PSO) has been proposed as a purpose for a diverse search. Therefore, it can be expected that the stagnation can be avoided by the N-AD-PSO. However, there is no report that the N-AD-PSO has been applied to any realistic problems. In this paper, the N-AD-PSO is applied for the IIR filter design. Several examples are shown to clarify the effectiveness and the drawback of the proposed method.

Current mobile communication terminals are equipped with multiple RF circuits that cover all frequency bands assigned for the communication. In order to make efficient use of frequency spectrum and to reduce circuits in a terminal, a low-loss reconfigurable RF filter is necessary to flexibly change RF frequencies. In this paper, a new reconfigurable bandpass filter (BPF) having eight-frequency (three-bit) selection capability is proposed. It employs branch-line switched type variable resonators that provide low insertion loss. One of the design issues is how to control pass bandwidths among selectable frequencies. In order to analyze the bandwidth variation of the reconfigurable BPF, we calculate the changes of external Q and coupling coefficients. It is shown that the inductive coupling design can achieve less variation of bandwidth for the reconfigurable BPF, compared with commonly used capacitive coupling design. A prototype BPF on a printed circuit board with high dielectric constant substrate has been fabricated and evaluated in 2 GHz bands. It presents performance very close to the design results with respect to insertion loss, center frequency and passband bandwidth. Low insertion loss of less than 1 dB is achieved among the eight frequencies.

This invited paper aims to present an overview of our recent research and development (R&D) of advanced microwave planar filters, in particular with miniaturization and/or electronically tunable/ reconfigurable functionalities, which are in demand for future communication/radar systems as well as emerging wireless applications.

In this paper, we propose an error-correction low-pass filter (EC-LPF) algorithm for estimating the wireless distance between devices. To measure this distance, the received signal strength indication (RSSI) is a popularly used method because the RSSI of a wireless signal, such as Wi-Fi and Bluetooth, can be measured easily without the need for additional hardware. However, estimating the wireless distance using an RSSI is known to be difficult owing to the occurrence of inaccuracies. To examine the inaccuracy characteristics of Bluetooth RSSI, we conduct a preliminary test to discover the relationship between the actual distance and Bluetooth RSSI under several different environments. The test results verify that the main reason for inaccuracy is the existence of measurement errors in the raw Bluetooth RSSI data. In this paper, the EC-LPF algorithm is proposed to reduce measurement errors by alleviating fluctuations in a Bluetooth signal with responsiveness for real-time applications. To evaluate the effectiveness of the EC-LPF algorithm, distance accuracies of different filtering algorithms are compared, namely, a low-pass filer (LPF), a Kalman filter, a particle filter, and the EC-LPF algorithm under two different environments: an electromagnetic compatibility (EMC) chamber and an indoor hall. The EC-LPF algorithm achieves the best performance in both environments in terms of the coefficient of determination, standard deviation, measurement range, and response time. In addition, we also implemented a meeting room application to verify the feasibility of the EC-LPF algorithm. The results prove that the EC-LPF algorithm distinguishes the inside and outside areas of a meeting room without error.

A novel method was developed to expand and adjust the bandwidth of long-period fiber gratings (LPFGs) as band-rejection filters. The band-rejection filters were constructed by concatenating two LPFGs with an appropriate space, that causes a $pi$-phase shift. The component LPFGs with the same period and the different numbers of periods are designed to have $-$3-dB transmission at wavelengths on both sides of a resonance wavelength symmetrically, and the transmission loss of the concatenated LPFGs peaks at the -3-dB transmission wavelengths. The rejection bandwidth was widened by changing the interval between the -3-dB transmission wavelengths. The concatenated LPFGs were simulated by using a transfer-matrix method based on a discrete coupling model, and were fabricated by a point-by-point arc discharge technique on the basis of the simulation results. It was demonstrated that the rejection bandwidth at 20-dB attenuation reached 26.6,nm and was 2.7 times broader than that of a single uniform LPFG.

Objects tracking methods have been wildly used in the field of video surveillance, motion monitoring, robotics and so on. Particle filter is one of the promising methods, but it is difficult to apply to real-time objects tracking because of its high computation cost. In order to reduce the processing cost without sacrificing the tracking quality, this paper proposes a new method for real-time 3D objects tracking, using parallelized particle filter algorithms by MapReduce architecture which is running on GPGPU. Our methods are as follows. First, we use a Kinect to get the 3D information of objects. Unlike the conventional 2D-based objects tracking, 3D objects tracking adds depth information. It can track not only from the x and y axis but also from the z axis, and the depth information can correct some errors in 2D objects tracking. Second, to solve the high computation cost problem, we use the MapReduce architecture on GPGPU to parallelize the particle filter algorithm. We implement the particle filter algorithms on GPU and evaluate the performance by actually running a program on CUDA5.5.

