The experimental findings unequivocally indicate that our proposed model's generalization capabilities surpass those of existing advanced methods, showcasing its effectiveness on unseen data.
Two-dimensional arrays, while essential for volumetric ultrasound imaging, experience resolution challenges due to limitations in aperture size, which result from the significant cost and complexity of fabricating, addressing, and processing large fully-addressed arrays. hepatic abscess This paper introduces Costas arrays as a gridded, sparse two-dimensional array architecture for volumetric ultrasound imaging. Costas arrays are structured with exactly one element per row and column, so that the vector displacement between any pair of elements is distinct. Eliminating grating lobes is facilitated by the aperiodic nature of these properties. Unlike previous reports, we researched the spatial distribution of active components using a 256-order Costas array over a wider region (96 x 96 pixels at 75 MHz center frequency), providing a pathway for higher-resolution imaging. Investigations employing focused scanline imaging on point targets and cyst phantoms revealed that Costas arrays displayed lower peak sidelobe levels than similarly sized random sparse arrays, exhibiting comparable contrast to Fermat spiral arrays. Furthermore, Costas arrays are arranged in a grid pattern, which might simplify the manufacturing process and include one element for each row and column, facilitating straightforward interconnection strategies. The proposed sparse arrays boast a higher lateral resolution and a wider field of view than the commonly used 32×32 matrix probes.
With high spatial resolution, acoustic holograms precisely manage pressure fields, enabling the projection of complex patterns with a minimal hardware footprint. Holograms have become attractive tools for various applications, including manipulation, fabrication, cellular assembly, and ultrasound therapy, due to their inherent capabilities. Although acoustic holograms offer considerable performance gains, their effectiveness has historically been linked to limitations in temporal control. After a hologram is constructed, the field it generates is permanently static and cannot be altered. A technique is introduced here that projects time-varying pressure fields by joining an input transducer array with a multiplane hologram, which is represented computationally as a diffractive acoustic network (DAN). By selectively activating elements of the input array, we generate varied and spatially complex amplitude patterns on a target plane. The multiplane DAN, as demonstrated numerically, outperforms a single-plane hologram in terms of performance, requiring a reduced total pixel count. More generally, we establish that a greater number of planes can improve the quality of the DAN's output for a constant number of degrees of freedom (DoFs, measured in pixels). Leveraging the pixel efficiency inherent in the DAN architecture, we devise a combinatorial projector capable of projecting a superior number of output fields compared to the transducer inputs. Through experimentation, we confirm that a multiplane DAN can be employed to construct such a projector.
The acoustic and performance characteristics of high-intensity focused ultrasound transducers utilizing lead-free sodium bismuth titanate (NBT) and lead-based lead zirconate titanate (PZT) piezoceramics are put under direct comparison in this study. Transducers at a third harmonic frequency of 12 MHz, are characterized by an outer diameter of 20 mm, a central hole with a 5 mm diameter, and a radius of curvature of 15 mm. Electro-acoustic efficiency, as determined by a radiation force balance, is scrutinized over a spectrum of input power levels, extending up to 15 watts. Evaluations of electro-acoustic efficiency demonstrate that NBT-based transducers achieve an average of approximately 40%, which is significantly lower than the roughly 80% efficiency seen in PZT-based transducers. NBT devices present a significantly higher degree of acoustic field inhomogeneity in schlieren tomography imaging, when juxtaposed with PZT devices. The inhomogeneity observed, as determined by pre-focal plane pressure measurements, stemmed from depolarization of substantial regions of the NBT piezoelectric component, occurring during the fabrication process itself. In summary, the performance of PZT-based devices outstripped that of lead-free material-based devices. The NBT devices, though promising for this application, could have better electro-acoustic effectiveness and acoustic field uniformity with the adoption of a low-temperature fabrication process or repoling after the manufacturing process.
