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EmotionBox: a music-element-driven emotional music generation system using Recurrent Neural Network

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arXiv 2021年

作者： Zheng, Kaitong Meng, Ruijie Zheng, Chengshi Li, Xiaodong Sang, Jinqiu Cai, Juanjuan Wang, Jie Key Laboratory of Noise and Vibration Research Institute of Acoustics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China School of Electronics and Communication Engineering Guangzhou University Guangzhou China National Environmental Protection Engineering and Technology Center for Road Traffic Noise Control Beijing China

With the development of deep neural networks, automatic music composition has made great progress. Although emotional music can evoke listeners’ different emotions and it is important for artistic expression, only few researches have focused on generating emotional music. This paper presents EmotionBox -an music-element-driven emotional music generator that is capable of composing music given a specific emotion, where this model does not require a music dataset labeled with emotions. Instead, pitch histogram and note density are extracted as features that represent mode and tempo respectively to control music emotions. The subjective listening tests show that the Emotionbox has a more competitive and balanced performance in arousing a specified emotion than the emotion-label-based method. Copyright © 2021, The Authors. All rights reserved.

关键词： Music

Real-time watermark reconstruction for the identification of source information based on deep neural network

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Real-time watermark reconstruction for the identification of...

作者： Sinhal, Rishi Ansari, Irshad Ahmad Jain, Deepak Kumar Electronics and Communication Engineering PDPM Indian Institute of Information Technology Design and Manufacturing JabalpurMP482005 India Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China

A novel deep neural network-based image watermarking method is presented to identify the source of digital data that is shared/forwarded on the internet using various messenger apps. The app that is used to share/communicate the image at the very first time is also identified in the proposed method. The ten-digit mobile number of the source (user) and identification data of particular messenger app (i.e. WhatsApp, Snapchat, Kik, Facebook messenger, etc.) is combined to get the text watermark signal. The part of the watermarksignal representing specific mobile-based messenger application is obtained by randomizing the Walsh orthogonal codes using secret keys. To embed the watermark, the host image (shared/forwarded) is divided into blocks of equal size and then, slantlet transform is applied on each block. To get high reliability, three copies of the source information (user and app) are embedded during watermark embedding. Watermark extraction is performed using trained multilayer deep neural network. Furthermore, an optimal block selection logic is used to get improved results for real-time applications. The method is examined against various signal-processing attacks and high robustness with significant imperceptibility is attained. The method is also found to be fast enough for real-time applications. The prime objective of identifying the first user (source) and the shared/forwarded status (app detection) is successfully accomplished. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Deep neural networks

Nonreciprocal thermal radiation based on Fibonacci quasi-periodic structures

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arXiv 2021年

作者： Wu, Jun Wu, Feng Zhao, Tiancheng Zhai, Han Wu, Xiaohu Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Ministry of Education College of Electrical Engineering Anhui Polytechnic University Wuhu241000 China School of Optoelectronic Engineering Guangdong Polytechnic Normal University Guangzhou510665 China Beijing Aerospace Institute for Metrology and Measurement Technology Beijing100076 China Shandong Institute of Advanced Technology Jinan250100 China

To violate Kirchhoff's law is very important in the areas of thermal radiation. However, due to the weak nonreciprocity in natural materials, it is necessary to engineer novel structures to break the balance between emission and absorption. In this work, we introduce magneto-optical material into Fibonacci photonic crystals. Assisted by the nonreciprocity of the magneto-optical material and the excitation of Tamm plasmon polaritons, strong nonreciprocal thermal radiation can be realized. The difference between absorption and emission at wavelength of 16 μm can reach 0.9 at the incident angle of 60o. The distributions of the magnetic field are also calculated to verify the underlying physical origin. By engineering the parameters of the structure, it is found that strong nonreciprocal thermal radiation can be achieved at shorter wavelength and smaller incident angle. The results indicate that the Fibonacci magnetophotonic crystals are the promising candidate to engineer the nonreciprocal emission for various requirements. Copyright © 2021, The Authors. All rights reserved.

关键词： Heat radiation

Entanglement-enhanced ac magnetometry in the presence of Markovian noise

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Physical Review A 2025年第4期111卷 042605-042605页

