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认知中继三跳网络联合优化 Article

澄赵,万良王,信威姚,双华杨

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2, Pages 253-261 doi: 10.1631/FITEE.1601414

Abstract: 同时，引入了叠加编码技术来处理网络中双接收节点的情况。本文将这个三阶段的联合优化问题转化为一个非线性的优化问题，并进行了求解。

Keywords：解码转发；三跳；认知中继网络；时间功率分配；叠加编码

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A New Algorithm of Fractal Image Coding

Wang Xiuni,Jiang Wei,Wang Licun

Strategic Study of CAE 2006, Volume 8, Issue 1, Pages 54-57

Abstract:

Because it takes too much of time in fractal image coding, the paper analyses the factors that affect the speed of fractal image coding , and proposes a novel idea by using the reformed variance (tentatively) to improve image fractal compression performance . A theorem is proved that the IFS cannot change the image blocks' reformed variance. Moreover , it gives a novel fractal image compression method based on the reformed variance. The simulation results illuminate that the new method can run fast, at the same time it can improve the PSNR when compared with other fast algorithms.

Keywords： fractal coding image compression variance

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Fully Flexible Loads in Distributed Energy Management: PV, Batteries, Loads and Value Stacking in Virtual Power Plants

Andrew Mears, James Martin

Engineering 2020, Volume 6, Issue 7, Pages 736-738 doi: 10.1016/j.eng.2020.07.004

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A new anti-jamming communication technical system：pre-encoded code hopping spread spectrum

Yao Fuqiang,Zhang Yi

Strategic Study of CAE 2011, Volume 13, Issue 10, Pages 69-75

Abstract:

A kind of pre-encoded code hopping spread spectrum (PCHSS) communication technical system for anti-jamming communication is brought forward and researched after analyzing its necessity based on the deficiency of conventional direct-sequence spread spectrum(DSSS). The main discussions include the basic principle of PCHSS, the differences between PCHSS and self-coded spread spectrum(SCHSS), and some key techniques. The PCHSS basic performance is analyzed finally. The technical system and its basic performance have been proved in practice.

Keywords： anti-jamming communication direct-sequence spread spectrum pre-encoded code hopping spread spectrum self-coded spread spectrum

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Vector quantization: a review Regular Papers

Ze-bin WU, Jun-qing YU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 4, Pages 507-524 doi: 10.1631/FITEE.1700833

Abstract:

Vector quantization (VQ) is a very effective way to save bandwidth and storage for speech coding and image coding. Traditional vector quantization methods can be divided into mainly seven types, tree-structured VQ, direct sum VQ, Cartesian product VQ, lattice VQ, classified VQ, feedback VQ, and fuzzy VQ, according to their codebook generation procedures. Over the past decade, quantization-based approximate nearest neighbor (ANN) search has been developing very fast and many methods have emerged for searching images with binary codes in the memory for large-scale datasets. Their most impressive characteristics are the use of multiple codebooks. This leads to the appearance of two kinds of codebook: the linear combination codebook and the joint codebook. This may be a trend for the future. However, these methods are just finding a balance among speed, accuracy, and memory consumption for ANN search, and sometimes one of these three suffers. So, finding a vector quantization method that can strike a balance between speed and accuracy and consume moderately sized memory, is still a problem requiring study.

Keywords： Approximate nearest neighbor search Image coding Vector quantization

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The study on general design framework and message encoding ofweather hazards warning information broadcasting system

Zhang Di,Li Zechun,Shi Peiliang,Wang Xuechen

Strategic Study of CAE 2009, Volume 11, Issue 9, Pages 8-12

Abstract:

This paper presents general design frame for Weather Hazards Warning Information Broadcasting System based on Digital Audio Broadcasting technology and provides a digital format of Weather Warning message encoding by conducting research on weather hazards warning message encoding. With the Broadcasting System of Weather Hazards Warning Information, all weather hazard information can be effectively disseminated in the shortest time to the specific areas, departments and people. What's more, Warning Information can cover all our country,effectively solving "the last mile" issue of weather warning information broadcasting.

Keywords： weather hazards warning information broadcasting system message encoding digital audio broadcasting technology

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Noncoding RNAs and Their Potential Therapeutic Applications in Tissue Engineering

Shiying Li, Tianmei Qian, Xinghui Wang, Jie Liu, Xiaosong Gu

Engineering 2017, Volume 3, Issue 1, Pages 3-15 doi: 10.1016/J.ENG.2017.01.005

Abstract:

Tissue engineering is a relatively new but rapidly developing field in the medical sciences. Noncoding RNAs (ncRNAs) are functional RNA molecules without a protein-coding function; they can regulate cellular behavior and change the biological milieu of the tissue. The application of ncRNAs in tissue engineering is starting to attract increasing attention as a means of resolving a large number of unmet healthcare needs, although ncRNA-based approaches have not yet entered clinical practice. In-depth research on the regulation and delivery of ncRNAs may improve their application in tissue engineering. The aim of this review is: to outline essential ncRNAs that are related to tissue engineering for the repair and regeneration of nerve, skin, liver, vascular system, and muscle tissue; to discuss their regulation and delivery; and to anticipate their potential therapeutic applications.

