In general, Transmission Control Protocol (TCP), e.g., TCP NewReno, considers all losses to be a sign of congestion. It decreases the sending rate whenever a loss is detected. Integrating the network coding (NC) into protocol stack and making it cooperate with TCP (TCP/NC) would provide the benefit of masking packet losses in lossy networks, e.g., wireless networks. TCP/NC complements the packet loss recovery capability without retransmission at a sink by sending the redundant combination packets which are encoded at the source. However, TCP/NC is less effective under heavy and bursty loss which often occurs in fast fading channel because the retransmission mechanism of the TCP/NC entirely relies on the TCP layer. Our solution is TCP/NC with enhanced retransmission (TCP/NCwER), for which a new retransmission mechanism is developed to retransmit more than one lost packet quickly and efficiently, to allow encoding the retransmitted packets for reducing the repeated losses, and to handle the dependent combination packets for avoiding the decoding failure. We implement and test our proposal in Network Simulator 3. The results show that TCP/NCwER overcomes the deficiencies of the original TCP/NC and improves the TCP goodput under both random loss and burst loss channels.

In this paper, we propose an efficient clutter suppression algorithm for automotive radar systems in iron-tunnel environments. In general, the clutters in iron tunnels makes it highly likely that automotive radar systems will fail to detect targets. In order to overcome this drawback, we first analyze the cepstral characteristic of the iron tunnel clutter to determine the periodic properties of the clutters in the frequency domain. Based on this observation, we suggest for removing the periodic components induced by the clutters in iron tunnels in the cepstral domain by using the cepstrum editing process. To verify the clutter suppression of the proposed method experimentally, we performed measurements by using 77GHz frequency modulated continuous waveform radar sensors for an adaptive cruise control (ACC) system. Experimental results show that the proposed method is effective to suppress the clutters in iron-tunnel environments in the sense that it improves the early target detection performance for ACC significantly.

Network coding (NC) is considered a new paradigm for distributed networks. However, NC has an all-or-nothing property. In this paper, we propose a sparse recovery approach using sparse sensing matrix to solve the NC all-or-nothing problem over a finite field. The effectiveness of the proposed approach is evaluated based on a sensor network.

Much attention has recently been paid to sparsity-aware space-time adaptive processing (STAP) algorithms. The idea of sparsity-aware technology is commonly based on the convex l1-norm penalty. However, some works investigate the lq (0 < q < 1) penalty which induces more sparsity owing to its lack of convexity. We herein consider the design of an lq penalized STAP processor with a generalized sidelobe canceler (GSC) architecture. The lq cyclic descent (CD) algorithm is utilized with the least squares (LS) design criterion. It is validated through simulations that the lq penalized STAP processor outperforms the existing l1-based counterparts in both convergence speed and steady-state performance.

Carrier transport characteristics of TIPS-pentacene single crystalline film were controlled by changing the deposition condition of the blade-coating method. Anisotropic carrier transport in the single crystalline grain was visualized by means of the time-resolved microscopic optical second harmonic generation (TRM-SHG) measurement. Slow deposition yields the film with high mobility and large transport anisotropy. For molecular crystals, intermolecular interaction can be modified easily by changing the process condition.

In this paper, we propose a method to find similar sentences based on language resources for building a parallel corpus between English and Korean from Wikipedia. We use a Wiki-dictionary consisted of document titles from the Wikipedia and bilingual example sentence pairs from Web dictionary instead of traditional machine readable dictionary. In this way, we perform similarity calculation between sentences using sequential matching of the language resources, and evaluate the extracted parallel sentences. In the experiments, the proposed parallel sentences extraction method finally shows 65.4% of F1-score.