Embodied question answering (EQA), a relatively new research area, involves an agent interacting with and gathering visual data from the environment to answer user queries. The broad potential applications of the EQA field, including in-home robots, self-driving vehicles, and personal assistants, draw a considerable amount of research attention. High-level visual tasks, like EQA, are especially vulnerable to noisy input data, as their reasoning processes are complex. Before the profits from the EQA field can be successfully translated into tangible applications, a significant improvement in robustness against label noise is necessary. In order to resolve this difficulty, we present a novel algorithm that is resilient to label noise for the EQA task. A novel, noise-resistant learning approach for visual question answering (VQA) is presented, employing joint training via co-regularization. Two parallel network branches are trained using a single loss function to filter noisy data. Noisy navigation labels in both trajectory and action levels are targeted for removal by a proposed two-stage hierarchical robust learning algorithm. The final step involves a robust joint learning technique that synchronizes the overall EQA system through the utilization of purified labels. Empirical findings indicate that our algorithm produces deep learning models possessing superior robustness to existing EQA models in noisy environments, particularly evident in extremely noisy conditions (45% noisy labels) and in less noisy yet impactful situations (20% noisy labels).
The problem of finding geodesics and studying generative models is closely associated with the challenge of interpolating between points. For geodesics, the aim is to identify the curves with minimal length, and in generative models, linear interpolation in the latent space is a frequent practice. However, this interpolation is dependent on the Gaussian function having a single peak. In conclusion, the difficulty of interpolating under the condition of a non-Gaussian latent distribution stands as an open problem. This article describes a general and unified interpolation method, permitting the search for both geodesics and interpolating curves within a latent space under conditions of any density. Our results enjoy a robust theoretical foundation, facilitated by the quality metric introduced for an interpolating curve. By maximizing the curve's quality measure, we essentially solve for a geodesic path, which is achieved by reformulating the Riemannian metric in the space. Examples are presented for three significant contexts. Our approach is shown to be readily applicable to the problem of finding geodesics on manifolds. Following this, our investigation centers on the identification of interpolations in pre-trained generative models. Our model demonstrates effective operation across a spectrum of densities. Furthermore, the interpolation process can be carried out on the data subset, where the data possesses a stipulated attribute. The final case study is structured around discovering interpolation within the complex chemical compound space.
Recent years have seen an increase in the academic attention given to grasping techniques in robotics. Still, grasping in congested visual fields remains a demanding problem for robots to address. The issue presented is one of crowded object placement, leaving insufficient space around them for the robot's gripper to operate effectively, making suitable grasping positions hard to pinpoint. To tackle this issue, the proposed method in this article leverages the combined pushing and grasping (PG) actions to enhance pose detection and robotic grasping. Our proposed method, PGTC, combines transformer-based models with convolutional layers to create a pushing-grasping grasping network. The pushing transformer network (PTNet), an object position prediction system grounded in a vision transformer (ViT), is designed to capture global and temporal features for enhanced accuracy in predicting object positions after a pushing action. To identify grasping actions, we introduce a cross-dense fusion network (CDFNet), leveraging both RGB and depth imagery to iteratively fuse and refine these visual inputs. click here In comparison to preceding networks, CDFNet exhibits enhanced precision in identifying the ideal grasping point. Finally, we leverage the network to conduct both simulated and real UR3 robot grasping experiments, resulting in the best performance observed thus far. Within the aforementioned URL, https//youtu.be/Q58YE-Cc250, you'll discover both the video and the corresponding dataset.
Within this article, we explore the cooperative tracking problem for nonlinear multi-agent systems (MASs) with unknown dynamics, which are impacted by denial-of-service (DoS) attacks. To address such a problem, this article details a hierarchical cooperative resilient learning method, comprising a distributed resilient observer and a decentralized learning controller. Communication delays and denial-of-service attacks can result from the multiple communication layers embedded within the hierarchical control architecture. Considering this factor, a dependable model-free adaptive control (MFAC) strategy is established to overcome the impact of communication delays and denial-of-service (DoS) attacks. art of medicine A virtual reference signal is generated uniquely for each agent to estimate the dynamic reference signal while enduring DoS attacks. To ensure effective tracking of each agent, the continuous virtual reference signal is broken down into individual data points. A decentralized MFAC algorithm is subsequently implemented on each agent, ensuring that each agent can monitor the reference signal solely through the utilization of locally gathered information.