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.

We propose a framework to detect lug position and orientation in robotics that is insensitive to the lug orientation, incorporating a proposed optimization based on the artificial bee colony genetic algorithm. Experimental results show that the proposed optimization method outperformed traditional artificial bee colony and other meta-heuristics in the considered cases and was up to 3 times faster than the traditional approach. The proposed detection framework provided excellent performance to detect lug objects for all test cases.

Non-linear precoding (NLP) scheme for downlink multi-user multiple-input multiple-output (DL-MU-MIMO) transmission has received much attention as a promising technology to achieve high capacity within the limited bandwidths available to radio access systems. In order to minimize the required transmission power for DL-MU-MIMO and achieve high spectrum efficiency, Vector Perturbation (VP) was proposed as an optimal NLP scheme. Unfortunately, the original VP suffers from significant computation complexity in detecting the optimal perturbation vector from an infinite number of the candidates. To reduce the complexity with near transmission performance of VP, several recent studies investigated various efficient NLP schemes based on the concept of Tomlinson-Harashima precoding (THP) that applies successive pre-cancellation of inter-user interference (IUI) and offsets the transmission vector based on a modulo operation. In order to attain transmission performance improvement over the original THP, a previous work proposed Minimum Mean Square Error based THP (MMSE-THP) employing IUI successive pre-cancellation based on MMSE criteria. On the other hand, to improve the transmission performance of MMSE-THP, other previous works proposed Ordered MMSE-THP and Lattice-Reduction-Aided MMSE-THP (LRA MMSE-THP). This paper investigates the further transmission performance improvement of Ordered MMSE-THP and LRA MMSE-THP. This paper starts by proposing an extension of MMSE-THP employing a perturbation vector search (PVS), called PVS MMSE-THP as a novel NLP scheme, where the modulo operation is substituted by PVS and a subtraction operation from the transmit signal vector. Then, it introduces an efficient search algorithm of appropriate perturbation vector based on a depth-first branch-and-bound search for PVS MMSE-THP. Next, it also evaluates the transmission performance of PVS MMSE-THP with the appropriate perturbation vector detected by the efficient search algorithm. Computer simulations quantitatively clarify that PVS MMSE-THP achieves better transmission performance than the conventional NLP schemes. Moreover, it also clarifies that PVS MMSE-THP increases the effect of required transmission power reduction with the number of transmit antennas compared to the conventional NLP schemes.

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.

A ripple-free dual-rate control system is designed for a single-input single-output dual-rate system, in which the sampling interval of a plant output is longer than the holding interval of a control input. The dual-rate system is converged to a multi-input single-output single-rate system using the lifting technique, and a control system is designed based on an error system using the steady-state variable. Because the proposed control law is designed so that the control input is constant in the steady state, the intersample output as well as the sampled output converges to the set-point without both steady-state error and intersample ripples when there is neither modeling nor disturbance. Furthermore, in the proposed method, a two-degree-of-freedom integral compensation is designed, and hence, the transient response is not deteriorated by the integral action because the integral action is canceled when there is neither modeling nor disturbance. Moreover, in the presence of the modeling error or disturbance, the integral compensation is revealed, and hence, the steady-state error is eliminated on both the intersample and sampled response.

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.

This paper proposes a painterly morphing algorithm for mobile smart devices, where each frame in the morphing sequence looks like an oil-painted picture with brush strokes. It can be presented, for example, during the transition between the main screen and a specific application screen. For this, a novel dissimilarity function and acceleration data structures are developed. The experimental results show that the algorithm produces visually stunning effects at an interactive time.

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.

In mobile phone systems, the 4th generation is widely prevailing in 2017, and in 2020, it is expected that the 5th generation (5G) will start to prevail. In both generations, a linear power amplifier (PA) is used. In case of 4G, in addition, such as the envelope tracking (ET) and the digital predistortion (DPD) systems are applied to improve efficiency and linearity. In case of 5G, because of wider modulation band width and parallel operation under the multiple-input and multiple output (MIMO) mode, it might be difficult to apply all systems as those of 4G. Therefore linear PA for 5G will require higher performance with standalone operation. The linear amplifier, in spite of its name, operates non-linearly. In this paper, the non-linear operations of the linear amplifier and their effects on the linearity characteristics are reviewed. After that, impacts of non-linear elements of a hetero junction bipolar transistor (HBT), by analyzing single stage amplifier, are stated. In addition, major PA architectures including ET and DPD systems are reviewed.

