60GHz wireless communication requires analog baseband circuits having a bandwidth of about 1GHz. This paper presents a wide bandwidth current-mode low pass filter technique which involves current amplifiers, resistors and capacitors. The proposed current-mode filter is obtained by replacing an integrator employing an op-amp with another integrator employing a current amplifier. With the low input impedance current amplifier having little variation of the input impedance, the proposed filter is expected to improve linearity and power efficiency. The proposed current amplifier which employs super source follower topology with complementary input is suitable for the filter because of its class AB operation. Although simulation results shows the conventional current amplifier which employs super source follower topology without the complementary input has 12Ω variation and 30Ω input impedance, the proposed current amplifier has 1Ω variation and 21Ω input impedance. A fourth order 1GHz bandwidth filter which involves the proposed current amplifiers is designed in a 65nm CMOS technology. The filter can achieve IIP3 of 1.3dBV and noise of 0.6mVrms with power consumption of 13mW under supply voltage of 1.2V according to simulation results with layout parasitic extraction models. Active area of the filter is 380μm×170μm.

We investigate a finite-sized self-complementary bow-tie antenna (SC-BTA) integrated with a semiconductor mesa with respect to radiation characteristics such as the peak radiation frequency and bandwidth around the fundamental radiation mode. For this investigation, we utilize an equivalent circuit model of the SC-BTA derived in our previous work and a finite element method solver. Moreover, we derive design guidelines for the radiation characteristics in the form of size scaling-rules with respect to the antenna outer size for a terahertz transmitter.

This paper proposes a formal approach of verifying ubiquitous computing application scenarios. Ubiquitous computing application scenarios assume that there are a lot of devices and physical things with computation and communication capabilities, which are called smart objects, and these are interacted with each other. Each of these interactions among smart objects is called “federation”, and these federations form a ubiquitous computing application scenario. Previously, Yuzuru Tanaka proposed “a proximity-based federation model among smart objects”, which is intended for liberating ubiquitous computing from stereotyped application scenarios. However, there are still challenges to establish the verification method of this model. This paper proposes a verification method of this model through model checking. Model checking is one of the most popular formal verification approach and it is often used in various fields of industry. Model checking is conducted using a Kripke structure which is a formal state transition model. We introduce a context catalytic reaction network (CCRN) to handle this federation model as a formal state transition model. We also give an algorithm to transform a CCRN into a Kripke structure and we conduct a case study of ubiquitous computing scenario verification, using this algorithm and the model checking. Finally, we discuss the advantages of our formal approach by showing the difficulties of our target problem experimentally.

The 1-bit band-pass delta-sigma modulator (BP-DSM) achieves high resolution by using the oversampling technique. This method allows direct RF signal transmission from a digitally modulated signal, using a 1-bit digital pulse train. However, it has been previously reported that the adjacent channel leakage ratio (ACLR) in a target frequency band degrades due to the pulse transition mismatch between rising and falling waveforms in the time domain. This paper clarifies that the spurious distortion in BP-DSM is caused by the asymmetricity of the waveform about the center of an eye pattern in the time axis, and proposes a 1-bit BP-DSM with the compensator consisting of a fractional delay filter and a binary data differentiator to cancel out the asymmetry in the target frequency band. This can accurately provide a wideband cancellation signal with more than 100MHz bandwidth, including the adjacent channel, within 50dB power dynamic range. Using long term evolution (LTE) signals with 5MHz bandwidth at 0.8GHz, we simulated the spurious distortion, performing various combinations of rising and falling times in the eye pattern, and the proposed 1-bit BP-DSM always achieved high ACLR, up to 60dB, in 140MHz bandwidth, under all conditions.

Linked Open Data (LOD) at Schema-Level and knowledge described in Chinese is an important part of the LOD project. Previous work generally ignored the rules of word-order sensitivity and polysemy in Chinese or could not deal with the out-of-vocabulary (OOV) mapping task. There is still no efficient system for large-scale Chinese ontology mapping. In order to solve the problem, this study proposes a novel TongYiCiCiLin (TYCCL) and Sequence Alignment-based Chinese Ontology Mapping model, which is called TongSACOM, to evaluate Chinese concept similarity in LOD environment. Firstly, an improved TYCCL-based similarity algorithm is proposed to compute the similarity between atomic Chinese concepts that have been included in TYCCL. Secondly, a global sequence-alignment and improved TYCCL-based combined algorithm is proposed to evaluate the similarity between Chinese OOV. Finally, comparing the TongSACOM to other typical similarity computing algorithms, and the results prove that it has higher overall performance and usability. This study may have important practical significance for promoting Chinese knowledge sharing, reusing, interoperation and it can be widely applied in the related area of Chinese information processing.

