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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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Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder Article

Zong-feng QI, Qiao-qiao LIU, Jun WANG, Jian-xun LI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 12,   Pages 1991-2000 doi: 10.1631/FITEE.1601395

Abstract: The nodes number of the hidden layer in a deep learning network is quite difficult to determine with traditional methods. To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder (KL-SAE) is proposed in this paper, which can be applied to battle damage assessment (BDA). This method can select automatically the hidden layer feature which contributes most to data reconstruction, and abandon the hidden layer feature which contributes least. Therefore, the structure of the network can be modified. In addition, the method can select automatically hidden layer feature without loss of the network prediction accuracy and increase the computation speed. Experiments on University of California-Irvine (UCI) data sets and BDA for battle damage data demonstrate that the method outperforms other reference data-driven methods. The following results can be found from this paper. First, the improved KL-SAE regression network can guarantee the prediction accuracy and increase the speed of training networks and prediction. Second, the proposed network can select automatically hidden layer effective feature and modify the structure of the network by optimizing the nodes number of the hidden layer.

Keywords： Battle damage assessment     Improved Kullback-Leibler divergence sparse autoencoder     Structural optimization     Feature selection    

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Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder Research Articles

Xin HE, Zhe ZHANG, Li XU, Jiapei YU,xinhe_ee@zju.edu.cn,xupower@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3,   Pages 452-462 doi: 10.1631/FITEE.2000667

Abstract: is important for a fair evaluation of the driving style. The longitudinal control of a vehicle is investigated in this study. The task can be considered as mapping of the in a different environment to the uniform condition. Unlike the model-based approach as in previous work, where a necessary driver model is employed to conduct the driving cycle test, the approach we propose directly normalizes the using an auto-encoder (AE) when following a standard speed profile. To ensure a positive correlation between the vehicle speed and , a gate constraint is imposed in between the encoder and decoder to form a gated AE (gAE). This approach is model-free and efficient. The proposed approach is tested for consistency with the model-based approach and for its applications to of the and fuel consumption analysis. Simulations are conducted to verify the effectiveness of the proposed scheme.

Keywords： Driving behavior     Normalization     Gated auto-encoder     Quantitative evaluation    

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Latent source-specific generative factor learning for monaural speech separation using weighted-factor autoencoder

Jing-jing Chen, Qi-rong Mao, You-cai Qin, Shuang-qing Qian, Zhi-shen Zheng,2221808071@stmail.ujs.edu.cn,mao_qr@ujs.edu.cn,2211908026@stmail.ujs.edu.cn,2211908025@stmail.ujs.edu.cn,3160602062@stmail.ujs.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 11,   Pages 1535-1670 doi: 10.1631/FITEE.2000019

Abstract: Much recent progress in monaural (MSS) has been achieved through a series of architectures based on s, which use an encoder to condense the input signal into compressed features and then feed these features into a decoder to construct a specific audio source of interest. However, these approaches can neither learn of the original input for MSS nor construct each audio source in mixed speech. In this study, we propose a novel weighted-factor (WFAE) model for MSS, which introduces a regularization loss in the objective function to isolate one source without containing other sources. By incorporating a latent attention mechanism and a supervised source constructor in the separation layer, WFAE can learn source-specific and a set of discriminative features for each source, leading to MSS performance improvement. Experiments on benchmark datasets show that our approach outperforms the existing methods. In terms of three important metrics, WFAE has great success on a relatively challenging MSS case, i.e., speaker-independent MSS.

