This paper presents an overview of the advance of the China millimeter-wave multiple gigabit (CMMG) wireless local area network (WLAN) system which operates in the 45 GHz frequency band. The CMMG WLAN system adopts the multiple antennas technologies to support data rate up to 15Gbps. During the progress of CMMG WLAN standardization, some new key technologies were introduced to adapt the millimeter-wave characteristic, including the usage of the zero correlation zone (ZCZ) sequence, a novel lower density parity check code (LDPC)-based packet encoding, and multiple input multiple output (MIMO) single carrier transmission. Extensive numerical results and system prototype test are also given to validate the performance of the technologies adopted by CMMG WLAN system.

This paper presents a 44 Gbit/s Transimpedance Amplifier (TIA) with wide-dynamic range and high-linearity for optical receiver fabricated in 130 nm BiCMOS technology. The TIA has the features of 67dBΩ overall transimpedance gain, a bandwidth of 28GHz, 10pA/√Hz of Input Referred Noise Current Power Spectral Density (IRNCPSD), and a power consumption of 95mW from a 2.5V supply. The Total Harmonic Distortion (THD) is less than 5% for a differential input current up to 2.63mApp, when the static input current is 0.1mA.

Block-state realization of state-space digital filters offers reduced implementation complexity relative to canonical state-space filters while filter's internal structure remains accessible. In this paper, we present a quantitative analysis on l2 coefficient sensitivity of block-state digital filters. Based on this, we develop two techniques for minimizing average l2-sensitivity subject to l2-scaling constraints. One of the techniques is based on a Lagrange function and some matrix-theoretic techniques. The other solution method converts the problem at hand into an unconstrained optimization problem which is solved by using an efficient quasi-Newton algorithm where the key gradient evaluation is done in closed-form formulas for fast and accurate execution of quasi-Newton iterations. A case study is presented to demonstrate the validity and effectiveness of the proposed techniques.

A method for accurate estimation of personalized video preference using multiple users' viewing behavior is presented in this paper. The proposed method uses three kinds of features: a video, user's viewing behavior and evaluation scores for the video given by a target user. First, the proposed method applies Supervised Multiview Spectral Embedding (SMSE) to obtain lower-dimensional video features suitable for the following correlation analysis. Next, supervised Multi-View Canonical Correlation Analysis (sMVCCA) is applied to integrate the three kinds of features. Then we can get optimal projections to obtain new visual features, “canonical video features” reflecting the target user's individual preference for a video based on sMVCCA. Furthermore, in our method, we use not only the target user's viewing behavior but also other users' viewing behavior for obtaining the optimal canonical video features of the target user. This unique approach is the biggest contribution of this paper. Finally, by integrating these canonical video features, Support Vector Ordinal Regression with Implicit Constraints (SVORIM) is trained in our method. Consequently, the target user's preference for a video can be estimated by using the trained SVORIM. Experimental results show the effectiveness of our method.

In this paper, we consider to develop a recovery algorithm of a sparse signal for a compressed sensing (CS) framework over finite fields. A basic framework of CS for discrete signals rather than continuous signals is established from the linear measurement step to the reconstruction. With predetermined priori distribution of a sparse signal, we reconstruct it by using a message passing algorithm, and evaluate the performance obtained from simulation. We compare our simulation results with the theoretic bounds obtained from probability analysis.

In social and information network analysis, ranking has been considered to be one of the most fundamental and important tasks where the goal is to rank the nodes of a given graph according to their importance. For example, the PageRank and the HITS algorithms are well-known ranking methods. While these traditional ranking methods focus only on the structure of the entire network, we propose to incorporate a local view into node ranking by exploiting the clustering structure of real-world networks. We develop localized ranking mechanisms by partitioning the graphs into a set of tightly-knit groups and extracting each of the groups where the localized ranking is computed. Experimental results show that our localized ranking methods rank the nodes quite differently from the traditional global ranking methods, which indicates that our methods provide new insights and meaningful viewpoints for network analysis.

