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Single image super-resolution (SISR) is to reconstruct a high-resolution (HR) image from a corresponding low-resolution (LR) input. It is an effective way to solve the problem that infrared remote sensing images are usually suffering low resolution due to hardware limitations. Most previous learning-based SISR methods just use synthetic HR-LR image pairs (obtained by bicubic kernels) to learn the mapping from LR images to HR images. However, the underlying degradation in the real world is often different from the synthetic method, i.e., the real LR images are obtained through a more complex degradation kernel, which leads to the adaptation problem and poor SR performance. To handle this problem, we propose a novel closed-loop framework that can not only make full use of the learning ability of the channel attention module but also introduce the information of real images as much as possible through a closed-loop structure. Our network includes two independent generative networks for down-sampling and super-resolution, respectively, and they are connected to each other to get more information from real images. We make a comprehensive analysis of the training data, resolution level and imaging spectrum to validate the performance of our network for infrared remote sensing image super-resolution. Experiments on real infrared remote sensing images show that our method achieves superior performance in various training strategies of supervised learning, weakly supervised learning and unsupervised learning. Especially, our peak signal-to-noise ratio (PSNR) is 0.9 dB better than the second-best unsupervised super-resolution model on PROBA-V dataset. [ABSTRACT FROM AUTHOR]

Seismic inversion is aimed at building a mapping from low-resolution seismic data to high-resolution impedance data. Most of the traditional methods have satisfactory interpretability, and most parameters tend to have specific physical definitions. On the other hand, deep learning-based methods present poor interpretability as their prediction performance is not always clearly explainable. One of the significant challenges of deep learning-based methods is to quantify the uncertainty of the model. The uncertainty includes aleatoric uncertainty and epistemic uncertainty, and epistemic uncertainty can be used to evaluate the predicted accuracy of the trained model. In this article, we propose a new deep learning model called uncertainty backpropagation network (UB-Net) to perform impedance inversion. The proposed UB-Net is based on a closed-loop framework and can predict the impedance and the epistemic uncertainty simultaneously. UB-Net has three closed-loop data flows, whereby the predicted uncertainty is utilized as the weight of loss functions to improve the inversion accuracy. Experimental analyses demonstrate that UB-Net presents advanced inversion accuracy on both synthetic and real examples. In particular, the mean absolute error (MAE) on synthetic examples drops by 40%, and the Pearson correlation coefficient (PCC) on real examples increases by 2%. Besides, compared with existing approaches, UB-Net presents superior spatial continuity and preserves more geological structures such as little faults in real examples. [ABSTRACT FROM AUTHOR]

This brief presents a PVT (process, voltage, temperature) compensated CMOS resistor to frequency read-out circuit for resistive sensing applications. Resistive sensing is used in different applications, such as olfactory sensing. An accurate read-out circuit is needed to detect the resistance values for on-chip sensor arrays. Existing techniques suffer from PVT variations and require a large silicon chip area. The proposed design offers a simple, accurate, PVT insensitive sensor read-out circuit where a closed-loop configuration, a switched capacitor resistor, operational amplifier integrator, and a voltage-controlled oscillator are used. The design is implemented and fabricated using TSMC 180 nm CMOS technology. The value for the measured sensor’s resistance ranges between 10k and 100M $\Omega $ with an accuracy of 0.1% while the chip area is $0.0455{ {mm}}^{{2}}$ with $98.1\mu {W}$ power consumption. Also, the settling time for the step resistance change is about $2 \mu \text{s}$. [ABSTRACT FROM AUTHOR]

The existing chaotic image encryption algorithms have common defects: (i) ciphertext does not participate in the generation processes of chaotic pseudo-random sequences and key sequences; (ii) the entire encryption process does not have a closed-loop structure. In order to solve above problems, in this paper, an image segmentation encryption algorithm based on hyperchaotic system is proposed. We decompose the scrambled sequence into three sequences of different lengths: S1, S2 and S3. Then, the initial values of the chaotic system are updated by the sequences S2 and S3 and using the updated initial value iterates the chaotic system and generates the key sequence K3, and the sequence S1 is encrypted by the sequence K3 to obtain the cipher sequence C1, using the sequences C1 and S3 updates the initial value of the chaotic system, and using the updated initial value iterates the chaotic system and generates the key sequence K4, and using the sequence K4 encrypts the sequence S2 to obtain the cipher sequence C2. Thus, ciphertext participates in the generation processes of chaotic pseudo-random sequences and key sequences, and the entire encryption process has a closed-loop structure. The experimental results show that the encryption algorithm has high security and sensitivity. [ABSTRACT FROM AUTHOR]

In Diabetes Mellitus, the pancreas remains incapable of insulin administration that leads to hyperglycaemia, an escalated glycaemic concentration, which may stimulate many complications. To circumvent this situation, a closed‐loop control strategy is much needed for the exogenous insulin infusion in diabetic patients. This closed‐loop structure is often termed as an artificial pancreas that is generally established by the employment of different feedback control strategies. In this work, the authors have proposed an arbitrary‐order sliding mode control approach for development of the said mechanism. The term, arbitrary, is exercised in the sense of its applicability to any n ‐order controllable canonical system. The proposed control algorithm affirms the finite‐time effective stabilisation of the glucose–insulin regulatory system, at the desired level, with the alleviation of sharp fluctuations. The novelty of this work lies in the sliding manifold that incorporates indirect non‐linear terms. In addition, the necessary discontinuous terms are filtered‐out once before its employment to the plant, i.e. diabetic patient. The robustness, in the presence of external disturbances, i.e. meal intake is confirmed via rigorous mathematical stability analysis. In addition, the effectiveness of the proposed control strategy is ascertained by comparing the results with the standard literature. [ABSTRACT FROM AUTHOR]

