A new classification method based on binary coding is proposed for composite information consisting of a variety of basic information types, which takes the composition of composite information into account at the ini...
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A new classification method based on binary coding is proposed for composite information consisting of a variety of basic information types, which takes the composition of composite information into account at the initial stage of design, so it has high efficiency and good scalability in search operation, and it is applied to a practical project successfully.
A binary coding based feature extraction (BCFE) method is proposed in this paper. In the BCFE method, the spectral signature of each pixel of hyperspectral image is partitioned into some equal segments. Then, the weig...
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A binary coding based feature extraction (BCFE) method is proposed in this paper. In the BCFE method, the spectral signature of each pixel of hyperspectral image is partitioned into some equal segments. Then, the weighted mean of spectral bands in each segment is considered as an extracted feature. BCFE uses a new method for calculation of weights. In BCFE, the binary codes of class means are obtained. Then, the information contained in the binary values and the edges of class means is used for calculation of weight in each band. The experimental results on three real hyperspectral images show the better performance of BCFE compared to some popular and state-of-the-art feature extraction methods, from the accuracy and computation time point of views, in a small sample size situation. (C) 2016 Elsevier Inc. All rights reserved.
The paper provides a new perspective on peak- and average-constrained Gaussian channels. Such channels model optical wireless communication (OWC) systems which employ intensity-modulation with direct detection (IM/DD)...
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The paper provides a new perspective on peak- and average-constrained Gaussian channels. Such channels model optical wireless communication (OWC) systems which employ intensity-modulation with direct detection (IM/DD). First, the paper proposes a new, capacity-preserving vector binary channel (VBC) model, consisting of dependent binary noisy bit-pipes. Then, to simplify coding over this VBC, the paper proposes coding schemes with varying levels of complexity, building on the capacity of binary-symmetric channels (BSC) and channels with state. The achievable rates are compared to capacity and capacity bounds, showing that coding for the BSC with state over the VBC achieves rates close to capacity at moderate to high signal-to-noise ratio (SNR), whereas simpler schemes achieve lower rates at lower complexity. The presented coding schemes are realizable using capacity-achieving codes for binary-input channels, such as polar codes. Numerical results are provided to validate the theoretical results and demonstrate the applicability of the proposed schemes.
Scene classification of high-resolution remote sensing (HRRS) imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing...
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Scene classification of high-resolution remote sensing (HRRS) imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW) model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this paper, we propose a fast binary coding (FBC) method, to effectively generate efficient discriminative scene representations of HRRS images. The main idea is inspired by the unsupervised feature learning technique and the binary feature descriptions. More precisely, equipped with the unsupervised feature learning technique, we first learn a set of optimal "filters" from large quantities of randomly-sampled image patches and then obtain feature maps by convolving the image scene with the learned filters. After binarizing the feature maps, we perform a simple hashing step to convert the binary-valued feature map to the integer-valued feature map. Finally, statistical histograms computed on the integer-valued feature map are used as global feature representations of the scenes of HRRS images, similar to the conventional BOW model. The analysis of the algorithm complexity and experiments on HRRS image datasets demonstrate that, in contrast with existing scene classification approaches, the proposed FBC has much faster computational speed and achieves comparable classification performance. In addition, we also propose two extensions to FBC, i.e., the spatial co-occurrence matrix and different visual saliency maps, for further improving its final classification accuracy.
Feature extraction is at the core of satellite scene classification task. In this paper, we propose a fast binary coding (FBC) method to effectively generate the global discriminative feature representation of image s...
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ISBN:
(纸本)9781479979295
Feature extraction is at the core of satellite scene classification task. In this paper, we propose a fast binary coding (FBC) method to effectively generate the global discriminative feature representation of image scenes. Equipped with unsupervised feature learning technique, we first learn a set of optimal "filters" from large quantities of randomly sampled image patches, and then we obtain feature maps by convolving image scene with the learned filter bank. After binarizing the feature maps, a simple skillful conversion of binary-valued feature map to integer-valued feature map is performed. The final statistical histograms, which are considered as the global feature representations of scenes, are computed on the integer-valued feature map similar to the conventional BOW model. Experiments on two datasets demonstrate that the proposed FBC achieve satisfying classification performance as well as has much faster computational speed compared with traditional scene classification methods.
Vehicle image classification can describe the visual vehicle with a semantically meaningful category directly. Motivated by its importance, this paper proposes a fast vehicle image classification based on binary codin...
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ISBN:
(纸本)9781479921867
Vehicle image classification can describe the visual vehicle with a semantically meaningful category directly. Motivated by its importance, this paper proposes a fast vehicle image classification based on binary coding. As for the vehicle image classification, this paper focuses on the image obtained from the video via analyzing the moving object near the key frames. The proposed method extracts a dense boosting binary feature computed with a boosted binary hash function, and then pools the features in different resolutions. At last, the SVM with spatial pyramid kernel finishes the classification task. In this work, 8 bytes for the feature computed with a hash function that ensures the real-time need. Experimental results on the vehicle datasets includes sedan, taxi, van, and truck show the efficiency and accuracy of the proposed method for vehicle classification in practice.