This paper describes the design and experiment results of a 25Gbps vertical-cavity surface emitting laser (VCSEL) driver circuit for a multi channel optical transmitter. To compensate for the non-linearity of the VCSEL and achieve high speed data rate communication, an asymmetric pre-emphasis technique is proposed for the VCSEL driver. An asymmetric pre-emphasis signal can be created by adjusting the duty ratio of the emphasis signal. The VCSEL driver adopts a double cascode connection that can apply a drive current from a high voltage DC bias and feed-forward compensation that can enhance the band-width for common-cathode VCSEL. For the design of the optical module structure, a two-tier low temperature co-fired ceramics (LTCC) package is adopted to minimize the wire bonding between the signal pad on the LTCC and the anode pad on the VCSEL. This structure and circuit reduces the simulated deterministic jitter from 12.7 to 4.1ps. A test chip was fabricated with the 65-nm standard CMOS process and demonstrated to work as an optical transmitter. An experimental evaluation showed that this VCSEL driver with asymmetric pre-emphasis reduced the total deterministic jitter up to 8.6ps and improved the vertical eye opening ratio by 3% compared with symmetric pre-emphasis at 25Gbps with a PRBS=29-1 test signal. The power consumption of the VCSEL driver was 3.0mW/Gbps/ch at 25Gbps. An optical transmitter including the VCSEL driver achieved 25-Gbps, 4-ch fully optical links.

The method for estimating propagation loss that classifies receiving points into multiple groups by focusing on the number of reflections and diffractions, and applies a separate statistical model to each group was extended from only 2.4 GHz band to both 2.4 GHz and 5 GHz band. The extended statistical model was created from received power measurements. First, an appropriate grouping method was investigated based on the fitting error of statistical model. Non-line-of-sight (NLOS) receiving points were grouped in order of points that a wave reflected one time reaches, points that a wave reflected two times reaches, and points that a wave diffracted one time reaches. Next, the effectiveness of the proposed method was verified by comparison with conventional statistical models (one-slope, dual-slope, multi-wall, partitioned) on three office floors that differ from the environment used to create the statistical model. The average NLOS estimation error for the three evaluation environments was 4.9 dB, demonstrating that the proposed method has accuracy equal to or better than that of conventional methods.

Power amplifiers (PAs) are key components of mobile base stations. In the last decade, the power efficiency of PAs for 3G/4G mobile base stations has risen to over 50% as a result of employing efficiency enhancement techniques, such as Doherty, envelope tracking, and outphasing, in combination with GaN devices and digital predistortion. This trend has significantly contributed to reducing the power consumption of mobile base stations. Furthermore, digital transmitters using switch-mode PAs have the potential of breaking through the 70% efficiency level. Achieving this goal will require advances not only in circuitry but also in device technology. For active antenna systems of 5G mobile systems, ease of integration, as well as high efficiency, becomes important for PAs, and thus, Si-based devices will play a major role.

Compressive spectral imaging (CSI) systems capture the 3D spatiospectral data by measuring the 2D compressed focal plane array (FPA) coded projection with the help of reconstruction algorithms exploiting the sparsity of signals. However, the contradiction between the multi-dimension of the scenes and the limited dimension of the sensors has limited improvement of recovery performance. In order to solve the problem, a novel CSI system based on a coded aperture snapshot spectral imager, RGB-CASSI, is proposed, which has two branches, one for CASSI, another for RGB images. In addition, considering that conventional reconstruction algorithms lead to oversmoothing, a RGB-guided low-rank (RGBLR) method for compressive hyperspectral image reconstruction based on compressed sensing and coded aperture spectral imaging system is presented, in which the available additional RGB information is used to guide the reconstruction and a low-rank regularization for compressive sensing and a non-convex surrogate of the rank is also used instead of nuclear norm for seeking a preferable solution. Experiments show that the proposed algorithm performs better in both PSNR and subjective effects compared with other state-of-art methods.

