In this article, a polarization-reconfigurable antenna based on magneto-electric (ME) dipoles is proposed. This antenna has three polarized states: linear polarization (LP), right-hand circular polarization (RHCP), an...
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System identification of nonlinear dynamical systems aims to predict the output of a system for a given input. In many engineering applications, the underlying physics are not fully understood and so there is no analy...
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ISBN:
(纸本)9780791887387
System identification of nonlinear dynamical systems aims to predict the output of a system for a given input. In many engineering applications, the underlying physics are not fully understood and so there is no analytical solution. The Wiener series is a classical data-driven technique that decomposes the system response into a set of orthogonal functionals of increasing order. Unlike standard black-box algorithms, such as neural networks, the series is highly interpretable and can offer insight into the nonlinearities present. To date, in order to calculate higher order terms in the Wiener series, vast quantities of data are needed. In this paper, a novel formulation of the Wiener series is developed in the frequency domain which applies to general stochastic inputs with an arbitrary spectrum. It is enhanced by placing Gaussian process priors over the Wiener kernels to enforce prior knowledge of their structure. This significantly reduces the quantity of data required for inference and has the benefit of enabling the calculation of the third order kernel for systems with long memory. The benefits were demonstrated in initial investigations using an idealised nonlinear oscillatory system. Decomposition of the system response into Wiener functionals also sheds light on the learnability of nonlinear dynamical systems, which could be used to assess the value of collecting additional data.
The purpose of this exploratory article is to investigate the possibility of utilizing Internet of Things (IoT) technology and machine literacy algorithms to develop intelligent irrigation systems for aquaculture agri...
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A major challenge of extant industrial systems designed under traditional engineering techniques and running on legacy automation platforms is that these systems are unable to automatically discover alternative soluti...
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Sentimental evaluation algorithms enable computer systems to stumble on, classify, and degree textual content sentiment. This era can be leveraged to evaluate consumer evaluations and provide remarks to corporations t...
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This paper offers an evaluation of the current literature associated with automated farming and the safety and privacy implications of device learning algorithms used in those systems. Gadget-studying algorithms are b...
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Recently, the use of brain-computer interface (BCI) systems based on Steady-State Visual Evoked Potentials (SSVEPs) has increased significantly. This increase is attributed to their advantageous features, such as acce...
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ISBN:
(纸本)9798331529710
Recently, the use of brain-computer interface (BCI) systems based on Steady-State Visual Evoked Potentials (SSVEPs) has increased significantly. This increase is attributed to their advantageous features, such as acceptable accuracy and minimal user training requirements. SSVEPs, a crucial pattern in BCI systems, are generated in the occipital region of the brain through visual stimulation within the frequency range of 6 to 60 Hz. In this paper, we propose two novel algorithms based on the Multiway Canonical Correlation Analysis (MCCA) method for frequency detection in SSVEP-based BCI systems. Our goal in the proposed algorithms, namely Sine-Cosine Filtered-CP-CCA (SCF-CP-CCA) and EEG-Filtered-CP-CCA (EF-CP-CCA), is to augment the initial reference signal by using tensor decomposition tools on multidimensional input EEG data. In both algorithms, we perform a modified Canonical Polyadic (CP) decomposition using an Alternating Least Squares (ALS) approach. Similar to the MCCA algorithm, for each stimulation frequency, a higher order array is generated using training data and the resulting tensor is decomposed using the proposed ALS method where loading matrices are filtered in each iteration. The obtained temporal loading matrix and a classic Sine-Cosine signal are then used in a Canonical Correlation Analysis (CCA) model to obtain the reference signal. The way the reference signal is generated, using the canonical scores of the EEG loading matrix or the Sine-Cosine matrix, leads to the proposal of two algorithms: SCF-CP-CCA and EF-CP-CCA. Finally, the Multiple Linear Regression (MLR) algorithm between the EEG test data matrix and the obtained reference signal is used to determine the target frequency. To evaluate the proposed algorithms, we use the real MEMANSSVEP dataset and compare the results of the proposed methods with those of the MCCA one. Compared to the MCCA algorithm, the average accuracy of the proposed SCF-CPCCA and EF-CP-CCA algorithms has improved by 3.
This paper presents a new assembler robot intended as part of a larger modular self-reconfigurable industrial machine system, with a design focus on (1) the integration of proprioceptive actuators into the assembler r...
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This paper presents a new assembler robot intended as part of a larger modular self-reconfigurable industrial machine system, with a design focus on (1) the integration of proprioceptive actuators into the assembler robot, and (2) the implementation of kinematic couplings in the modular connectors. We propose two comparison metrics concerning energy efficiency and mechanical repeatability, anticipating their growing importance as robot-assembled modular systems expand in scale. Our prototype assembler robot, Belty, employs high torque density quasi-direct drive belt transmission BLDC actuators. These offer benefits including superior efficiency, back-drivability, impact resilience, and the ability to perform dynamic motions such as hopping. Robust dynamic motion is advantageous for assembly tasks in restricted spaces, where contact-rich actions, such as dragging or bumping, can streamline assembly operations and algorithms. Additionally, we delve into the mechanical design of our connector, which is based on an exact-constraint mechanism called a kinematic coupling. This connector achieves a wide area of acceptance while also offering outstanding repeatability on the order of low tens of micrometers (10 mu m).
A reconfigurable microstrip patch antenna array operating at 25 GHz has been designed for K-band applications such as short-range radar and IoT networks. The antenna uses a 1x2 design with PIN diodes modelled as an RL...
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mmWave massive MIMO systems will be used extensively in future communications systems to enable the increasing demand for high data rates. In such systems, hybrid precoders are preferred to fully digital precoders to ...
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