In this paper, we propose an algorithm that enhances the number of pixels for high-speed imaging. High-speed cameras have a principle problem that the number of pixels reduces when the number of frames per second (fps) increases. To enhance the number of pixels, we suppose an optical structure that block-randomly selects some percent of pixels in an image. Then, we need to reconstruct the entire image. For this, a state-of-the-art method takes three-dimensional reconstruction strategy, which requires a heavy computational cost in terms of time. To reduce the cost, the proposed method reconstructs the entire image frame-by-frame using a new cost function exploiting two types of sparsity. One is within each frame and the other is induced from the similarity between adjacent frames. The latter further means not only in the image domain, but also in a sparsifying transformed domain. Since the cost function we define is convex, we can find the optimal solution using a convex optimization technique with small computational cost. We conducted simulations using grayscale image sequences. The results show that the proposed method produces a sequence, mostly the same quality as the state-of-the-art method, with dramatically less computational time.

In this paper, we investigate a novel preamble channel estimation (CE) method based on the compressed sensing (CS) theory in the orthogonal frequency division multiplexing system with offset quadrature amplitude modulation (OQAM/OFDM) over a frequency selective fading channel. Most of the preamble based CE methods waste power by deploying the pilots in all the subcarriers. Inspired by the CS theory, we focus on using many fewer pilots than one of traditional CE methods and realize accurate reconstruction of the channel response. After describing and analyzing the concept of OQAM/OFDM and its traditional CE methods, we propose a novel channel estimation method based on CS that requires fewer pilots in the preamble, and we design the corresponding preamble pattern to meet the requirements of CS. Simulation results validate the efficiency and superior performance of the proposed method in wireless channel.

Existing noise inference algorithms neglected the smooth characteristics of noise data, which results in executing slowly of noise inference. In order to address this problem, we present a noise inference algorithm based on fast context-aware tensor decomposition (F-CATD). F-CATD improves the noise inference algorithm based on context-aware tensor decomposition algorithm. It combines the smoothness constraint with context-aware tensor decomposition to speed up the process of decomposition. Experiments with New York City 311 noise data show that the proposed method accelerates the noise inference. Compared with the existing method, F-CATD reduces 4-5 times in terms of time consumption while keeping the effectiveness of the results.

Attaining extremely low latency service in 5G cellular networks is an important challenge in the communication research field. A higher QoS in the next-generation network could enable several unprecedented services, such as Tactile Internet, Augmented Reality, and Virtual Reality. However, these services will all need support from powerful computational resources provided through cloud computing. Unfortunately, the geolocation of cloud data centers could be insufficient to satisfy the latency aimed for in 5G networks. The physical distance between servers and users will sometimes be too great to enable quick reaction within the service time boundary. The problem of long latency resulting from long communication distances can be solved by Mobile Edge Computing (MEC), though, which places many servers along the edges of networks. MEC can provide shorter communication latency, but total latency consists of both the transmission and the processing times. Always selecting the closest edge server will lead to a longer computing latency in many cases, especially when there is a mass of users around particular edge servers. Therefore, the research studies the effects of both latencies. The communication latency is represented by hop count, and the computation latency is modeled by processor sharing (PS). An optimization model and selection policies are also proposed. Quantitative evaluations using simulations show that selecting a server according to the lowest total latency leads to the best performance, and permitting an over-latency barrier would further improve results.

Variance analysis is an important research topic to assess the quality of estimators. In this paper, we analyze the performance of the least ℓp-norm estimator in the presence of mixture of generalized Gaussian (MGG) noise. In the case of known density parameters, the variance expression of the ℓp-norm minimizer is first derived, for the general complex-valued signal model. Since the formula is a function of p, the optimal value of p corresponding to the minimum variance is then investigated. Simulation results show the correctness of our study and the near-optimality of the ℓp-norm minimizer compared with Cramér-Rao lower bound.

In this letter, we propose a Hadoop-based Video Transcoding System (HVTS), which is designed to run on all major cloud computing services. HVTS is highly adapted to the structure and policies of Hadoop, thus it has additional capacities for transcoding, task distribution, load balancing, and content replication and distribution. To evaluate, our proposed system, we carry out two performance tests on our local testbed, transcoding and robustness to data node and task failures. The results confirmed that our system delivers satisfactory performance in facilitating seamless streaming services in cloud computing environments.

