Activating Wider Areas in Image Super-Resolution

The prevalence of convolution neural networks (CNNs) and vision transformers (ViTs) has markedly revolutionized the area of single-image super-resolution (SISR). To further boost the SR performances, several techniques, such as residual learning and attention mechanism, are introduced, which can be largely attributed to a wider range of activated area, that is, the input pixels that strongly influence the SR results. However, the possibility of further improving SR performance through another versatile vision backbone remains an unresolved challenge. To address this issue, in this paper, we unleash the representation potential of the modern state space model, i.e., Vision Mamba (Vim), in the context of SISR. Specifically, we present three recipes for better utilization of Vim-based models: 1) Integration into a MetaFormer-style block; 2) Pre-training on a larger and broader dataset; 3) Employing complementary attention mechanism, upon which we introduce the MMA. The resulting network MMA is capable of finding the most relevant and representative input pixels to reconstruct the corresponding high-resolution images. Comprehensive experimental analysis reveals that MMA not only achieves competitive or even superior performance compared to state-of-the-art SISR methods but also maintains relatively low memory and computational overheads (e.g., +0.5 dB PSNR elevation on Manga109 dataset with 19.8 M parameters at the scale of 2). Furthermore, MMA proves its versatility in lightweight SR applications. Through this work, we aim to illuminate the potential applications of state space models in the broader realm of image processing rather than SISR, encouraging further exploration in this innovative direction.Comment: 19 pages, 7 figure

Geometric Multi-Model Fitting by Deep Reinforcement Learning

This paper deals with the geometric multi-model fitting from noisy, unstructured point set data (e.g., laser scanned point clouds). We formulate multi-model fitting problem as a sequential decision making process. We then use a deep reinforcement learning algorithm to learn the optimal decisions towards the best fitting result. In this paper, we have compared our method against the state-of-the-art on simulated data. The results demonstrated that our approach significantly reduced the number of fitting iterations

State estimation based on unscented Kalman filter for semi-active suspension systems

In this paper, a novel approach to estimate vehicle vibration state information in real time is proposed; it is based on unscented Kalman filter (UKF) theory. The UKF is based on the unscented transfer technique which considers high order terms during the measurement and update stage during the estimation. The proposed observer uses easily accessible measurements such as accelerations and suspension deflections to estimate the sprung and unspring mass vertical velocity for the suspension systems of full vehicle under unknown road disturbance. And it is with low sensitivity and robust to the unknown road surfaces. Matlab/Carsim co-simulation experiments are carried out to validate the performance of the estimator under two typical road excitations. The simulation results clearly indicate that the proposed UKF sate observer is precise

Neutral network-PID control algorithm for semi-active suspensions with magneto-rheological damper

In this paper, a semi-active suspension control system based on Magneto-Rheological (MR) damper is designed for a commercial vehicle to improve the ride comfort and driving stability. A mathematical model of MR damper based on the Bouc-Wen hysteresis model is built. The mathematical model could precisely describe the characteristics of MR damper compared with the bench test results. The neural network-PID controller is designed for the semi-active suspension systems. According to the numerical results, the proposed controller can constrain vehicle vibrations and roll angle significantly. A detailed multi-body dynamic model of the light vehicle with four semi-active suspensions are established, and an actual vehicle handling and stability tests are carried out to verify the control performances of the proposed controller. It can be concluded that MR semi-active suspension systems can play a key role in coordination between the ride comfort and handling stability for the commercial vehicle

