Browsing by Author "Jin, Xin"

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A 2-GS/s 8-Bit ADC Featuring Virtual-Ground Sampling Interleaved Architecture in 28-nm CMOS
(2018-11-01)
Wang, X. Shawn
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Jin, Xin
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Du, Jieqiong
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Li, Yilei
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Du, Yuan
This brief presents a two-way time-interleaved twostep pipelined analog-to-digital converter (ADC) architecture built upon a new concept of virtual-ground sampling, featuring merged front-end track-and-hold, residue generation, input termination, and buffering. This architecture is investigated to alleviate the front-end performance tradeoff among the total-harmonic- distortion, bandwidth, and sampling rate (interleaving factor). A 2-GS/s 8b ADC using the new architecture was designed and fabricated in 28-nm CMOS, achieving 43-dB SNDR and 55-dB SFDR up to Nyquist frequency.
A new similarity/distance measure between intuitionistic fuzzy sets based on the transformed isosceles triangles and its applications to pattern recognition
(2019-02-01)
Jiang, Qian
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Jin, Xin
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Lee, Shin-Jye
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Yao, Shaowen
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科技管理研究所
Intuitionistic fuzzy set (IFS) is a representative model of fuzzy theory, and it is widely used to deal with fuzzy and uncertain problems in many practical applications. In IFS theory, the calculation of the distance and similarity between IFSs are significant techniques that are effective measurement methods for distinguishing the similarity degree between IFSs, and the two measures can be used in pattern recognition, decision-making and so on. In this paper, a novel similarity/distance measure between IFSs is proposed according to the intersections of the transformed isosceles triangles from IFSs, and the isosceles triangles are placed in a square area; furthermore, the properties of the proposed measure are also analyzed to prove whether the measure satisfies the definition of the similarity/distance measure for IFSs or not. The numerical experiments are implemented to test the validity of the proposed measure; in addition, several pattern recognition and clustering problems are also employed to further demonstrate its effectiveness. The experimental results show that the proposed measure is an accurate and superior measure that can avoid the shortcomings of most existing measures. Finally, the directions for future research of this work are also represented in the conclusion. (C) 2018 Elsevier Ltd. All rights reserved.
Downlink Scheduling in Network MIMO Using Two-Stage Channel State Feedback
(2012)
Tseng, Li-Chuan
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Jin, Xin
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Marzouki, Abdelwaheb
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Huang, ChingYao
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交大名義發表
In this paper, we propose a user scheduling scheme which is based merely on the ergodic rate. We derive the ergodic rate of each user for our network MIMO system from the average Signal-to-Noise Ratio (SNR) and the covariance of prediction error. Further more, we design a joint precoding for the selected users. The precoding matrices are contrived according to the predicted Channel State Information at Transmitter (CSIT) in the future time slot to reduce the deviation caused by delay. Such a network MIMO system is under consideration, where the users are cooperatively served by multiple Base Stations (BSs). User scheduling and joint precoding are executed by a central unit connected to all BSs. Both the above functions require the CSIT which is acquired by an uplink feedback overhead. Simulation results demonstrate a conspicuous improvement in user spectral efficiency with reduced feedback.
Multi-Sensor Image Fusion Based on Interval Type-2 Fuzzy Sets and Regional Features in Nonsubsampled Shearlet Transform Domain
(2018-03-15)
Jiang, Qian
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Jin, Xin
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Hou, Jingyu
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Lee, Shin-Jye
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Yao, Shaowen
Multi-scale geometric analysis, one of the most often-used multi-sensor image fusion (MSIF) techniques, can offer outstanding performance during extracting the features of source image. Interval type-2 fuzzy sets (Type-2 FS) have a good prospect in image fusion field, because it can effectively address the uncertain and fuzzy problem in image fusion for selecting the high-quality pixels or coefficients of source images. We try to extend the application fields of Type-2 FS and improve the performance of MSIF; therefore, this paper presents a hybrid method by combining the local spatial frequency (LSF) with interval Type-2 FS in nonsubsampled shearlet transform (NSST) domain. NSST is used to decompose source images, and interval Type-2 FS and LSF is employed to extract the regional features of source images; so it can extract and fuse the detailed features of different source images accurately. First, NSST is performed to decompose the source images into low frequency and high frequency sub-images. Second, LSF-based fusion rule is applied to fuse low frequency sub-images. Thirdly, a novel fusion process based on interval Type-2 FS is designed to fuse high frequency sub-images. At last, inverse NSST2 (INSST) is implemented to reconstruct the fused images. The experimental and contrastive results of different image sets show that the proposed method is an effective MSIF scheme, which can achieve better fusion effect than the existing representative methods.
Remote sensing image colorization using symmetrical multi-scale DCGAN in YUV color space
(1970-01-01)
Wu, Min
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Jin, Xin
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Jiang, Qian
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Lee, Shin-jye
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Liang, Wentao
Image colorization technique is used to colorize the gray-level image or single-channel image, which is a very significant and challenging task in image processing, especially the colorization of remote sensing images. This paper proposes a new method for coloring remote sensing images based on deep convolution generation adversarial network. The adopted generator model is a symmetrical structure using the principle of auto-encoder, and a multi-scale convolutional module is specially designed to introduce into the generator model. Thus, the proposed generator can enable the whole model to retain more image features in the process of up-sampling and down-sampling. Meanwhile, the discriminator uses residual neural network 18 that can compete with the generator, so that the generator and discriminator can effectively optimize each other. In the proposed method, the color space transformation technique is first utilized to convert remote sensing images from RGB to YUV. Then, the Y channel (a gray-level image) is used as the input of the neural network model to predict UV channels. Finally, the predicted UV channels are concatenated with the original Y channel as a whole YUV that is then transformed into RGB space to get the final color image. Experiments are conducted to test the performance of different image colorization methods, and the results show that the proposed method has good performance in both visual quality and objective indexes on the colorization of remote sensing image.