An online nonnegative matrix factorization (NMF) algorithm based on recursive least squares (RLS) is described in a matrix form, and a simplified algorithm for a low-complexity calculation is developed for frame-by-frame online audio source separation system. First, the online NMF algorithm based on the RLS method is described as solving the NMF problem recursively. Next, a simplified algorithm is developed to approximate the RLS-based online NMF algorithm with low complexity. The proposed algorithm is evaluated in terms of audio source separation, and the results show that the performance of the proposed algorithms are superior to that of the conventional online NMF algorithm with significantly reduced complexity.

In this paper, the differences in navigation information design, which is important for kiosk-type pedestrian navigation systems, were experimentally examined depending on presence or absence of carriable navigation information in order to acquire the knowledge to contribute design guidelines of kiosk-type pedestrian navigation systems. In particular, we used route complexity information calculated using a regression equation that contained multiple factors. In the absence of carriable navigation information, both the destination arrival rate and route deviation rate improved. Easy routes were designed as M (17 to 39 characters in Japanese), while complicated routes were denoted as L (40 or more characters in Japanese). On the contrary, in the presence of carriable navigation information, the user's memory load was found to be reduced by carrying the same navigation information as kiosk-type terminals. Thus, the reconsideration of kiosk-type pedestrian navigation systems design, e.g., the means of presenting navigation information, is required. For example, if the system attaches importance to a high destination arrival rate, L_Carrying without regard to route complexity is better. If the system attaching importance to the low route deviation rate, M_Carrying in the case of easy routes and L_Carrying in the case of complicated routes have been better. Consequently, this paper presents the differences in the designs of pedestrian navigation systems depending on whether carriable navigation information is absent or present.

The transmission on fronthaul links in the cloud radio access network has become a bottleneck with the increasing data rate. In this paper, we propose a novel two-stage compression scheme for fronthaul links. In the first stage, the commonly used techniques like cyclic prefix stripping and sampling rate adaptation are implemented. In the second stage, a structure called linear prediction coding with decision threshold (LPC-DT) is proposed to remove the redundancies of signal. Considering that the linear prediction outputs have large dynamic range, a two-piecewise quantization with optimized decision threshold is applied to enhance the quantization performance. In order to further lower the transmission rate, a multi-level successive structure of lossless polar source coding is proposed to compress the quantization output with low encoding and decoding complexity. Simulation results demonstrate that the proposed scheme with LPC-DT and LPSC offers not only significantly better compression ratios but also more flexibility in bandwidth settings compared with traditional ones.

Sorting is an extremely important computation kernel that has been accelerated in a lot of fields such as databases, image processing, and genome analysis. Given that advent of Internet of Things (IoT) era due to mobile technology progressions, the future needs a sorting method that is available on any environment, such as not only high performance systems like servers but also low computational performance machines like embedded systems. In this paper, we present an FPGA-based sorting accelerator combining Sorting Network and Merge Sorter Tree, which is customizable by means of tuning design parameters. The proposed FPGA accelerator sorts data sent from a host PC via the PCIe bus, and sends back the fully sorted data sequence to it. We also present a detailed analytical model that accurately estimates the sorting performance. Due to these characteristics, designers can know how fast a developed sorting hardware is in advance and can implement the best one to fulfill the cost and performance constraints. Our experiments show that the proposed hardware achieves up to 19.5x sorting performance, compared with Intel Core i7-3770K operating at 3.50GHz, when sorting 256M 32-bits integer elements. However, this result is limited because of insufficient memory bandwidth. To overcome this problem, we propose a data compression mechanism and the experimental result shows that the sorting hardware with it achieves almost 90% of the estimated performance, while the hardware without it does about 60%. In order to allow every designer to easily and freely use this accelerator, the RTL source code is released as open-source hardware.

Fuzzy techniques can implement the fine-grained access control of encrypted data in the Cloud because they support error-tolerance. In this system, using biometric attributes such as fingerprints, faces and irises as pubic parameters is advantageous over those systems based on Public Key Infrastructure (PKI). This is because biometric information is unique, unforgettable and non-transferable. However the biometric-attribute measurements are noisy and most of the existing encryption systems can not support the biometric-attribute encryption. Additionally, the previous fuzzy encryption schemes only achieve the selective security which is a weak security model. To overcome these drawbacks, we propose a new fuzzy encryption scheme based on the lattice in this letter. The proposed scheme is based on a hierarchical identity-based encryption with fixed-dimensional private keys space and thus has short public parameters and short private keys, which results in high computation efficiency. Furthermore, it achieves the strong security, i.e., adaptive security. Lastly, the security is reduced to the learning with errors (LWE) problem in the standard model.

