Siamese Meets Diffusion Network: SMDNet for Enhanced Change Detection in High-Resolution RS Imagery

Recently, the application of deep learning to change detection (CD) has significantly progressed in remote sensing images. In recent years, CD tasks have mostly used architectures such as CNN and Transformer to identify these changes. However, these architectures have shortcomings in representing boundary details and are prone to false alarms and missed detections under complex lighting and weather conditions. For that, we propose a new network, Siamese Meets Diffusion Network (SMDNet). This network combines the Siam-U2Net Feature Differential Encoder (SU-FDE) and the denoising diffusion implicit model to improve the accuracy of image edge change detection and enhance the model's robustness under environmental changes. First, we propose an innovative SU-FDE module that utilizes shared weight features to capture differences between time series images and identify similarities between features to enhance edge detail detection. Furthermore, we add an attention mechanism to identify key coarse features to improve the model's sensitivity and accuracy. Finally, the diffusion model of progressive sampling is used to fuse key coarse features, and the noise reduction ability of the diffusion model and the advantages of capturing the probability distribution of image data are used to enhance the adaptability of the model in different environments. Our method's combination of feature extraction and diffusion models demonstrates effectiveness in change detection in remote sensing images. The performance evaluation of SMDNet on LEVIR-CD, DSIFN-CD, and CDD datasets yields validated F1 scores of 90.99%, 88.40%, and 88.47%, respectively. This substantiates the advanced capabilities of our model in accurately identifying variations and intricate details.Comment: 12 pages, 4 figure

Adaptive Navigation Control for Swarms of Autonomous Mobile Robots

This paper was devoted to developing a new and general coordinated adaptive navigation scheme for large-scale mobile robot swarms adapting to geographically constrained environments. Our distributed solution approach was built on the following assumptions: anonymity, disagreement on common coordinate systems, no pre-selected leader, and no direct communication. The proposed adaptive navigation was largely composed of four functions, commonly relying on dynamic neighbor selection and local interaction. When each robot found itself what situation it was in, individual appropriate ranges for neighbor selection were defined within its limited sensing boundary and the robots properly selected their neighbors in the limited range. Through local interactions with the neighbors, each robot could maintain a uniform distance to its neighbors, and adapt their direction of heading and geometric shape. More specifically, under the proposed adaptive navigation, a group of robots could be trapped in a dead-end passage,but they merge with an adjacent group to emergently escape from the dead-end passage. Furthermore, we verified the effectiveness of the proposed strategy using our in-housesimulator. The simulation results clearly demonstrated that the proposed algorithm is a simple yet robust approach to autonomous navigation of robot swarms in highlyclutteredenvironments. Since our algorithm is local and completely scalable to any size, it is easily implementable on a wide variety of resource-constrained mobile robots andplatforms. Our adaptive navigation control for mobile robot swarms is expected to be used in many applications ranging from examination and assessment of hazardous environments to domestic applications

Transnasal targeted delivery of therapeutics in central nervous system diseases: a narrative review

Currently, neurointervention, surgery, medication, and central nervous system (CNS) stimulation are the main treatments used in CNS diseases. These approaches are used to overcome the blood brain barrier (BBB), but they have limitations that necessitate the development of targeted delivery methods. Thus, recent research has focused on spatiotemporally direct and indirect targeted delivery methods because they decrease the effect on nontarget cells, thus minimizing side effects and increasing the patient’s quality of life. Methods that enable therapeutics to be directly passed through the BBB to facilitate delivery to target cells include the use of nanomedicine (nanoparticles and extracellular vesicles), and magnetic field-mediated delivery. Nanoparticles are divided into organic, inorganic types depending on their outer shell composition. Extracellular vesicles consist of apoptotic bodies, microvesicles, and exosomes. Magnetic field-mediated delivery methods include magnetic field-mediated passive/actively-assisted navigation, magnetotactic bacteria, magnetic resonance navigation, and magnetic nanobots—in developmental chronological order of when they were developed. Indirect methods increase the BBB permeability, allowing therapeutics to reach the CNS, and include chemical delivery and mechanical delivery (focused ultrasound and LASER therapy). Chemical methods (chemical permeation enhancers) include mannitol, a prevalent BBB permeabilizer, and other chemicals—bradykinin and 1-O-pentylglycerol—to resolve the limitations of mannitol. Focused ultrasound is in either high intensity or low intensity. LASER therapies includes three types: laser interstitial therapy, photodynamic therapy, and photobiomodulation therapy. The combination of direct and indirect methods is not as common as their individual use but represents an area for further research in the field. This review aims to analyze the advantages and disadvantages of these methods, describe the combined use of direct and indirect deliveries, and provide the future prospects of each targeted delivery method. We conclude that the most promising method is the nose-to-CNS delivery of hybrid nanomedicine, multiple combination of organic, inorganic nanoparticles and exosomes, via magnetic resonance navigation following preconditioning treatment with photobiomodulation therapy or focused ultrasound in low intensity as a strategy for differentiating this review from others on targeted CNS delivery; however, additional studies are needed to demonstrate the application of this approach in more complex in vivo pathways