Keywords： 语音分离；生成因子；自动编码器；深度学习    

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Deep 3D reconstruction: methods, data, and challenges Review Article

Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.2000068

Abstract: Three-dimensional (3D) reconstruction of shapes is an important research topic in the fields of computer vision, computer graphics, pattern recognition, and virtual reality. Existing 3D reconstruction methods usually suffer from two bottlenecks: (1) they involve multiple manually designed states which can lead to cumulative errors, but can hardly learn semantic features of 3D shapes automatically; (2) they depend heavily on the content and quality of images, as well as precisely calibrated cameras. As a result, it is difficult to improve the reconstruction accuracy of those methods. 3D reconstruction methods based on deep learning overcome both of these bottlenecks by automatically learning semantic features of 3D shapes from low-quality images using deep networks. However, while these methods have various architectures, in-depth analysis and comparisons of them are unavailable so far. We present a comprehensive survey of 3D reconstruction methods based on deep learning. First, based on different deep learning model architectures, we divide 3D reconstruction methods based on deep learning into four types, , , , and based methods, and analyze the corresponding methodologies carefully. Second, we investigate four representative databases that are commonly used by the above methods in detail. Third, we give a comprehensive comparison of 3D reconstruction methods based on deep learning, which consists of the results of different methods with respect to the same database, the results of each method with respect to different databases, and the robustness of each method with respect to the number of views. Finally, we discuss future development of 3D reconstruction methods based on deep learning.

Keywords： 深度学习模型；三维重建；循环神经网络；深度自编码器；生成对抗网络；卷积神经网络    

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Attention-based efficient robot grasp detection network Research Article

Xiaofei QIN, Wenkai HU, Chen XIAO, Changxiang HE, Songwen PEI, Xuedian ZHANG,xiaofei.qin@usst.edu.cn,obmmd_zxd@163.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1430-1444 doi: 10.1631/FITEE.2200502

Abstract: To balance the inference speed and detection accuracy of a grasp detection algorithm, which are both important for robot grasping tasks, we propose an ; structured pixel-level grasp detection named the attention-based efficient network (AE-GDN). Three spatial attention modules are introduced in the encoder stages to enhance the detailed information, and three channel attention modules are introduced in the stages to extract more semantic information. Several lightweight and efficient DenseBlocks are used to connect the encoder and paths to improve the feature modeling capability of AE-GDN. A high intersection over union (IoU) value between the predicted grasp rectangle and the ground truth does not necessarily mean a high-quality grasp configuration, but might cause a collision. This is because traditional IoU loss calculation methods treat the center part of the predicted rectangle as having the same importance as the area around the grippers. We design a new IoU loss calculation method based on an hourglass box matching mechanism, which will create good correspondence between high IoUs and high-quality grasp configurations. AE-GDN achieves the accuracy of 98.9% and 96.6% on the Cornell and Jacquard datasets, respectively. The inference speed reaches 43.5 frames per second with only about 1.2×10 parameters. The proposed AE-GDN has also been deployed on a practical robotic arm grasping system and performs grasping well. Codes are available at https://github.com/robvincen/robot_gradethttps://github.com/robvincen/robot_gradet.

Keywords： Robot grasp detection     Attention mechanism     Encoder–     decoder     Neural network    

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Displacement measuring grating interferometer: a review Special Feature on Precision Measurement and Inst

Peng-cheng HU, Di CHANG, Jiu-bin TAN, Rui-tao YANG, Hong-xing YANG, Hai-jin FU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 5,   Pages 631-654 doi: 10.1631/FITEE.1800708

Abstract:

A grating interferometer, called the “optical encoder,” is a commonly used tool for precise displacement measurements. In contrast to a laser interferometer, a grating interferometer is insensitive to the air refractive index and can be easily applied to multi-degree-of-freedom measurements, which has made it an extensively researched and widely used device. Classified based on the measuring principle and optical configuration, a grating interferometer experiences three distinct stages of development: homodyne, heterodyne, and spatially separated heterodyne. Compared with the former two, the spatially separated heterodyne grating interferometer could achieve a better resolution with a feature of eliminating periodic nonlinear errors. Meanwhile, numerous structures of grating interferometers with a high optical fold factor, a large measurement range, good usability, and multidegree-of-freedom measurements have been investigated. The development of incremental displacement measuring grating interferometers achieved in recent years is summarized in detail, and studies on error analysis of a grating interferometer are briefly introduced.