Seismic inversion is a process of predicting high-resolution stratigraphic parameters from low-resolution seismic data. Traditional inversion methods tend to impose human prior knowledge, such as sparsity, to the modeling of the seismic inversion process. Nowadays, with the development of deep learning, the idea of modeling by learning from data has gained great attention in varieties of research fields. As a data-driven method, an artificial neural network (ANN) has already been explored by many researchers in the field of seismic inversion. Compared to ANN, a convolutional neural network (CNN) has a stronger learning ability attributing to its sophisticated structures. However, the development of CNN is limited by the amount of labeled data in many industrial fields including the field of seismic inversion. In order to mitigate the dependence of CNN on the amount of labeled data, we propose a closed-loop CNN structure in this article. The proposed closed-loop CNN can model the seismic forward and inversion process simultaneously from the training data set. Compared to traditional CNN, which is in an open-loop form, closed-loop CNN can not only learn from labeled data but also extract information contained in unlabeled data. The experimental results show that the closed-loop CNN has a better performance than both traditional methods and other deep learning-based methods on the synthetic data set and also can be efficiently applied on the real seismic data set. [ABSTRACT FROM AUTHOR]

For planar closed-loop structures with clearances, the angular and positional error uncertainties are studied. By using the vector translation method and geometric method, the boundaries of the errors are analyzed. The joint clearance is considered as being distributed uniformly in a circle area. A virtual link projection method is proposed to deal with the clearance affected length error probability density function (PDF) for open-loop links. The error relationship between open loop and closed loop is established. The open-loop length PDF and the closed-loop angular error PDF both approach being Gaussian distribution if there are many clearances. The angular propagation error of multi-loop structures is also investigated by using convolution. The positional errors of single and multiple loops are both discussed as joint distribution functions. Monte Carlo simulations are conducted to verify the proposed methods.

Jumping, a fundamental survival behavior observed in organisms, serves as a vital mechanism for adapting to the surrounding environment and overcoming significant obstacles within a given terrain. Here, we present a light-controlled soft jumping actuator inspired by asphondylia, which employs a closed-loop structure and utilizes a liquid crystal elastomer (LCE). Photo-mechanical coupling highlights the significant influence of the light source on the actuator's jumping behavior. Manipulating the light intensity, the relative position of stimulus and light lock, and the concentration of disperse red 1 (DR1) allows precise control over both the maximum take-off velocity and jump height. Furthermore, tailoring the size of the LCE actuator offers a means of regulating jumping behavior. Upon exposure to 460 nm LED irradiation, our actuator achieves remarkable performance, with a maximum jumping height of 10 body length (BL) and take-off velocity of 62 BL/s. These actuators accumulate and rapidly release energy, enabling the effective transportation of microcargos across substantial distances. Our research yields valuable insights into the realm of soft robotics, underscoring the pivotal importance of photo-mechanical coupling in the field of soft robotics, thereby serving as a catalyst for inspiring continued exploration of agile and capable systems by prestoring elastic energy.

A fundamental problem in proteomics is the identification of protein complexes and their components. We have used analytical ultracentrifugation with a fluorescence detection system (AU-FDS) to precisely and rapidly identify translation complexes in the yeast Saccharomyces cerevisiae. Following a one-step affinity purification of either poly(A)-binding protein (PAB1) or the large ribosomal subunit protein RPL25A in conjunction with GFP-tagged yeast proteins/RNAs, we have detected a 77S translation complex that contains the 80S ribosome, mRNA, and components of the closed-loop structure, eIF4E, eIF4G, and PAB1. This 77S structure, not readily observed previously, is consistent with the monosomal translation complex. The 77S complex abundance decreased with translational defects and following the stress of glucose deprivation that causes translational stoppage. By quantitating the abundance of the 77S complex in response to different stress conditions that block translation initiation, we observed that the stress of glucose deprivation affected translation initiation primarily by operating through a pathway involving the mRNA cap binding protein eIF4E whereas amino acid deprivation, as previously known, acted through the 43S complex. High salt conditions (1 M KCl) and robust heat shock acted at other steps. The presumed sites of translational blockage caused by these stresses coincided with the types of stress granules, if any, which are subsequently formed. [ABSTRACT FROM AUTHOR]

According to the current problems of safety management processes in coal mine enterprises, we introduced barrel theory to coal mine safety management, constructed the closed-loop structure of a coal mine safety management system, and pointed out that efficient safety management lies in three factors: safety quality of all of the staff in coal mine enterprises, weak links in security management systems, and cooperation among departments. After conducting detailed analysis of these three factors, we proposed concrete ways of preventing and controlling potential safety hazards during the process of coal mine production. [ABSTRACT FROM AUTHOR]

For planar closed-loop structures with clearances, the angular and positional error uncertainties are studied. By using the vector translation method and geometric method, the boundaries of the errors are analyzed. The joint clearance is considered as being distributed uniformly in a circle area. A virtual link projection method is proposed to deal with the clearance affected length error probability density function (PDF) for open-loop links. The error relationship between open loop and closed loop is established. The open-loop length PDF and the closed-loop angular error PDF both approach being Gaussian distribution if there are many clearances. The angular propagation error of multi-loop structures is also investigated by using convolution. The positional errors of single and multiple loops are both discussed as joint distribution functions. Monte Carlo simulations are conducted to verify the proposed methods. [ABSTRACT FROM AUTHOR]