Modeling of multi-mesh gear dynamic analysis based on pseudo-interference stiffness estimation

Modern trends of gear design require higher speed and efficiency along with greater reliability and power density. Furthermore, less vibration and noise, and less gear system weight and volume, are increasingly importance factors. The proposed research represents contributions in developing rigorous methods for analyzing, predicting and optimizing the static and dynamic characteristics of gear systems. The generation of gear geometry is greatly affected by the complexity of the tooth form whereas analytical procedures commonly become intractable because of their complexity. A user friendly interface is developed to generate gear geometry and construct the finite element mesh as a pre-processor. The characteristics of the gear contact are analyzed from the behavior of the gear tooth contact. The variation of mesh stiffness is one important factor causing transmission error. To accurately predict the running time in the Finite Element Method (FEM) and to improve the accuracy, simplicity and integrity of the gear factors. The Pseudo-Interference Stiffness Estimation (PISE) method draws upon the finite element analysis method to analyze non-linear, geometric based characteristics of the local contact region. The PISE method is directly applied to the parametric and the dynamic analysis of gear systems. For the dynamic analysis, a rigid body in the arbitrary space is expressed by three dimensional Euler\u27s equations of motion. These equations of motion are numerically integrated for each component to calculate the system configuration for the next time step. The fourth order multi-step Adams numerical integration is used to integrate the non-linear transient differential equations. The Pseudo-Interference Stiffness Estimation method is utilized for contact force between two bodies. The dynamic multi-mesh gear contact model will be analyzed for dynamic factors such as dynamic behaviors, static and dynamic loads, deformations, contact stresses, and vibration characteristics. This will be done in shorter, faster, and more economical way as far as computer and designer time are concerned.

Uv To Soft X-Ray Continuum Characteristics Of Bright Quasars 3c 273 And 1e1821+643

1821+643 were taken from Kolman et al. (1993). We then empirically characterize the shape of the UV continuum of 3C 273 and 1E1821+643. We model the continuum as a power-law curve and all the identified emission and absorption features in the observed spectrum as Gaussian profiles. We use the program SPECFIT (Kriss 1994) to fit the observed spectrum. SPECFIT runs in IRAF and determines best-fit parameters by simplex or Marquardt non-linear minimization of Ø 2 . As shown in Fig. 1a (the dot-dashed curve represents the best-fit continuum), the HUT spectrum of 3C 273 shows a distinct break in the continuum slope just longward of the redshifted Lyman edge (marked as L.E.). The continuum shape of 3C 273 is empirically well characterized by a broken power law in F . This continuum break may be a signature of the Lyman edge in the thermal spectrum of an accretion disk (Lee 1995). The combined HUT and HST=FOS&lt

A geometric approach to deploying robot swarms

We discuss the fundamental problems and practical issues underlying the deployment of a swarm of autonomous mobile robots that can potentially be used to build mobile robotic sensor networks. For the purpose, a geometric approach isproposed that allows robots to configure themselves into a two-dimensional plane with uniform spatial density. Particular emphasis is paid to the hole repair capability for dynamic network reconfiguration. Specifically, each robot interacts selectively with two neighboring robots so that three robots can converge onto each vertex of the equilateral triangle configuration. Based on the local interaction, the self-configuration algorithm is presented to enable a swarm of robots to form a communication network arranged in equilateral triangular lattices by shuffling the neighbors. Convergence of the algorithms is mathematically proved using Lyapunov theory. Moreover, it is verified that the self-reparation algorithm enables robot swarms to reconfigure themselves when holes existin the network or new robots are added to the network. Through extensive simulations, we validate the feasibility of applying the proposed algorithms to self-configuring anetwork of mobile robotic sensors. We describe in detail the features of the algorithm, including self-organization, self-stabilization, and robustness, with the results of thesimulation