This paper presents a low profile and multiband antenna based on binary encoding and multiobjective optimization using artificial intelligence (AI). The antenna adopts a single-arm helical structure on the top layer a...
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This paper presents a low profile and multiband antenna based on binary encoding and multiobjective optimization using artificial intelligence (AI). The antenna adopts a single-arm helical structure on the top layer and two metal parasitic middle layers above the ground. The parasitic layers are composed of 10 x 10 rectangular squares, being similar as a chessboard, and they are coded as two sets of binary sequences, respectively, according to whether there is a metal sheet at the corresponding position. The multiobjective optimization to binary coding antenna with multiple effective bands, low profile, and high gains is carried out with three steps. Firstly, the discretized parameters are converted into continuous parameters, and the crude model of the antenna is optimized. Secondly, optimization algorithms under different weight assignments to the goals are applied to optimize the antenna and produce a large amount of sample data. AI is introduced to learn and train the model and then produce a large amount of sample data, and we finally get the optimal antenna structure that satisfies the multiobjective performance. The optimized prototype of the antenna is manufactured, and its impedance is well matched with the bandwidth of 1.43-2.494 GHz and 4.62-6.75 GHz. The measured maximum gain of prototype is 7.5 dBi with 10.98%lambda for profile.
This is predicted by the researchers that smart grid will replace the traditional electric power grid within next decade. This can be used to enhance efficiency, sensing and metering technologies. A pervasive communic...
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ISBN:
(纸本)9781479959679
This is predicted by the researchers that smart grid will replace the traditional electric power grid within next decade. This can be used to enhance efficiency, sensing and metering technologies. A pervasive communication infrastructure is a part and parcel for the flawless development of smart grid. One of the main issues for the deployment of the communication system for smart devices in a smart grid communication system is the high data rate and the service coverage. binary coding has already been a proven technique to extend the cellular communication coverage by recovering lost native packets. In order to enhance the coverage area in smart grid communication systems, we propose a cellular infrastructure with binary coding. We also explain the suitability of using the binary codebook in smart grid communication and how it can upgrade the system performance. Simulation results show that using binary coding we can serve more users which results in increase in the coverage area of the smart grid communication system.
With the acceleration of urbanization and population growth, water resource management is facing increasingly severe challenges. Especially in large-scale water supply networks, how to efficiently, fairly, and sustain...
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ISBN:
(纸本)9798400718144
With the acceleration of urbanization and population growth, water resource management is facing increasingly severe challenges. Especially in large-scale water supply networks, how to efficiently, fairly, and sustainably allocate and dispatch water resources has become a key issue. Therefore, this article proposes an optimization decision algorithm for water resource allocation and scheduling in large-scale water supply networks. When delving into the problem of water resource allocation and scheduling, it is necessary to first comprehensively consider the key factors of social, economic, and environmental dimensions, and establish a scientific and reasonable objective function. Consider the water supply capacity, transmission capacity, and water demand capacity of each region as constraints. The binary genetic algorithm is used to transform the allocation and scheduling problem of water resources into a binary coding form. Through iterative optimization of the initial population, genetic, mutation, crossover and other mechanisms, the optimal solution is found in the search space. Through experimental verification, this method can reasonably allocate water resources, ensure water supply security in various regions, improve the balance of the water supply network, and reduce water resource waste and loss.
The photonic spin Hall effect (PSHE), featured by a spin-dependent shift driven by its polarization handedness, is proposed to facilitate the applications in precision metrology and quantum information processing. Her...
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The photonic spin Hall effect (PSHE), featured by a spin-dependent shift driven by its polarization handedness, is proposed to facilitate the applications in precision metrology and quantum information processing. Here, due to the magnetoelectric coupling of the chirality, the PSHE is accompanied with Goos-Hanchen and Imbert-Fedorov effects. Taking advantage of this superiority, the transverse shift (TS) and longitudinal shift (LS) can be applied simultaneously. Rearranging the PT-symmetric scattering matrix, the responsive PSHE near the exceptional points and their basic physical mechanisms are discussed in detail in the case of complex chirality kappa. Re[kappa] and Im[kappa] regulated the rich (at multi-angle), gaint (reach upper limit) and tunable (magnitude and direction) TS and LS, respectively. Based on the chirality-modulated PSHE, the novel applications in binary code conversion and barcode encryption are proposed systematically. By incorporating the quantum weak measurement technology, our applications provide new mechanisms to realize optoelectronic communication.
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