Keywords： Grating interferometer     Optical encoder     Displacement measurement     Precision measurement    

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Low-power, high-speed, and area-efficient sequential circuits by quantum-dot cellular automata: T-latch and counter study Research Article

Mohammad GHOLAMI, Zaman AMIRZADEH

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 3,   Pages 457-469 doi: 10.1631/FITEE.2200361

Abstract:

cellular automata (QCA)%29&ck%5B%5D=abstract&ck%5B%5D=keyword'> is a new nanotechnology for the implementation of nano-sized digital circuits. This nanotechnology is remarkable in terms of speed, area, and power consumption compared to complementary metal-oxide-semiconductor (CMOS) technology and can significantly improve the design of various logic circuits. We propose a new method for implementing a in QCA technology in this paper. The proposed method uses the intrinsic features of QCA in timing and clock phases, and therefore, the proposed cell structure is less occupied and less power-consuming than existing implementation methods. In the proposed , compared to previous best designs, reductions of 6.45% in area occupation and 44.49% in power consumption were achieved. In addition, for the first time, a reset-based and a with set and reset capabilities are designed. Using the proposed , a new 3-bit is developed which reduces 2.14% cell numbers compared to the best of previous designs. Moreover, based on the 3-bit , a 4-bit is designed, which reduces 0.51% cell numbers and 4.16% cross-section area compared to previous designs. In addition, two selective s are introduced to count from 0 to 5 and from 2 to 5. Simulations were performed using and tools in coherence vector engine mode. The proposed circuits are compared with related designs in terms of delay, cell numbers, area, and leakage power.

Keywords： Quantum-dot cellular automata (QCA)     Quantum-dot     T-latch     T-flip-flop     Counter     Selective counter     QCADesigner     QCAPro    

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A creative concept for designing and simulating quaternary logic gates in quantum-dot cellular automata Research Articles

Alireza Navidi, Reza Sabbaghi-Nadooshan, Massoud Dousti,alireza.navidi@srbiau.ac.ir,r_sabbaghi@iauctb.ac.ir,m_dousti@srbiau.ac.ir

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 11,   Pages 1441-1550 doi: 10.1631/FITEE.2000590

Abstract: New technologies such as have been showing some remarkable characteristics that standard complementary-metal-oxide semiconductor (CMOS) in deep sub-micron cannot afford. Modeling systems and designing multiple-valued logic gates with QCA have advantages that facilitate the design of complicated logic circuits. In this paper, we propose a novel creative concept for . The concept has been set in , the new simulator developed by our team exclusively for QCAs’ quaternary mode. Proposed basic gates such as MIN, MAX, and different types of inverters (SQI, PQI, NQI, and IQI) have been designed and verified by . This study will exemplify how fast and accurately works by its handy set of CAD tools. A 1×4 decoder is presented using our proposed main gates. Preference points such as the minimum delay, area, and complexity have been achieved in this work. QQCA main logic gates are compared with based on carbon nanotube field-effect transistor (CNFET). The results show that the proposed design is more efficient in terms of latency and energy consumption.

Keywords： 量子点细胞自动机（QCA）；四值逻辑；量子点细胞自动模拟器（QCASim）；四值QCA（QQCA）；四值译码器；四值门    

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Cellular automata based multi-bit stuck-at fault diagnosis for resistive memory Research Article

Sutapa SARKAR, Biplab Kumar SIKDAR, Mousumi SAHA

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1110-1126 doi: 10.1631/FITEE.2100255

Abstract: This paper presents a group-based dynamic scheme intended for resistive random-access memory (ReRAM). Traditional static random-access memory, dynamic random-access memory, NAND, and NOR flash memory are limited by their scalability, power, package density, and so forth. Next-generation memory types like ReRAMs are considered to have various advantages such as high package density, non-volatility, scalability, and low power consumption, but has been a problem. Unreliable memory operation is caused by permanent stuck-at faults due to extensive use of write- or memory-intensive workloads. An increased number of stuck-at faults also prematurely limit chip lifetime. Therefore, a cellular automaton (CA) based dynamic stuck-at fault-tolerant design is proposed here to combat unreliable cell functioning and variable cell lifetime issues. A scalable, block-level fault diagnosis and recovery scheme is introduced to ensure readable data despite multi-bit stuck-at faults. The scheme is a novel approach because its goal is to remove all the restrictions on the number and nature of stuck-at faults in general fault conditions. The proposed scheme is based on Wolfram's null boundary and periodic boundary CA theory. Various special classes of CAs are introduced for 100% fault tolerance: (SACAs), (TACAs), and (MACAs). The target micro-architectural unit is designed with optimal space overhead.