Content-centric networking (CCN) is an innovative network architecture that is being considered as a successor to the Internet. In recent years, CCN has received increasing attention from all over the world because its novel technologies (e.g., caching, multicast, aggregating requests) and communication based on names that act as addresses for content have the potential to resolve various problems facing the Internet. To implement these technologies, however, requires routers with performance far superior to that offered by today's Internet routers. Although many researchers have proposed various router components, such as caching and name lookup mechanisms, there are few router-level designs incorporating all the necessary components. The design and evaluation of a complete router is the primary contribution of this paper. We provide a concrete hardware design for a router model that uses three basic tables — forwarding information base (FIB), pending interest table (PIT), and content store (CS) — and incorporates two entities that we propose. One of these entities is the name lookup entity, which looks up a name address within a few cycles from content-addressable memory by use of a Bloom filter; the other is the interest count entity, which counts interest packets that require certain content and selects content worth caching. Our contributions are (1) presenting a proper algorithm for looking up and matching name addresses in CCN communication, (2) proposing a method to process CCN packets in a way that achieves high throughput and very low latency, and (3) demonstrating feasible performance and cost on the basis of a concrete hardware design using distributed content-addressable memory.

Square-root Nyquist transmit filtering is typically used in single-carrier (SC) transmission. By changing the filter roll-off factor, the bit-error rate (BER), peak-to-average power ratio (PAPR), and spectrum efficiency (SE) changes, resulting in a tradeoff among these performance indicators. In this paper, assuming SC with frequency-domain equalization (SC-FDE), we design a new transmit filtering based on the minimum variance of instantaneous transmit power (VIP) criterion in order to reduce the PAPR of the transmit signal of SC-FDE. Performance evaluation of SC-FDE using the proposed transmit filtering is done by computer simulation, and shows that the proposed transmit filtering contributes lower transmit PAPR, while there exists only a small degradation in BER performance compared to SC-FDE using square-root Nyquist filtering.

Recommender systems, which provide users with recommendations of content suited to their needs, have received great attention in today's online business world. However, most recommendation approaches exploit only a single source of input data and suffer from the data sparsity problem and the cold start problem. To improve recommendation accuracy in this situation, additional sources of information, such as friend relationship and user-generated tags, should be incorporated in recommendation systems. In this paper, we revise the user-based collaborative filtering (CF) technique, and propose two recommendation approaches fusing user-generated tags and social relations in a novel way. In order to evaluate the performance of our approaches, we compare experimental results with two baseline methods: user-based CF and user-based CF with weighted friendship similarity using the real datasets (Last.fm and Movielens). Our experimental results show that our methods get higher accuracy. We also verify our methods in cold-start settings, and our methods achieve more precise recommendations than the compared approaches.

A class-E power amplifier (PA) with novel dynamic biasing scheme is proposed to enhance power added efficiency (PAE) over a wide power range. A look-up table (LUT) adjusts input power and drain supply voltage simultaneously to keep switch mode condition of a power transistor and to optimize the PAE. Experimental results show that the class-E PA using the proposed scheme with harmonic suppression filter gives the PAE higher than 80% over 8.5,dB range with less than 40,dBc harmonic suppression.

The indoor fingerprinting localization technology has received more attention in recent years due to the increasing demand of the indoor location based services (LBSs). However, a high quality of the LBS requires a positioning solution with high accuracy and low computational complexity. The particle swarm optimization (PSO) technique, which emulates the social behavior of a flock of birds to search for the optimal solution of a special problem, can provide attractive performance in terms of accuracy, computational efficiency and convergence rate. In this paper, we adopt the PSO algorithm to estimate the location information. First, our system establishes a Bayesian-rule based objective function. It then applies PSO to identify the optimal solution. We also propose a hybrid access point (AP) selection method to improve the accuracy, and analyze the effects of the number and the initial positions of particles on the localization performance. In order to mitigate the estimation error, we use the Kalman Filter to update the initial estimated location via the PSO algorithm to track the trail of the mobile user. Our analysis indicates that our method can reduce the computational complexity and improve the real-time performance. Numerous experiments also demonstrate that our proposed localization and tracking system achieve higher localization accuracy than existing systems.

This paper presents a filtering method for efficient face image retrieval over large volume of face databases. The proposed method employs a new face image descriptor, called a cell-orientation vector (COV). It has a simple form: a 72-dimensional vector of integers from 0 to 8. Despite of its simplicity, it achieves high accuracy and efficiency. Our experimental results show that the proposed method based on COVs provides better performance than a recent approach based on identity-based quantization in terms of both accuracy and efficiency.

In this letter, we propose a simple framework for accelerating a state-of-the-art histogram-based weighted median filter at no expense. It is based on a process of determining the filter processing direction. The determination is achieved by measuring the local feature variation of input images. Through experiments with natural images, it is verified that, depending on input images, the filtering speed can be substantially increased by changing the filtering direction.