作者： Thanaporn Sichanugrist Hajime Fukuda Takeo Moroi Kazunori Nakayama So Chigusa Norikazu Mizuochi Masashi Hazumi Yuichiro Matsuzaki Department of Physics The University of Tokyo Tokyo 113-0033 Japan International Center for Quantum-field Measurement Systems for Studies of the Universe and Particles (QUP) High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba Ibaraki 305-0801 Japan Department of Physics Tohoku University Sendai Miyagi 980-8578 Japan Theoretical Physics Group Lawrence Berkeley National Laboratory Berkeley California 94720 USA Berkeley Center for Theoretical Physics Department of Physics University of California Berkeley California 94720 USA Center for Theoretical Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Institute for Chemical Research Kyoto University Gokasho Uji-city Kyoto 611-0011 Japan Institute of Particle and Nuclear Studies (IPNS) KEK Tsukuba Ibaraki 305-0801 Japan Japan Aerospace Exploration Agency (JAXA) Institute of Space and Astronautical Science (ISAS) Sagamihara Kanagawa 252-5210 Japan Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU WPI) UTIAS The University of Tokyo Kashiwa Chiba 277-8583 Japan The Graduate University for Advanced Studies (SOKENDAI) Miura District Kanagawa 240-0115 Hayama Japan Department of Electrical Electronic and Communication Engineering Faculty of Science and Engineering Chuo University 1-13-27 Kasuga Bunkyo-ku Tokyo 112-8551 Japan

Entanglement is a resource to improve the sensitivity of quantum sensors. In an ideal case, using an entangled state as a probe to detect target fields, we can beat the standard quantum limit by which all classical sensors are bounded. However, since entanglement is fragile against decoherence, it is unclear whether entanglement-enhanced metrology is useful in a noisy environment. Its benefit is indeed limited when estimating the amplitude of dc magnetic fields under the effect of parallel Markovian decoherence, where the noise operator is parallel to the target field. In this paper, on the contrary, we show an advantage to using an entanglement over the classical strategy under the effect of parallel Markovian decoherence when we try to detect ac magnetic fields. We consider a scenario to induce a Rabi oscillation of the qubits with the target ac magnetic fields. Although we can, in principle, estimate the amplitude of the ac magnetic fields from the Rabi oscillation, the signal becomes weak if the qubit frequency is significantly detuned from the frequency of the ac magnetic field. We show that, by using the Greenberger-Horne-Zeilinger (GHZ) states, we can significantly enhance the signal of the detuned Rabi oscillation even under the effect of parallel Markovian decoherence. Our method is based on the fact that the interaction time between the GHZ states and ac magnetic fields scales as 1/L to mitigate the decoherence effect, where L is the number of qubits, which contributes to improving the bandwidth of the detectable frequencies of the ac magnetic fields. Our results pave the way for new applications of entanglement-enhanced ac magnetometry.

关键词： Qubits

On the regularization and optimization in quantum detector tomography

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arXiv 2022年

作者： Xiao, Shuixin Wang, Yuanlong Zhang, Jun Dong, Daoyi Yokoyama, Shota Petersen, Ian R. Yonezawa, Hidehiro University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Centre for Quantum Computation and Communication Technology Australian Research Council Centre for Quantum Dynamics Griffith University BrisbaneQLD4111 Australia Centre for Quantum Computation and Communication Technology Australian Research Council CanberraACT2600 Australia School of Engineering Australian National University CanberraACT2601 Australia

Quantum detector tomography (QDT) is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, we utilize regularization to improve the QDT accuracy whenever the probe states are informationally complete or informationally incomplete. In the informationally complete scenario, without regularization, we optimize the resource (probe state) distribution by converting it to a semidefinite programming problem. Then in both the informationally complete and informationally incomplete scenarios, we discuss different regularization forms and prove the mean squared error scales as O(1/N) or tends to a constant with N state copies under the static assumption. We also characterize the ideal best regularization for the identifiable parameters, accounting for both the informationally complete and informationally incomplete scenarios. Numerical examples demonstrate the effectiveness of different regularization forms and a quantum optical experiment test shows that a suitable regularization form can reach a reduced mean squared error. Copyright © 2022, The Authors. All rights reserved.

关键词： Tomography

Recursive Weighted Null-Space Fitting Method for Identification of Multivariate Systems ⁎

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IFAC-PapersOnLine 2021年第7期54卷 345-350页

作者： Mengyuan Fang Miguel Galrinho Håkan Hjalmarsson Zhejiang Engineering Laboratory of Industrial Internet Communication Technology Zhejiang Sci-Tech University Hangzhou China. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China OHB Sweden AB Kista Sweden Centre for Advanced Bio Production and Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Recursive identification of structured multivariate models is known to be difficult due to the general non-convexity of the likelihood function. In this work, we propose a recursive multivariate weighted null-space fitting method for identification of structured multivariate models. The proposed method first uses recursive least squares to estimate a high order non-parametric model, then a parametric model is obtained through weighted least squares from the non-parametric model. In this way, the method avoids directly optimizing a non-convex likelihood function and has guaranteed global convergency. Moreover, the proposed method is flexible in model structures and has the same finite sample performance as its off-line counterpart. We use simulation examples to illustrate the performance.

关键词： Recursive system identification multivariate systems weighted null-space fitting

Two-stage solution of quantum process tomography in the natural basis

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Two-stage solution of quantum process tomography in the natu...