In this paper, we study robust Principal Component Analysis (PCA)-based anomaly detection techniques in network traffic, which can detect traffic anomalies by projecting measured traffic data onto a normal subspace and an anomalous subspace. In a PCA-based anomaly detection, outliers, anomalies with excessively large traffic volume, may contaminate the subspaces and degrade the performance of the detector. To solve this problem, robust PCA methods have been studied. In a robust PCA-based anomaly detection scheme, outliers can be removed from the measured traffic data before constructing the subspaces. Although the robust PCA methods are promising, they incure high computational cost to obtain the optimal location vector and scatter matrix for the subspace. We propose a novel anomaly detection scheme by extending the minimum covariance determinant (MCD) estimator, a robust PCA method. The proposed scheme utilizes the daily periodicity in traffic volume and attempts to detect anomalies for every period of measured traffic. In each period, before constructing the subspace, outliers are removed from the measured traffic data by using a location vector and a scatter matrix obtained in the preceding period. We validate the proposed scheme by applying it to measured traffic data in the Abiline network. Numerical results show that the proposed scheme provides robust anomaly detection with less computational cost.

Identifying IDC (Internet Data Center) IP addresses and analyzing the connection relationship of IDC could reflect the IDC network resource allocation and network layout which is helpful for IDC resource allocation optimization. Recent research mainly focuses on minimizing electricity consumption and optimizing network resource allocation based on IDC traffic behavior analysis. However, the lack of network-wide IP information from network operators has led to problems like management difficulties and unbalanced resource allocation of IDC, which are still unsolved today. In this paper, we propose a method for the IP identification and connection relationship analysis of IDC based on the flow connection behavior analysis. In our method, the frequent IP are extracted and aggregated in backbone communication network based on the traffic characteristics of IDC. After that, the connection graph of frequent IP (CGFIP) are built by analyzing the behavior of the users who visit the IDC servers, and IDC IP blocks are thus identified using CGFIP. Furthermore, the connection behavior characteristics of IDC are analyzed based on the connection graphs of IDC (CGIDC). Our findings show that the method can accurately identify the IDC IP addresses and is also capable of reflecting the relationships among IDCs effectively.

This paper presents the design and characterization of an E-band 16×16-slot monopulse array antenna with full-corporate-feed fabricated by the commercially available batch process of diffusion bonding of laminated copper plates. The antenna is multi-layered, and consists of vertically-interconnected radiating elements, a corporate-feed circuit and a comparator. It has four input ports for different excitations. Sum and difference beams in different cut-planes for monopulse operation can be generated. The antenna has a quasi-planar profile, and a total size of 13.31 λ0×13.31λ0×1.52λ0 (λ0 is the wavelength at the design frequency of 78.5GHz). The antenna demonstrates a wide operation bandwidth of 17.2 (70-87.2) GHz for VSWR < 2. At 78.5GHz: 1) for the sum beam, there is a 32.6-dBi realized gain (83% antenna efficiency) and a 33.3-dBi directivity (95% aperture efficiency); 2) for the difference beams in the E-, H-, 45°-, and 135°-planes, the null depths are -53.0, -58.0, -57.8, and -65.6dB, respectively. Across the full operation band where the sum main-beam and difference null are able to consistently point at the boresight, the antenna also demonstrates excellent performance in terms of high gain, high efficiency, high isolation, low cross-polarization, and distinguished monopulse capability.

A 1.9GHz film bulk acoustic resonator (FBAR)-based low-phase-noise complementary cross-coupled voltage-controlled oscillator (VCO) is presented. The FBAR-VCO is designed and fabricated in 0.18µm CMOS process. The DC latch and the low frequency instability are resolved by employing the NMOS source coupling capacitor and the DC blocked cross-coupled pairs. Since no additional voltage headroom is required, the proposed FBAR-VCO can be operated at a low power supply voltage of 1.1V with a wide voltage swing of 0.9V. An effective phase noise optimization is realized by a reasonable trade-off between the output resistance and the trans-conductance of the cross-coupled pairs. The measured performance shows the proposed FBAR-VCO achieves a phase noise of -148dBc/Hz at 1MHz offset with a figure of merit (FoM) of -211.6dB.