Keywords： Resistive memory     Cell reliability     Stuck-at fault diagnosis     Single-length-cycle single-attractor cellular automata     Single-length-cycle two-attractor cellular automata     Single-length-cycle multiple-attractor cellular automata    

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Indeterminacy Causal Inductive Automatic Reasoning Mechanism Based On Fuzzy State Describing

Yang Bingru,Tang Jing

Strategic Study of CAE 2000, Volume 2, Issue 5,   Pages 44-50

Abstract:

New framework of knowledge representation of fuzzy language field and fuzzy language value structure is shown in this paper. Then the generalized cell automation that can synthetically process fuzzy indeterminacy and random indeterminacy and the generalized inductive logic causal model are brought forward. On this basis, the new logic indeterminate causal inductive automatic reasoning mechanism which is based on fuzzy state describing is brought forward. At the end of this paper its application in the development of intelligent controller is discussed.

Keywords： language field     language value structure     generalized cell automation     generalized inductive logic causal model     automatic reasoning     intelligent controller    

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Two-dimensional Controllable Cellular Automata BasedPseudo Random Bit Sequence Generator

Zhu Baoping,Ma Qian,Liu Fengyu

Strategic Study of CAE 2007, Volume 9, Issue 6,   Pages 43-47

Abstract:

A novel cellular automata (CA) — two-dimensional controllable CA — is proposed in this paper.  According to characteristics of two-dimensional controllable CA,  a pseudo random generating method based on two-dimensional controllable CA with a trapezoidal structure is presented.  Simulation demonstrates that pseudo random bit sequence generator based on the two-dimensional controllable CA with a trapezoidal structure is easily implemented,  and can generate high speed bit sequence and excellent statistical properties.  This novel CA is widely used in symmetrical cryptography.

Keywords： cellular automata     pseudorandom number generators     controllable     cryptography    

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Performance analysis of the stop-and-wait automatic repeat request protocol under Markovian interruptions Regular Papers-Research Articles

Dashdondov KHONGORZUL, Yong-Ki KIM, Mi-Hye KIM

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 9,   Pages 1296-1306 doi: 10.1631/FITEE.1700185

Abstract: The performance of an integrated packet voice/data multiplexer using a stop-and-wait (SW) automatic repeat request (ARQ) protocol is discussed. We assume that the input for the data traffic is exponentially distributed in increments via the Poisson process, with each data packet transmitted within an individual slot time. Another assumption is that there is only a single voice signal, which has a higher priority over the data packet, and whose traffic is given via an on-off Markov process. Whenever the voice signal is active, the output link is used and will be blocked for the data packet. We introduce the concept of buffer occupancy to simplify the analysis, and discover that data multiplexers using the SW ARQ protocol exhibit a behavior of queueing delay and buffering when the interruption signal is given via a Markov process. Simulation results verify the validity of the analytical results.

