Improving the operational bandwidth of a 1-3 piezoelectric composite transducer using Sierpinski Gasket fractal geometry

Wider operational bandwidth is an important requirement of an ultrasound transducer across many applications. It has been reported mathematically that by having elements with varying length scales in the piezoelectric transducer design, the device may possess a wider operational bandwidth or a higher sensitivity compared to a conventional device. In this paper, the potential for extending the operational bandwidth of a 1-3 piezoelectric composite transducer configured in a fractal geometry, known as the Sierpinski Gasket (SG), will be investigated using finite element analysis package PZFlex (Thornton Tomasetti). Two equivalent piezocomposite designs will be simulated: a conventional 1-3 piezocomposite structure and the novel SG fractal geometry arrangement. The transmit voltage response and open circuit voltage extracted from the simulations are used to illustrate the improved bandwidth predicted from the fractal composite design

Effects of Virtual Reality Intervention on Neural Plasticity in Stroke Rehabilitation: A Systematic Review

Effects of virtual reality intervention on neural plasticity in stroke rehabilitation: a systematic review Background: Virtual reality (VR) has been increasingly adopted in medicine for recent decades, and this emerging technology has shown promising results in stroke rehabilitation. As a computer-generated simulation technology, VR creates an enriched and gamified environment, facilitate task-specific training and provides multimodal feedback to augment the functional recovery by driving the experience-dependent neural plasticity. Currently, a majority of research focuses on effects of VR on functional recovery and clinical outcomes; understanding how the neural underpinnings of those effects are critical for optimizing the use of VR technology for patient care. Purpose: This systematic review summarized the current literature regarding the effects of VR-based rehabilitation on neural changes in stroke patients. Methods: By following the PRISMA reporting guideline, this systematic review was conducted and registered at the PROSPERO (ID: CRD42020196405). Six literature databases were searched, including Medline via Ebsco, Embase, PsycoINFO, IEEE Explore, Cumulative Index of Nursing and Allied Health, and Scopus. The results were limited to articles published between 2000-2020 and in English. The search strategy was designed by an experienced academic medical librarian and using keywords such as “virtual reality”, “stroke” and “neural plasticity”. The Physiotherapy Evidence Database (PEDro) scale was used to evaluate the methodological quality of all included randomized controlled studies. Two reviewers screened, selected and evaluated the articles independently, and any discrepancy was resolved by the third experienced reviewer. Results: A total of 217 records were identified from the six databases, and another 4 were found from through other sources. After removing duplicates, 137 records remained and were screened. 29 full-text articles were assessed for eligibility, and finally 6 randomized controlled trials were included in this systematic review. In terms of the quality assessment, all of the 6 RCTs had the PEDro score no less than 6, which was considered as high quality. The interactive VR gaming systems were used in both upper and lower extremity exercises. The functional magnetic resonance, electroencephalogram (EEG) and navigated brain stimulation techniques were used to measure the changes of neural activities. The general findings regarding the VR-induced neural plasticity shown across the studies include: (1) improved interhemispheric balance, with the shift of activation from the contralesional to the ipsilesional primary sensorimotor cortex dominance during the paretic limb movement; (2) increased cortical representation mapping of the lesioned side muscles; (3) the correlation between improved neural plasticity measures and enhanced behavior outcomes; (4) increased activation of the frontal region identified by the EEG; (5) the mirror neuron system may be involved in the VR intervention. Conclusions: Virtual reality induced changes in neural plasticity for stroke patients, these changes reflected the neural substrates of restoration and compensation of functional deficits. The positive correlation between the neural plasticity changes and functional recovery elucidates the mechanisms of VR’s therapeutic effects in stroke rehabilitation. Implications: This review prompts the systematic understanding of the neurophysiological mechanisms of VR-based stroke rehabilitation, and provides the emerging evidence for ongoing innovation of VR system and its application in stroke rehabilitation. Keywords: virtual reality, simulation, stroke, neural plasticit

Color-NeuS: Reconstructing Neural Implicit Surfaces with Color

The reconstruction of object surfaces from multi-view images or monocular video is a fundamental issue in computer vision. However, much of the recent research concentrates on reconstructing geometry through implicit or explicit methods. In this paper, we shift our focus towards reconstructing mesh in conjunction with color. We remove the view-dependent color from neural volume rendering while retaining volume rendering performance through a relighting network. Mesh is extracted from the signed distance function (SDF) network for the surface, and color for each surface vertex is drawn from the global color network. To evaluate our approach, we conceived a in hand object scanning task featuring numerous occlusions and dramatic shifts in lighting conditions. We've gathered several videos for this task, and the results surpass those of any existing methods capable of reconstructing mesh alongside color. Additionally, our method's performance was assessed using public datasets, including DTU, BlendedMVS, and OmniObject3D. The results indicated that our method performs well across all these datasets. Project page: https://colmar-zlicheng.github.io/color_neus