In this paper, we present an FPGA hardware implementation for a phylogenetic tree reconstruction with a maximum parsimony algorithm. We base our approach on a particular stochastic local search algorithm that uses the Progressive Neighborhood and the Indirect Calculation of Tree Lengths method. This method is widely used for the acceleration of the phylogenetic tree reconstruction algorithm in software. In our implementation, we define a tree structure and accelerate the search by parallel and pipeline processing. We show results for eight real-world biological datasets. We compare execution times against our previous hardware approach, and TNT, the fastest available parsimony program, which is also accelerated by the Indirect Calculation of Tree Lengths method. Acceleration rates between 34 to 45 per rearrangement, and 2 to 6 for the whole search, are obtained against our previous hardware approach. Acceleration rates between 2 to 36 per rearrangement, and 18 to 112 for the whole search, are obtained against TNT.

Environment perception is an important task for intelligent vehicles applications. Typically, multiple sensors with different characteristics are employed to perceive the environment. To robustly perceive the environment, the information from the different sensors are often integrated or fused. In this article, we propose to perform the sensor fusion and registration of the LIDAR and stereo camera using the particle swarm optimization algorithm, without the aid of any external calibration objects. The proposed algorithm automatically calibrates the sensors and registers the LIDAR range image with the stereo depth image. The registered LIDAR range image functions as the disparity map for the stereo disparity estimation and results in an effective sensor fusion mechanism. Additionally, we perform the image denoising using the modified non-local means filter on the input image during the stereo disparity estimation to improve the robustness, especially at night time. To evaluate our proposed algorithm, the calibration and registration algorithm is compared with baseline algorithms on multiple datasets acquired with varying illuminations. Compared to the baseline algorithms, we show that our proposed algorithm demonstrates better accuracy. We also demonstrate that integrating the LIDAR range image within the stereo's disparity estimation results in an improved disparity map with significant reduction in the computational complexity.

Purchase behavior prediction is one of the most important issues to promote both e-commerce companies' sales and the consumers' satisfaction. The prediction usually uses features based on the statistics of items. This kind of features can lead to the loss of detailed information of items. While all items are included, a large number of features has the negative impact on the efficiency of learning the predictive model. In this study, we propose to use the most popular items for improving the prediction. Experiments on the real-world dataset have demonstrated the effectiveness and the efficiency of our proposed method. We also analyze the reason for the performance of the most popular items. In addition, our work also reveals if interactions among most popular items are taken into account, the further significant improvement can be achieved. One possible explanation is that online retailers usually use a variety of sales promotion methods and the interactions can help to predict the purchase behavior.

This paper presents human-centered video feature selection via mRMR-SCMMCCA (minimum Redundancy and Maximum Relevance-Specific Correlation Maximization Multiset Canonical Correlation Analysis) algorithm for preference extraction. The proposed method derives SCMMCCA, which simultaneously maximizes two kinds of correlations, correlation between video features and users' viewing behavior features and correlation between video features and their corresponding rating scores. By monitoring the derived correlations, the selection of the optimal video features that represent users' individual preference becomes feasible.

This paper presents a method to accelerate target recognition processing in advanced driver assistance systems (ADAS). A histogram of oriented gradients (HOG) is an effective descriptor for object recognition in computer vision and image processing. The HOG is expected to replace conventional descriptors, e.g., template-matching, in ADAS. However, the HOG does not consider the occurrences of gradient orientation on objects when localized portions of an image, i.e., a region of interest (ROI), are not set precisely. The size and position of the ROI should be set precisely for each frame in an automotive environment where the target distance changes dynamically. We use radar to determine the size and position of the ROI in a HOG and propose a radar and camera sensor fusion algorithm. Experimental results are discussed.

The multiple assignment scheme is to assign one or more shares to single participant so that any kind of access structure can be realized by classical secret sharing schemes. We propose its quantum version including ramp secret sharing schemes. Then we propose an integer optimization approach to minimize the average share size.

Based on the codewords of the [q,2,q-1] extended Reed-Solomon (RS) code over the finite field Fq, we can construct a regular binary γq×q2 matrix H(γ,q), where q is a power of 2 and γ≤q. The matrix H(γ,q) defines a regular low-density parity-check (LDPC) code C(γ,q), called a full-length RS-LDPC code. Using some analytical methods, we completely determine the values of s(H(4,q)), s(H(5,q)), and d(C(5,q)) in this letter, where s(H(γ,q)) and d(C(γ,q)) are the stopping distance of H(γ,q) and the minimum distance of C(γ,q), respectively.