Keywords： Tissue engineering Noncoding RNAs MicroRNAs Nerve Skin Liver Vascular system Muscle

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Long-term prediction for hierarchical-B-picture-based coding of video with repeated shots None

Xu-guang ZUO, Lu YU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 3, Pages 459-470 doi: 10.1631/FITEE.1601552

Abstract: The latest video coding standard High Efficiency Video Coding (HEVC) can achieve much higher coding efficiency than previous video coding standards. Particularly, by exploiting the hierarchical B-picture prediction structure, temporal redundancy among neighbor frames is eliminated remarkably well. In practice, videos available to consumers usually contain many repeated shots, such as TV series, movies, and talk shows. According to our observations, when these videos are encoded by HEVC with the hierarchical B-picture structure, the temporal correlation in each shot is well exploited. However, the long-term correlation between repeated shots has not been used. We propose a long-term prediction (LTP) scheme to use the long-term temporal correlation between correlated shots in a video. The long-term reference (LTR) frames of a source video are chosen by clustering similar shots and extracting the representative frames, and a modified hierarchical B-picture coding structure based on an LTR frame is introduced to support long-term temporal prediction. An adaptive quantization method is further designed for LTR frames to improve the overall video coding efficiency. Experimental results show that up to 22.86% coding gain can be achieved using the new coding scheme.

Keywords： High Efficiency Video Coding (HEVC) Long-term temporal correlation Long-term prediction Hierarchical B-picture structure

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Quantum coding genetic algorithm based on frog leaping

Xu Bo,Peng Zhiping,Yu Jianping and Ke Wende

Strategic Study of CAE 2014, Volume 16, Issue 3, Pages 108-112

Abstract:

The determinations of the rotation phase of quantum gates and mutation probability are the two main issues that restrict the efficiency of quantum genetic algorithm. This paper presents a quantum real coding genetic algorithm（QRGA）. QRGA used an adaptive means to adjust the direction and the size of the rotation angle of quantum rotation gate. In order to ensure the direction of evolution and population diversity，the mutation probability is guided based on the step of frog leaping algorithm which quantified by fuzzy logic. Comparative experimental results show that the algorithm can avoid falling into part optimal solution and astringe to the global optimum solution quickly，which has achieved good results in the running time and performance of the solution.

Keywords： quantum encoding quantum genetic algorithm frog leaping algorithm swarm intelligence

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Certificateless broadcast multi-signature for network coding Research Article

Huifang YU, Zhewei QI

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 9, Pages 1369-1377 doi: 10.1631/FITEE.2200271

Abstract: can save wireless network resources and is very fast in comparison with traditional routing. In real application scenarios, is vulnerable to pollution attacks and forgery attacks. To solve these problems, the certificateless broadcast multi-signature for (NC-CLBMS) method is devised, where each source node user generates a multi-signature about the message vector, and the intermediate node linearly combines the received data. NC-CLBMS is a multi-source multi-signature method with anti-pollution and anti-forgery advantages; moreover, it has a fixed signature length and its computation efficiency is very high. NC-CLBMS has extensive application prospects in unmanned aerial vehicle (UAV) communication networks, fifth-generation wireless networks, wireless sensor networks, mobile wireless networks, and Internet of Vehicles.

Keywords： Network coding Certificateless multi-signature Linear combination Homomorphic hash function

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Suboptimal network coding subgraph algorithms for 5G minimum-cost multicast networks None

Feng WEI, Wei-xia ZOU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 5, Pages 662-673 doi: 10.1631/FITEE.1700020

Abstract: To reduce the transmission cost in 5G multicast networks that have separate control and data planes, we focus on the minimum-power-cost network-coding subgraph problem for the coexistence of two multicasts in wireless networks. We propose two suboptimal algorithms as extensions of the Steiner tree multicast. The critical 1-cut path eliminating (C1CPE) algorithm attempts to find the minimum-cost solution for the coexistence of two multicast trees with the same throughput by reusing the links in the topology, and keeps the solution decodable by a coloring process. For the special case in which the two multicast trees share the same source and destinations, we propose the extended selective closest terminal first (E-SCTF) algorithm out of the C1CPE algorithm. Theoretically the complexity of the E-SCTF algorithm is lower than that of the C1CPE algorithm. Simulation results show that both algorithms have superior performance in terms of power cost and that the advantage is more evident in networks with ultra-densification.