3D-LLM: Injecting the 3D World into Large Language Models

Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs. Project Page: : https://vis-www.cs.umass.edu/3dllm/.Comment: Project Page: : https://vis-www.cs.umass.edu/3dllm

3D-VLA: A 3D Vision-Language-Action Generative World Model

Recent vision-language-action (VLA) models rely on 2D inputs, lacking integration with the broader realm of the 3D physical world. Furthermore, they perform action prediction by learning a direct mapping from perception to action, neglecting the vast dynamics of the world and the relations between actions and dynamics. In contrast, human beings are endowed with world models that depict imagination about future scenarios to plan actions accordingly. To this end, we propose 3D-VLA by introducing a new family of embodied foundation models that seamlessly link 3D perception, reasoning, and action through a generative world model. Specifically, 3D-VLA is built on top of a 3D-based large language model (LLM), and a set of interaction tokens is introduced to engage with the embodied environment. Furthermore, to inject generation abilities into the model, we train a series of embodied diffusion models and align them into the LLM for predicting the goal images and point clouds. To train our 3D-VLA, we curate a large-scale 3D embodied instruction dataset by extracting vast 3D-related information from existing robotics datasets. Our experiments on held-in datasets demonstrate that 3D-VLA significantly improves the reasoning, multimodal generation, and planning capabilities in embodied environments, showcasing its potential in real-world applications.Comment: Project page: https://vis-www.cs.umass.edu/3dvla

Broadband piezocrystal transducer array for non-destructive evaluation imaging applications

A 32-element, 5MHz linear array, incorporating PMN-PT 1-3 piezo-polymer composite, has been designed using finite element (FE) modelling. The Elasto-Electric material properties of the PMN-PT samples were characterized and evaluated within the FE model to ensure accurate corroboration. The array configuration and performance were then investigated, including array microstructure, and steering and focusing ability. The matching and backing material selection, array sensitivity and bandwidth were assessed using pulse echo responses. Imaging performance was then undertaken, employing full matrix capture and total focusing method. For performance comparison, a reference array comprising of conventional PZT5H ceramic was also investigated, following the same design criteria. The piezocrystal device provides a bandwidth of 87% and a sensitivity-bandwidth product improvement of 160% when compared to the reference PZT5H based array

CHORD: Category-level Hand-held Object Reconstruction via Shape Deformation

In daily life, humans utilize hands to manipulate objects. Modeling the shape of objects that are manipulated by the hand is essential for AI to comprehend daily tasks and to learn manipulation skills. However, previous approaches have encountered difficulties in reconstructing the precise shapes of hand-held objects, primarily owing to a deficiency in prior shape knowledge and inadequate data for training. As illustrated, given a particular type of tool, such as a mug, despite its infinite variations in shape and appearance, humans have a limited number of 'effective' modes and poses for its manipulation. This can be attributed to the fact that humans have mastered the shape prior of the 'mug' category, and can quickly establish the corresponding relations between different mug instances and the prior, such as where the rim and handle are located. In light of this, we propose a new method, CHORD, for Category-level Hand-held Object Reconstruction via shape Deformation. CHORD deforms a categorical shape prior for reconstructing the intra-class objects. To ensure accurate reconstruction, we empower CHORD with three types of awareness: appearance, shape, and interacting pose. In addition, we have constructed a new dataset, COMIC, of category-level hand-object interaction. COMIC contains a rich array of object instances, materials, hand interactions, and viewing directions. Extensive evaluation shows that CHORD outperforms state-of-the-art approaches in both quantitative and qualitative measures. Code, model, and datasets are available at https://kailinli.github.io/CHORD.Comment: To be presented at ICCV 2023, Pari

Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning

Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual reinforcement learning (RL) algorithms. Notably, employing simple observation transformations alone can yield outstanding performance without extra auxiliary representation tasks or pre-trained encoders. However, it remains unclear which attributes of DA account for its effectiveness in achieving sample-efficient visual RL. To investigate this issue and further explore the potential of DA, this work conducts comprehensive experiments to assess the impact of DA's attributes on its efficacy and provides the following insights and improvements: (1) For individual DA operations, we reveal that both ample spatial diversity and slight hardness are indispensable. Building on this finding, we introduce Random PadResize (Rand PR), a new DA operation that offers abundant spatial diversity with minimal hardness. (2) For multi-type DA fusion schemes, the increased DA hardness and unstable data distribution result in the current fusion schemes being unable to achieve higher sample efficiency than their corresponding individual operations. Taking the non-stationary nature of RL into account, we propose a RL-tailored multi-type DA fusion scheme called Cycling Augmentation (CycAug), which performs periodic cycles of different DA operations to increase type diversity while maintaining data distribution consistency. Extensive evaluations on the DeepMind Control suite and CARLA driving simulator demonstrate that our methods achieve superior sample efficiency compared with the prior state-of-the-art methods.Comment: NeurIPS 2023 poste

Broadband 1-3 piezoelectric composite transducer design using Sierpinski Gasket fractal geometry

Wider operational bandwidth is an important requirement of an ultrasound transducer across many applications. In nature, it can be observed that several hearing organs possess a broad operating bandwidth by having a varying length scales structure. Moreover, conventional 1-3 piezoelectric composite transducers have been widely recognized for their wider bandwidth over their piezoelectric ceramic counterparts. In this paper, a novel 1-3 piezoelectric composite design using a fractal geometry, known as the Sierpinski Gasket (SG), is proposed in order to explore the potential of further extending the operational bandwidth and sensitivity of the transducer. Two equivalent 1-3 piezocomposite designs are compared to this end, one with a conventional periodic parallelepiped shaped pillar structure and one with the SG fractal geometry, both theoretically, using a finite element (FE) analysis package, and experimentally. The transmit voltage response and open circuit voltage response are used to illustrate bandwidth improvement from the fractal composite design. Following the simulation results, a 580 kHz single element transducer, utilizing the proposed SG fractal microstructure, is fabricated using a pillar placement methodology. The performance of the prototyped device is characterized and compared with a conventional 1-3 composite design, as well as with a commercial ultrasound transducer. In the one-way transmission mode, a bandwidth improvement of 27.2 % and sensitivity enhancement of 3.8 dB can be found with the SG fractal design compared to an equivalent conventional composite design and up 105.1 % bandwidth improvement when compared to the commercial transducer. In the one-way reception mode, the bandwidth improvement for the SG fractal design is 2.5 % and 32.9 % when compared to the conventional and commercial transducers, respectively

Absence of topological Hall effect in Fex_xRh100−x_{100-x} epitaxial films: revisiting their phase diagram

A series of Fe$_x$Rh$_{100-x}$ ($30 \leq x \leq 57$) films were epitaxially grown using magnetron sputtering, and were systematically studied by magnetization-, electrical resistivity-, and Hall resistivity measurements. After optimizing the growth conditions, phase-pure Fe$_{x}$Rh$_{100-x}$ films were obtained, and their magnetic phase diagram was revisited. The ferromagnetic (FM) to antiferromagnetic (AFM) transition is limited at narrow Fe-contents with $48 \leq x \leq 54$ in the bulk Fe$_x$Rh$_{100-x}$ alloys. By contrast, the FM-AFM transition in the Fe$_x$Rh$_{100-x}$ films is extended to cover a much wider $x$ range between 33 % and 53 %, whose critical temperature slightly decreases as increasing the Fe-content. The resistivity jump and magnetization drop at the FM-AFM transition are much more significant in the Fe$_x$Rh$_{100-x}$ films with $\sim$50 % Fe-content than in the Fe-deficient films, the latter have a large amount of paramagnetic phase. The magnetoresistivity (MR) is rather weak and positive in the AFM state, while it becomes negative when the FM phase shows up, and a giant MR appears in the mixed FM- and AFM states. The Hall resistivity is dominated by the ordinary Hall effect in the AFM state, while in the mixed state or high-temperature FM state, the anomalous Hall effect takes over. The absence of topological Hall resistivity in Fe$_{x}$Rh$_{100-x}$ films with various Fe-contents implies that the previously observed topological Hall effect is most likely extrinsic. We propose that the anomalous Hall effect caused by the FM iron moments at the interfaces nicely explains the hump-like anomaly in the Hall resistivity. Our systematic investigations may offer valuable insights into the spintronics based on iron-rhodium alloys.Comment: 9 pages, 10 figures; accepted by Phys. Rev.