A statistical call admission control (CAC) allows more calls with on-off patterns to be accepted and a higher channel efficiency to be achieved. In this paper, we propose three statistical CACs for VoIP calls with silence suppression considering the priority of each VoIP call, where the call priority is determined by the call acceptance order in an IP-PBX. We analyse the packet loss rates in an IP-PBX under the proposed strategies and express the end-to-end QoS of a VoIP call as an R-factor in a VoIP service network. The performances of the proposed CACs are evaluated using the maximum allowable number of VoIP calls while satisfying the end-to-end QoS constraint, the average QoS of acceptable VoIP calls and the channel efficiency. The advantage of the proposed statistical CACs over the non-statistical CAC is verified in terms of these three performance metrics. The results indicate that a trade-off is possible in that the maximum allowable number of VoIP calls in an IP-PBX increases as the average QoS of acceptable VoIP calls is lowered according to the proposed statistical CAC used. Nevertheless, the results allow us to verify that the channel efficiencies are the same for all the statistical CACs considered.

The mixer is a crucial circuit block in a WiMax system receiver. The performance of a mixer depends on three specifications: conversion gain, linearity and noise figure. Many mixers have been recently proposed for UWB and wideband systems; however, they either cannot achieve the high conversion gain required for a WiMAX system or they are prone to high power consumption. In this paper, a folded mixer with a high conversion gain is designed for a 2-11GHz WiMAX system and it can achieve a 20MHz IF output signal. From the simulation results, the proposed folded mixer achieves a conversion gain of 18.9 to 21.5dB for the full bandwidth. With a 0.2 to 4.4dBm IIP3, the NF is 13.5 to 17.6dB. The folded mixer is designed using TSMC 0.18µm CMOS technology. The core power consumption of the mixer is 11.8mW.

This paper reports dual-polarization In-phase and Quadrature (DP-IQ) modulators and photodetectors integrated with the 90° hybrid using InP-based monolithic integration technologies for 100/200Gb/s coherent transmission. The DP-IQ modulator was monolithically integrated with the Mach-Zehnder modulator array consisting of deep-ridge waveguides formed through dry etching and benzocyclobutene planarization processes. This DP-IQ modulator exhibited the low half-wavelength voltage (Vπ=1.5V) and the wide 3-dB bandwidth (f3dB > 28GHz). The photodetector monolithically integrated with the 90° hybrid consisting of multimode interference structures was realized by the butt-joint regrowth. A responsivity including total loss of 7.9dB in the waveguide was as high as 0.155A/W at a wavelength of 1550nm, and responsivity imbalance of the In-phase and Quadrature channels was less than ±0.5dB over the C-band. In addition, the low dark current (less than 500pA up to 85°C @ -3.0V) and the stable operation in the accelerated aging test (test condition: -5V at 175°C) over 5,000h were successfully achieved for the p-i-n-photodiode array with a buried heterostructure formed through the selective embedding regrowth. Finally, a receiver responsivity including intrinsic loss of 3dB in the polarization beam splitter was higher than 0.070A/W at a wavelength of 1550nm through the integration of the spot-size converter, and demodulation of 128Gb/s DP-QPSK and 224Gb/s DP-16QAM modulated signals was demonstrated for the compact coherent receiver using this photodetector integrated with the 90° hybrid. Therefore, we indicated that these InP-based monolithically integrated photonic devices are very useful for 100/200Gb/s pluggable coherent transceivers.