FABRICATION OF ZINC OXIDE MICRO-NANOSTRUCTURES AND THEIR APPLICATIONS IN GAS SENSING AND NANOCOMPOSITES

To date, one-dimensional ZnO micro/nanostructures have been attracting much attention for wide potential applications due to their unique electrical, piezoelectric, optoelectronic, and photochemical properties. The overall objective of this dissertation is to grow various ZnO micro and nanostructures using a novel microwave thermal evaporation-deposition approach, to explore the application of ZnO nanostructures in gas sensing, and to fabricate and characterize multifunctional ZnO nanowires-polyimide nanocomposite. Therefore, three parts were included in this study: (1) A novel thermal evaporation-deposition method using microwave energy was investigated. Batch of ZnO structures including microtubes, microrods, nanotubes, nanowires and nanobelts have been successfully synthesized in the microwave system with a unique source materials-substrate configuration and a desirable temperature profile. These products are pure, structurally uniform, and single crystalline. The photoluminescence (PL) exhibits strong ultraviolet emission at room temperature, indicating potential applications for short-wave light-emitting photonic devices. (2) Piezoelectric crystal langasite bulk acoustic wave (LGS) resonator based high temperature gas sensor was fabricated. Ordered ZnO nanowire arrays were grown on the langasite resonator as the sensitive layer by two-step hydrothermal method at low temperature. The gas sensor coated with ZnO nanowire arrays exhibited good sensitivity to NO2 and NH3. The response of the sensor is fast due to the large surface area of ZnO nanowires. In addition, this work demonstrates that the combination of nanowire arrays with langasite thickness shear mode resonators could provide a promising high temperature gas sensing platform with both high sensitivity and enhanced response speed. (3) The nanocomposite with controlled alignment of ZnO nanowires in the polyimide matrix was achieved using self-alignment method and external electric field assisted method. For the the self-alignment process, the morphologies of the designed nanocomposites were dramatically influenced by the viscosity of the polymer and the geometrical structure of ZnO nanowires. For the nanocomposite prepared by the electric field assisted alignment, the density and the alignment degree of ordered ZnO nanowires significantly depended on the magnitude and the frequency of the applied ac electric field. The DC offset voltage had strong effect on the deposition sites of nanowires. The resultant nanocomposite devices exhibited great dielectric constant and conductivity enhancement at room temperature due to the interfacial effect between ZnO nanowires and the polymer matrix. These nanocomposites combining the superb properties of ZnO nanowires with the polyimide matrix provide a smart material candidate for multifunctional applications that require self-sensing and self-actuation capabilities. The self-alignment method and electric field assisted alignment method also provide a bright route to combine superb properties of nanomaterials with the lightweight, flexibility, and manufacturability of dielectric polymers for future generations of multifunctional materials

All-IP wireless sensor networks for real-time patient monitoring

AbstractThis paper proposes the all-IP WSNs (wireless sensor networks) for real-time patient monitoring. In this paper, the all-IP WSN architecture based on gateway trees is proposed and the hierarchical address structure is presented. Based on this architecture, the all-IP WSN can perform routing without route discovery. Moreover, a mobile node is always identified by a home address and it does not need to be configured with a care-of address during the mobility process, so the communication disruption caused by the address change is avoided. Through the proposed scheme, a physician can monitor the vital signs of a patient at any time and at any places, and according to the IPv6 address he can also obtain the location information of the patient in order to perform effective and timely treatment. Finally, the proposed scheme is evaluated based on the simulation, and the simulation data indicate that the proposed scheme might effectively reduce the communication delay and control cost, and lower the packet loss rate

Neural-Learning-Based Telerobot Control with Guaranteed Performance

© 2013 IEEE. In this paper, a neural networks (NNs) enhanced telerobot control system is designed and tested on a Baxter robot. Guaranteed performance of the telerobot control system is achieved at both kinematic and dynamic levels. At kinematic level, automatic collision avoidance is achieved by the control design at the kinematic level exploiting the joint space redundancy, thus the human operator would be able to only concentrate on motion of robot's end-effector without concern on possible collision. A posture restoration scheme is also integrated based on a simulated parallel system to enable the manipulator restore back to the natural posture in the absence of obstacles. At dynamic level, adaptive control using radial basis function NNs is developed to compensate for the effect caused by the internal and external uncertainties, e.g., unknown payload. Both the steady state and the transient performance are guaranteed to satisfy a prescribed performance requirement. Comparative experiments have been performed to test the effectiveness and to demonstrate the guaranteed performance of the proposed methods