IEEE Conference on Decision and control

作者： Shuixin Xiao Yuanlong Wang Daoyi Dong Jun Zhang School of Engineering and Information Technology University of New South Wales Canberra ACT Australia UMich-SJTU Joint Institute Shanghai Jiao Tong University Shanghai China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Centre for Quantum Computation and Communication Technology (Australian Research Council) Centre for Quantum Dynamics Griffith University Brisbane Queensland Australia

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Quantum process tomography (QPT) is a critical task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this paper, we propose an effective two-stage solution (TSS) for QPT in the natural basis. Our algorithm has O(d 6 ) computational complexity where d is the dimension of the quantum system. We establish an error bound $O\left( {\frac{{{d^5}}}{{\sqrt N }}} \right)$ where N is the copy number for each output state. The numerical examples compared with a convex optimization algorithm demonstrate the effectiveness of our algorithm.

关键词： Quantum system Error analysis Heuristic algorithms Process control Tomography Quantum state Convex functions

Adaptive neural network output-feedback tracking control for switched nonlinear ship maneuvering systems with time delays under arbitrary switching

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Adaptive neural network output-feedback tracking control for...

Chinese Automation Congress (CAC)

作者： Zhenhua Li Xiangxuan Ren Botao Dong Hong Chen Weidong Zhang SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya P. R. China Department of Automation Shanghai Jiao Tong University Shanghai P. R. China College of Mechatronics and Control Engineering Shenzhen University Shenzhen P. R. China School of Information and Communication Engineering Hainan University Haikou P. R. China

This paper focuses on the tracking control problem for switched nonlinear ship maneuvering time-delay systems with only a heading angle available by adaptive neural network (NN) output feedback. Using the backstepping method, an adaptive NN control mechanism is designed to solve the problem cooperated with the state observer. The uncertain terms of the system are approximated by NNs, and the state observer is designed to estimate the yaw rate and rudder angle. The unknown time delays are overcome by exploiting the common Lyapunov-Krasovskii functionals (CLKFs). Combined with error transformation, the proposed control method guarantees that i) all of the signals for the system are semi-global uniformly ultimately boundedness (SGUUB) under arbitrary switching; and, ii) the tracking error of system output keeps within a small neighborhood around the origin. The results of simulation results are shown to demonstrate the feasibility of the control strategy.

关键词： Adaptive systems Backstepping Delay effects Simulation Switches Artificial neural networks Observers

MDD-Enabled Two-Tier Terahertz Fronthaul in Indoor Industrial Cell-Free Massive MIMO

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arXiv 2023年

作者： Li, Bohan Dupleich, Diego Xia, Guoqing Zhou, Huiyu Zhang, Yue Xiao, Pei Yang, Lie-Liang The School of CMS Uni. of Leicester LeicesterLE1 7RH United Kingdom Fraunhofer Institute for Integrated Circuits IIS Erlangen91058 Germany The School of Engineering Uni. of Leicester LeicesterLE1 7RH United Kingdom The Institute of Communication Measurement Technology Chengdu610095 China 5GIC 6GIC Uni. of Surrey GuildfordGU2 7XH United Kingdom The School of ECS Univ. of Southampton SouthamptonSO17 1BJ United Kingdom

To make indoor industrial cell-free massive multiple-input multiple-output (CF-mMIMO) networks free from wired fronthaul, this paper studies a multicarrier-division duplex (MDD)enabled two-tier terahertz (THz) fronthaul scheme. More specifically, two layers of fronthaul links rely on the mutually orthogonal subcarreir sets in the same THz band, while access links are implemented over sub-6G band. The proposed scheme leads to a complicated mixed-integer nonconvex optimization problem incorporating access point (AP) clustering, device selection, the assignment of subcarrier sets between two fronthaul links and the resource allocation at both the central processing unit (CPU) and APs. In order to address the formulated problem, we first resort to the low-complexity but efficient heuristic methods thereby relaxing the binary variables. Then, the overall end-to-end rate is obtained by iteratively optimizing the assignment of subcarrier sets and the number of AP clusters. Furthermore, an advanced MDD frame structure consisting of three parallel data streams is tailored for the proposed scheme. Simulation results demonstrate the effectiveness of the proposed dynamic AP clustering approach in dealing with the varying sizes of networks. Moreover, benefiting from the well-designed frame structure, MDD is capable of outperforming TDD in the two-tier fronthaul networks. Additionally, the effect of the THz bandwidth on system performance is analyzed, and it is shown that with sufficient frequency resources, our proposed two-tier fully-wireless fronthaul scheme can achieve a comparable performance to the fiber-optic based systems. Finally, the superiority of the proposed MDD-enabled fronthaul scheme is verified in a practical scenario with realistic ray-tracing simulations. © 2023, CC BY.

关键词： Heuristic methods