Advances in intelligent vehicle systems have led to modern automobiles being able to aid drivers with tasks such as lane following and automatic braking. Such automated driving tasks increasingly require reliable ego-localization. Although there is a large number of sensors that can be employed for this purpose, the use of a single camera still remains one of the most appealing, but also one of the most challenging. GPS localization in urban environments may not be reliable enough for automated driving systems, and various combinations of range sensors and inertial navigation systems are often too complex and expensive for a consumer setup. Therefore accurate localization with a single camera is a desirable goal. In this paper we propose a method for vehicle localization using images captured from a single vehicle-mounted camera and a pre-constructed database. Image feature points are extracted, but the calculation of camera poses is not required — instead we make use of the feature points' scale. For image feature-based localization methods, matching of many features against candidate database images is time consuming, and database sizes can become large. Therefore, here we propose a method that constructs a database with pre-matched features of known good scale stability. This limits the number of unused and incorrectly matched features, and allows recording of the database scales into “tracklets”. These “Feature scale tracklets” are used for fast image match voting based on scale comparison with corresponding query image features. This process reduces the number of image-to-image matching iterations that need to be performed while improving the localization stability. We also present an analysis of the system performance using a dataset with high accuracy ground truth. We demonstrate robust vehicle positioning even in challenging lane change and real traffic situations.

A 12-bit 1.25MS/s cyclic analog-to-digital converter (ADC) is designed and fabricated in 90nm CMOS technology, and only occupies an active area as small as 0.037mm2. The proposed ADC is composed of a non-binary AD convertion stage, and a on-chip non-binary-to-binary digital block includes a built-in radix-value self-estimation scheme. Therefore, althouh a non-binary convertion architechture is adopted, the proposed ADC is the same as other stand-alone binary ADCs. The redundancy of non-binary 1-bit/step architecture relaxes the accuracy requirement on analog components of ADC. As a result, the implementation of analog circuits such as amplifier and comparator becomes simple, and high-density Metal-Oxide-Metal (MOM) capacitors can be used to achieve a small chip area. Furthermore, the novel radix-value self-estimation technique can be realized by only simple logic circuits without any extra analog input, so that the total active area of ADC is dramatically reduced. The prototype ADC achieves a measured peak signal-to-noise-and-distortion-ratio (SNDR) of 62.3dB using a poor DC gain amplifier as low as 45dB and MOM capacitors without any careful layout techniques to improve the capacitor matching. The proposed ADC dissipated 490µW in analog circuits at 1.4V power supply and 1.25Msps (20MHz clocking). The measured DNL is +0.94/-0.71LSB and INL is +1.9/-1.2LSB at 30kHz sinusoidal input.

Similarity search is an important and fundamental problem, and thus widely used in various fields of computer science including multimedia, computer vision, database, information retrieval, etc. Recently, since loitering behavior often leads to abnormal situations, such as pickpocketing and terrorist attacks, its analysis attracts increasing attention from research communities. In this paper, we present AntiLoiter, a loitering discovery system adopting efficient similarity search on surveillance videos. As we know, most of existing systems for loitering analysis, mainly focus on how to detect or identify loiterers by behavior tracking techniques. However, the difficulties of tracking-based methods are known as that their analysis results are heavily influenced by occlusions, overlaps, and shadows. Moreover, tracking-based methods need to track the human appearance continuously. Therefore, existing methods are not readily applied to real-world surveillance cameras due to the appearance discontinuity of criminal loiterers. To solve this problem, we abandon the tracking method, instead, propose AntiLoiter to efficiently discover loiterers based on their frequent appearance patterns in longtime multiple surveillance videos. In AntiLoiter, we propose a novel data structure Luigi that indexes data using only similarity value returned by a corresponding function (e.g., face matching). Luigi is adopted to perform efficient similarity search to realize loitering discovery. We conducted extensive experiments on both synthetic and real surveillance videos to evaluate the efficiency and efficacy of our approach. The experimental results show that our system can find out loitering candidates correctly and outperforms existing method by 100 times in terms of runtime.

Crystalline GaN films can be grown even on amorphous substrates with the use of graphene buffer layers by pulsed sputtering deposition (PSD). The graphene buffer layers allowed us to grow highly c-axis-oriented GaN films at low substrate temperatures. Full-color GaN-based LEDs can be fabricated on the GaN/graphene structures and they are operated successfully. This indicates that the present technique is promising for future large-area light-emitting displays on amorphous substrates.