This paper shows a describing method of an image processing software in C for high-level synthesis (HLS) technology considering function chaining to realize an efficient hardware. A sophisticated image processing would be built on the sequence of several primitives represented as sub-functions like the gray scaling, filtering, binarization, thinning, and so on. Conventionally, generic describing methods for each sub-function so that HLS technology can generate an efficient hardware module have been shown. However, few studies have focused on a systematic describing method of the single top function consisting of the sub-functions chained. According to the proposed method, any number of sub-functions can be chained, maintaining the pipeline structure. Thus, the image processing can achieve the near ideal performance of 1 pixel per clock even when the processing chain is long. In addition, implicitly, the deadlock due to the mismatch of the number of pushes and pops on the FIFO connecting the functions is eliminated and the interpolation of the border pixels is done. The case study on a canny edge detection including the chain of some sub-functions demonstrates that our proposal can easily realize the expected hardware mentioned above. The experimental results on ZYNQ FPGA show that our proposal can be converted to the pipelined hardware with moderate size and achieve the performance gain of more than 70 times compared to the software execution. Moreover, the reconstructed C software program following our proposed method shows the small performance degradation of 8% compared with the pure C software through a comparative evaluation preformed on the Cortex A9 embedded processor in ZYNQ FPGA. This fact indicates that a unified image processing library using HLS software which can be executed on CPU or hardware module for HW/SW co-design can be established by using our proposed describing method.

In this paper, we consider a similarity control problem for nondeterministic discrete event systems, which requires us to synthesize a nonblocking supervisor such that the supervised plant is simulated by a given specification. We assume that a supervisor can observe not only the event occurrence but also the current state of the plant. We present a necessary and sufficient condition for the existence of a nonblocking supervisor that solves the similarity control problem and show how to verify it in polynomial time. Moreover, when the existence condition of a nonblocking supervisor is satisfied, we synthesize such a supervisor as a solution to the similarity control problem.

In this work, the metal-oxide-metal (MOM) capacitor in the scaled CMOS process has been modeled at high frequencies using an EM simulator, and its layout has been optimized. The modeled parasitic resistance consists of four components, and the modeled parasitic inductance consists of the comb inductance and many mutual inductances. Each component of the parasitic resistance and inductance show different degrees of dependence on the finger length and on the number of fingers. The substrate network parameters also have optimum points. As such, the geometric dependence of the characteristics of the MOM capacitor is investigated and the optimum layout in the constant-capacitance case is proposed by calculating the results of the model. The proposed MOM capacitor structures for 50fF at f =60GHz are L =5μm with M =3, and, L =2μm with M =5 and that for 100fF at f =30GHz are L =9μm with M =3, and L =4μm with M =5. The target process is 65-nm CMOS.

This research addresses improvements in the efficiency of spectrum utilization by defending against jamming attacks and corrupting the communications of the adversary network by executing its own jamming strategy. The proposed scheme, based on game theory, selects the best operational strategy (i.e., communications and jamming strategies) to maximize the successful communications and jamming rates of the network. Moreover, an estimation algorithm is investigated to predict the behavior of the adversary network in order to improve the efficiency of the proposed game theory-based scheme.

In this paper, we propose an indoor localization method that uses only the Received Signal Strength Indicator (RSSI) of signals transmitted from wireless beacons. The beacons use three-element array antennas, and the position of the receiving terminal is estimated by using multiple DOD information. Each beacon transmits four beacon signals with different directivities by feeding signals to the three-element array antennas via 180-degree and 90-degree hybrids. The correlation matrix of the propagation channels is estimated from just the strength of the signals, and the DOD is estimated from the calculated correlation matrix. For determining the location of the receiving terminal, the existence probability function is introduced. Experiments show that the proposed method attains lower position estimation error than the conventional method.