Keywords： Stop-and-wait ARQ protocol     Markovian interruptions     Poisson distribution     Buffer occupancy     Waiting time    

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Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation None

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4,   Pages 471-480 doi: 10.1631/FITEE.1620342

Abstract: The segmentation of brain tumor plays an important role in diagnosis, treatment planning, and surgical simulation. The precise segmentation of brain tumor can help clinicians obtain its location, size, and shape information. We propose a fully automatic brain tumor segmentation method based on kernel sparse coding. It is validated with 3D multiple-modality magnetic resonance imaging (MRI). In this method, MRI images are pre-processed first to reduce the noise, and then kernel dictionary learning is used to extract the nonlinear features to construct five adaptive dictionaries for healthy tissues, necrosis, edema, non-enhancing tumor, and enhancing tumor tissues. Sparse coding is performed on the feature vectors extracted from the original MRI images, which are a patch of × × around the voxel. A kernel-clustering algorithm based on dictionary learning is developed to code the voxels. In the end, morphological filtering is used to fill in the area among multiple connected components to improve the segmentation quality. To assess the segmentation performance, the segmentation results are uploaded to the online evaluation system where the evaluation metrics dice score, positive predictive value (PPV), sensitivity, and kappa are used. The results demonstrate that the proposed method has good performance on the complete tumor region (dice: 0.83; PPV: 0.84; sensitivity: 0.82), while slightly worse performance on the tumor core (dice: 0.69; PPV: 0.76; sensitivity: 0.80) and enhancing tumor (dice: 0.58; PPV: 0.60; sensitivity: 0.65). It is competitive to the other groups in the brain tumor segmentation challenge. Therefore, it is a potential method in differentiation of healthy and pathological tissues.

Keywords： Brain tumor segmentation     Kernel method     Sparse coding     Dictionary learning    

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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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Title Author Date Type Operation

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

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

Journal Article

Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder

Zong-feng QI, Qiao-qiao LIU, Jun WANG, Jian-xun LI

Journal Article

Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder

Xin HE, Zhe ZHANG, Li XU, Jiapei YU,xinhe_ee@zju.edu.cn,xupower@zju.edu.cn

Journal Article

Latent source-specific generative factor learning for monaural speech separation using weighted-factor autoencoder

Jing-jing Chen, Qi-rong Mao, You-cai Qin, Shuang-qing Qian, Zhi-shen Zheng,2221808071@stmail.ujs.edu.cn,mao_qr@ujs.edu.cn,2211908026@stmail.ujs.edu.cn,2211908025@stmail.ujs.edu.cn,3160602062@stmail.ujs.edu.cn

Journal Article

Deep 3D reconstruction: methods, data, and challenges

Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn

Journal Article

Attention-based efficient robot grasp detection network

Xiaofei QIN, Wenkai HU, Chen XIAO, Changxiang HE, Songwen PEI, Xuedian ZHANG,xiaofei.qin@usst.edu.cn,obmmd_zxd@163.com

Journal Article

Displacement measuring grating interferometer: a review

Peng-cheng HU, Di CHANG, Jiu-bin TAN, Rui-tao YANG, Hong-xing YANG, Hai-jin FU

Journal Article

Low-power, high-speed, and area-efficient sequential circuits by quantum-dot cellular automata: T-latch and counter study

Mohammad GHOLAMI, Zaman AMIRZADEH

Journal Article

A creative concept for designing and simulating quaternary logic gates in quantum-dot cellular automata

Alireza Navidi, Reza Sabbaghi-Nadooshan, Massoud Dousti,alireza.navidi@srbiau.ac.ir,r_sabbaghi@iauctb.ac.ir,m_dousti@srbiau.ac.ir

Journal Article

Cellular automata based multi-bit stuck-at fault diagnosis for resistive memory

Sutapa SARKAR, Biplab Kumar SIKDAR, Mousumi SAHA

Journal Article

Indeterminacy Causal Inductive Automatic Reasoning Mechanism Based On Fuzzy State Describing

Yang Bingru,Tang Jing

Journal Article

Two-dimensional Controllable Cellular Automata BasedPseudo Random Bit Sequence Generator

Zhu Baoping,Ma Qian,Liu Fengyu

Journal Article

Performance analysis of the stop-and-wait automatic repeat request protocol under Markovian interruptions

Dashdondov KHONGORZUL, Yong-Ki KIM, Mi-Hye KIM

Journal Article

Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

Journal Article

A New Algorithm of Fractal Image Coding

Wang Xiuni,Jiang Wei,Wang Licun

Journal Article