Keywords： Network coding subgraph Minimum power cost 5G Separation architecture

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A Realization to Enhance the Signal Noise Ratio With Superposition on the Same Phase Signal

Han Xiuting,Wang Jiechun,Jiao Zhenqaing,Gao Fei,Song Yubo

Strategic Study of CAE 2005, Volume 7, Issue 10, Pages 64-68

Abstract:

This paper talks about method to enhance vibration signals gotten by hitting iron pipe to cause a vibrating response from which the damaged pipe information will be found. However, the reflection wave is usually too weak to acquire useful information correctly. The method, in which mccltiple similar signals are superposed, can not only decrease the noise in the signal, but also receive more message that may be lost before because of the interval of sample collection. Since the noise in signals could be treated as white noise, after multiple superposition the added noise will become to zero. The programmed the software to performs efficiently and conveniently. There are some examples in practice to express that this method is correct.

Keywords： vibrating wave reflection wave signal enhancement software for superposition signal

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A Study on Regional Assessment of Risk of Urban Major Hazard

Weng Tao,Zhu Jiping,Ma Minggeng,Liao Guangxuan,Wu zongzhi

Strategic Study of CAE 2006, Volume 8, Issue 9, Pages 80-84

Abstract:

The purpose of the article is to regard city as the object and work out a set of methods of assessment of risk. The improved technology of assessment of risk for urban major hazards and the method of quantization are studied. Principle of “level-superposed” of the grade of security is brought forward. Analyzing the possibility of all kinds of calamities to take place, confirming the grade of security relative to city of the kind calamity, and superposing its coefficient correlation in certain proportion, this paper gets the synthetical security atlas similar to the real one based on urban geography and urban resource. The evaluation index of social risk is set up based on the distributed comprehensive density of population, thus comprehensive safe planning to the dangerous source in the city can be carried on. At last, an example for providing the visual safe grade of city and the divided electronic map is given. The exploration work of this text can offer reference for relevant research work, which is also significant to improve the engineering level of urban assessment of risk, to prevent and control the emergence of great malignants accidents effectively such as fire, explosion, poisonous substance leakage, etc. , and to offer the scientific decision basis for city safety management.

Keywords： major hazards assessment of risk level-superposed grade of security GIS

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Representation learning via a semi-supervised stacked distance autoencoder for image classification Research Articles

Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 963-1118 doi: 10.1631/FITEE.1900116

Abstract: is an important application of deep learning. In a typical classification task, the classification accuracy is strongly related to the features that are extracted via deep learning methods. An is a special type of , often used for dimensionality reduction and feature extraction. The proposed method is based on the traditional , incorporating the “distance” information between samples from different categories. The model is called a semi-supervised distance . Each layer is first pre-trained in an unsupervised manner. In the subsequent supervised training, the optimized parameters are set as the initial values. To obtain more suitable features, we use a stacked model to replace the basic structure with a single hidden layer. A series of experiments are carried out to test the performance of different models on several datasets, including the MNIST dataset, street view house numbers (SVHN) dataset, German traffic sign recognition benchmark (GTSRB), and CIFAR-10 dataset. The proposed semi-supervised distance method is compared with the traditional , sparse , and supervised . Experimental results verify the effectiveness of the proposed model.

Keywords：自动编码器；图像分类；半监督学习；神经网络

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Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models Review

Changde Du, Jinpeng Li, Lijie Huang, Huiguang He

Engineering 2019, Volume 5, Issue 5, Pages 948-953 doi: 10.1016/j.eng.2019.03.010

Abstract:

Brain encoding and decoding via functional magnetic resonance imaging (fMRI) are two important aspects of visual perception neuroscience. Although previous researchers have made significant advances in brain encoding and decoding models, existing methods still require improvement using advanced machine learning techniques. For example, traditional methods usually build the encoding and decoding models separately, and are prone to overfitting on a small dataset. In fact, effectively unifying the encoding and decoding procedures may allow for more accurate predictions. In this paper, we first review the existing encoding and decoding methods and discuss
the potential advantages of a "bidirectional" modeling strategy. Next, we show that there are correspondences between deep neural networks and human visual streams in terms of the architecture and computational rules Furthermore, deep generative models (e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs)) have produced promising results in studies on brain encoding and decoding. Finally, we propose that the dual learning method, which was originally designed for machine translation tasks, could help to improve the performance of encoding and decoding models by leveraging large-scale unpaired data.

Keywords： Brain encoding and decoding Functional magnetic resonance imaging Deep neural networks Deep generative models Dual learning