This paper proposes a novel method of reducing channel state information (CSI) feedback by using transmit antenna selection for downlink multiuser multiple input multiple output (DL-MU-MIMO) transmission in dense distributed antenna systems. It is widely known that DL-MU-MIMO transmission achieves higher total bit-rate by mitigating inter-user interference based on pre-coding techniques. The pre-coding techniques require CSI between access point (AP) and multiple users. However, overhead for CSI acquisition degrades the transmission efficiency of DL-MU-MIMO transmission. In the proposed CSI feedback reduction method, AP first selects the antenna set that maximizes the received power at each user, second it skips the sequence of CSI feedback for users whose signal to interference power ratio is larger than a threshold, and finally it performs DL-MU-MIMO transmission to multiple users by using the selected antenna set. To clarify the proposed method, we evaluate it by computer simulations in an indoor scenario. The results show that the proposed method can offer higher transmission efficiency than the conventional DL-MU-MIMO transmission with the usual CSI feedback method.

Seed detection or sometimes known as nuclei detection is a prerequisite step of nuclei segmentation which plays a critical role in quantitative cell analysis. The detection result is considered as accurate if each detected seed lies only in one nucleus and is close to the nucleus center. In previous works, voting methods are employed to detect nucleus center by extracting the nucleus saliency features. However, these methods still encounter the risk of false seeding, especially for the heterogeneous intensity images. To overcome the drawbacks of previous works, a novel detection method is proposed, which is called secant normal voting. Secant normal voting achieves good performance with the proposed skipping range. Skipping range avoids over-segmentation by preventing false seeding on the occlusion regions. Nucleus centers are obtained by mean-shift clustering from clouds of voting points. In the experiments, we show that our proposed method outperforms the comparison methods by achieving high detection accuracy without sacrificing the computational efficiency.

Two different types of representations, such as an image and its manually-assigned corresponding labels, generally have complex and strong relationships to each other. In this paper, we represent such deep relationships between two different types of visible variables using an energy-based probabilistic model, called a deep relational model (DRM) to improve the prediction accuracies. A DRM stacks several layers from one visible layer on to another visible layer, sandwiching several hidden layers between them. As with restricted Boltzmann machines (RBMs) and deep Boltzmann machines (DBMs), all connections (weights) between two adjacent layers are undirected. During maximum likelihood (ML) -based training, the network attempts to capture the latent complex relationships between two visible variables with its deep architecture. Unlike deep neural networks (DNNs), 1) the DRM is a totally generative model and 2) allows us to generate one visible variables given the other, and 2) the parameters can be optimized in a probabilistic manner. The DRM can be also fine-tuned using DNNs, like deep belief nets (DBNs) or DBMs pre-training. This paper presents experiments conduced to evaluate the performance of a DRM in image recognition and generation tasks using the MNIST data set. In the image recognition experiments, we observed that the DRM outperformed DNNs even without fine-tuning. In the image generation experiments, we obtained much more realistic images generated from the DRM more than those from the other generative models.

In this paper, a novel method for pitch estimation and voicing classification is proposed using reconstructed spectrum from Mel-frequency cepstral coefficients (MFCC). The proposed algorithm reconstructs spectrum from MFCC with Moore-Penrose pseudo-inverse by Mel-scale weighting functions. The reconstructed spectrum is compressed and filtered in log-frequency. Pitch estimation is achieved by modeling the joint density of pitch frequency and the filter spectrum with Gaussian Mixture Model (GMM). Voicing classification is also achieved by GMM-based model, and the test results show that over 99% frames can be correctly classified. The results of pitch estimation demonstrate that the proposed GMM-based pitch estimator has high accuracy, and the relative error is 6.68% on TIMIT database.

In this paper, we describe the direct learning of an end-to-end mapping between under-/over-exposed images and well-exposed images. The mapping is represented as a deep convolutional neural network (CNN) that takes multiple-exposure images as input and outputs a high-quality image. Our CNN has a lightweight structure, yet gives state-of-the-art fusion quality. Furthermore, we know that for a given pixel, the influence of the surrounding pixels gradually increases as the distance decreases. If the only pixels considered are those in the convolution kernel neighborhood, the final result will be affected. To overcome this problem, the size of the convolution kernel is often increased. However, this also increases the complexity of the network (too many parameters) and the training time. In this paper, we present a method in which a number of sub-images of the source image are obtained using the same CNN model, providing more neighborhood information for the convolution operation. Experimental results demonstrate that the proposed method achieves better performance in terms of both objective evaluation and visual quality.