Through experimental results, we highlight the exceptional generalization performance of our proposed model, which outperforms existing advanced methodologies on unseen domains.
Volumetric ultrasound imaging relies on two-dimensional arrays, but these are hampered by small aperture sizes and thus low resolution. The high manufacturing, addressing, and processing costs for large fully-addressed arrays contribute significantly to this limitation. biobased composite Costas arrays are proposed as a gridded, sparse two-dimensional array architecture for volumetric ultrasound image acquisition. In Costas arrays, each row and column contains exactly one element, and the vector displacement between any two elements is uniquely determined. These properties' aperiodicity is key to avoiding the emergence of grating lobes. In contrast to prior research, this study investigated the spatial distribution of active elements using a 256-order Costas array across a larger aperture (96 x 96 at 75 MHz center frequency) for high-resolution imaging purposes. Our focused scanline imaging studies of point targets and cyst phantoms revealed that Costas arrays exhibited lower peak sidelobe levels than random sparse arrays of identical size and maintained similar contrast properties to Fermat spiral arrays. Besides the grid layout, Costas arrays offer one element per row/column, potentially simplifying manufacturing and facilitating straightforward interconnections. While state-of-the-art matrix probes are commonly 32 by 32, the proposed sparse arrays surpass them in terms of both lateral resolution and field of view.
Acoustic holograms, with high spatial resolution, orchestrate pressure fields, projecting complex patterns with minimal equipment. Given their capabilities, holograms have become a desirable tool in a wide array of applications, from manipulation and fabrication to cellular assembly and ultrasound therapy. However, the effectiveness of acoustic holograms in terms of performance has traditionally been inversely related to their ability to manage temporal parameters. 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). Diversifying input elements within the array enables projection of unique and spatially complex amplitude fields onto the output. Through numerical means, we show that the multiplane DAN exhibits better performance than a single-plane hologram, demanding fewer pixels in the overall. Generally speaking, we find that an increase in the number of planes can lead to an improved output quality from the DAN, with the number of degrees of freedom (DoFs; pixels) held constant. In conclusion, we exploit the pixel efficiency of the DAN to introduce a combinatorial projector that surpasses the transducer input limit in projecting output fields. The experiments confirm that using a multiplane DAN allows the realization of a projector of this kind.
A direct comparative assessment of the performance and acoustic attributes of high-intensity focused ultrasonic transducers, employing lead-free sodium bismuth titanate (NBT) and lead-based lead zirconate titanate (PZT) piezoceramics, is presented. At a third harmonic frequency of 12 MHz, the transducers are all designed with an outer diameter of 20 mm, a central hole of 5 mm diameter and a 15 mm radius of curvature. Input power levels up to 15 watts are considered in the assessment of electro-acoustic efficiency by means of a radiation force balance. Comparative studies of electro-acoustic efficiency reveal that NBT-based transducers have an average value of approximately 40%, substantially less than the approximately 80% efficiency of PZT-based devices. Schlieren tomography reveals a substantially greater disparity in acoustic field homogeneity for NBT devices than for PZT devices. Pressure measurements in the pre-focal plane revealed that the inhomogeneity was a consequence of substantial depolarization of the NBT piezoelectric material, occurring during the manufacturing process. In summary, the performance of PZT-based devices outstripped that of lead-free material-based devices. While the NBT devices show promise in this application, their electro-acoustic efficiency and the consistency of the acoustic field could be enhanced by adopting a low-temperature fabrication method or by re-poling the devices post-processing.
In the burgeoning field of embodied question answering (EQA), an agent is tasked with addressing user questions through environmental exploration and visual data acquisition. Many researchers' attention is drawn to the EQA field due to its broad potential applications, including advancements in in-home robotics, self-propelled vehicles, and personal digital support systems. Noisy inputs can negatively impact high-level visual tasks, such as EQA, which rely on complex reasoning. The EQA field's profit potential cannot be realized in practical applications without first establishing a strong defense mechanism against label noise. To effectively address this issue, we develop a new learning algorithm, tolerant to label noise, intended for the EQA task. A robust visual question answering (VQA) system is built using a co-regularization-based noise-resistant learning method. This method involves training two parallel network branches under the supervision of a unified loss function. Filtering noisy navigation labels at both trajectory and action levels is accomplished using a proposed two-stage hierarchical robust learning algorithm. To summarize, a robust joint learning method is applied to align the operations of the entire EQA system, with purified labels providing input. Experimental results highlight the superior robustness of our algorithm-trained deep learning models compared to existing EQA models in challenging noisy environments, including both extremely noisy situations (45% noisy labels) and lower-noise scenarios (20% noisy labels).
The search for geodesics, the analysis of generative models, and the process of interpolation between points are closely related and mutually impactful challenges. Within geodesic analysis, the shortest possible curves are sought, but in generative models, linear interpolation in the latent space is generally the method of choice. In spite of this, the interpolation process makes an implicit assumption about the Gaussian's unimodal structure. Subsequently, the predicament of interpolation within a non-Gaussian latent space is still an open challenge. This article proposes a general and unified interpolation technique. It allows for the concurrent search of geodesics and interpolating curves in latent space, regardless of the density. Our results derive substantial theoretical support from the novel quality measure of an interpolating curve. Our analysis reveals that maximizing the curve's quality measure is mathematically equivalent to locating a geodesic, under a specific redefinition of the Riemannian metric within the space. Examples are presented for three significant contexts. The calculation of geodesics on manifolds benefits from our readily applicable approach, as demonstrated. Next, we dedicate our focus to locating interpolations within pre-trained generative models. Our model displays remarkable adaptability in dealing with the issue of arbitrary density. Furthermore, the interpolation process can be carried out on the data subset, where the data possesses a stipulated attribute. In the concluding case, the emphasis is on pinpointing interpolation phenomena within the space of chemical compounds.
Researchers have actively explored robotic grasping procedures over the recent years. However, the difficulty of grasping objects in environments filled with obstructions continues to be a significant challenge for robots. This particular arrangement has objects positioned too closely together, creating a lack of space for the robot's gripper to operate, making it challenging to identify an appropriate grasping point. This article suggests utilizing a combination of pushing and grasping (PG) actions to improve pose detection and robotic grasping for problem resolution. We present a pushing-grasping network (PGTC) that integrates transformer and convolutional architectures for grasping. To anticipate the outcome of pushing actions, a vision transformer (ViT)-based pushing transformer network (PTNet) is proposed. This network effectively integrates global and temporal information for improved object position prediction post-push. We present a cross-dense fusion network (CDFNet) for grasping detection, which effectively integrates RGB and depth data through repeated fusion processes. 1-Azakenpaullone datasheet Prior networks are surpassed by CDFNet's increased accuracy in determining the optimal grasp position. The network's capabilities extend to both simulation and real-world experiments with the UR3 robot, culminating in cutting-edge performance. At the address https//youtu.be/Q58YE-Cc250, one can find the video and the dataset.
Concerning the cooperative tracking problem for a class of nonlinear multi-agent systems (MASs) with unknown dynamics under denial-of-service (DoS) attacks, this article provides an analysis. This article introduces a novel, hierarchical, cooperative, and resilient learning method for such a problem. This method includes a distributed resilient observer and a decentralized learning controller. Hierarchical control architectures, with their inherent communication layers, might suffer from communication delays and denial-of-service attacks. Motivated by this principle, a sturdy model-free adaptive control (MFAC) approach is engineered to resist the effect of communication delays and denial-of-service (DoS) assaults. continuous medical education A virtual reference signal is specifically designed for each agent to gauge the shifting reference signal, mitigating the impact of 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.