Protein-Protein Interaction Extraction (PPIE) from biomedical literatures is an important task in biomedical text mining and has achieved great success on public datasets. However, in real-world applications, the existing PPI extraction methods are limited to label effort. Therefore, transfer learning method is applied to reduce the cost of manual labeling. Current transfer learning methods suffer from negative transfer and lower performance. To tackle this problem, an improved TrAdaBoost algorithm is proposed, that is, relative distribution is introduced to initialize the weights of TrAdaBoost to overcome the negative transfer caused by domain differences. To make further improvement on the performance of transfer learning, an approach combining active learning with the improved TrAdaBoost is presented. The experimental results on publicly available PPI corpora show that our method outperforms TrAdaBoost and SVM when the labeled data is insufficient,and on document classification corpora, it also illustrates that the proposed approaches can achieve better performance than TrAdaBoost and TPTSVM in final, which verifies the effectiveness of our methods.

For a pre-trained deep convolutional neural network (CNN) for an embedded system, a high-speed and a low power consumption are required. In the former of the CNN, it consists of convolutional layers, while in the latter, it consists of fully connection layers. In the convolutional layer, the multiply accumulation operation is a bottleneck, while the fully connection layer, the memory access is a bottleneck. The binarized CNN has been proposed to realize many multiply accumulation circuit on the FPGA, thus, the convolutional layer can be done with a high-seed operation. However, even if we apply the binarization to the fully connection layer, the amount of memory was still a bottleneck. In this paper, we propose a neuron pruning technique which eliminates almost part of the weight memory, and we apply it to the fully connection layer on the binarized CNN. In that case, since the weight memory is realized by an on-chip memory on the FPGA, it achieves a high-speed memory access. To further reduce the memory size, we apply the retraining the CNN after neuron pruning. In this paper, we propose a sequential-input parallel-output fully connection layer circuit for the binarized fully connection layer, while proposing a streaming circuit for the binarized 2D convolutional layer. The experimental results showed that, by the neuron pruning, as for the fully connected layer on the VGG-11 CNN, the number of neurons was reduced by 39.8% with keeping the 99% baseline accuracy. We implemented the neuron pruning CNN on the Xilinx Inc. Zynq Zedboard. Compared with the ARM Cortex-A57, it was 1773.0 times faster, it dissipated 3.1 times lower power, and its performance per power efficiency was 5781.3 times better. Also, compared with the Maxwell GPU, it was 11.1 times faster, it dissipated 7.7 times lower power, and its performance per power efficiency was 84.1 times better. Thus, the binarized CNN on the FPGA is suitable for the embedded system.

The main idea of filter methods in feature selection is constructing a feature-assessing criterion and searching for feature subset that optimizes the criterion. The primary principle of designing such criterion is to capture the relevance between feature subset and the class as precisely as possible. It would be difficult to compute the relevance directly due to the computation complexity when the size of feature subset grows. As a result, researchers adopt approximate strategies to measure relevance. Though these strategies worked well in some applications, they suffer from three problems: parameter determination problem, the neglect of feature interaction information and overestimation of some features. We propose a new feature selection algorithm that could compute mutual information between feature subset and the class directly without deteriorating computation complexity based on the computation of partitions. In light of the specific properties of mutual information and partitions, we propose a pruning rule and a stopping criterion to accelerate the searching speed. To evaluate the effectiveness of the proposed algorithm, we compare our algorithm to the other five algorithms in terms of the number of selected features and the classification accuracies on three classifiers. The results on the six synthetic datasets show that our algorithm performs well in capturing interaction information. The results on the thirteen real world datasets show that our algorithm selects less yet better feature subset.

A new Waffle-iron Ridge Guide (WRG) structure that has the ability to control both wavelength and impedance is proposed. With the proposed structure, not only can the wavelength be controlled over a wide range for both fast- and slow-waves in free space but the impedance can also be controlled. These features can improve the performance of array antennas in terms of reducing grating lobes and side lobes. In this paper, we discuss and evaluate a design scheme using equivalent circuits and EM-simulation. This paper also discusses how the conductivity and dielectric loss in the WRG affect the total gain of the array antenna.

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.