Electrolysis-based Parylene Balloon Actuators for Movable Neural Probes

In order to track a specific neuron and keep good sampling neural signals during chronic implantation, the neural probes are highly desired to have moving capability. This paper presents a novel electrolysis-based parylene balloon actuator fabricated with MEMS technology. The actuator is integrated with silicon probe to make it movable. A new fabrication technology has been developed to build a parylene balloon structure with silicon spring structure, electrolysis electrodes and electrolyte inside. By applying little current to electrolysis electrodes, high pressure is generated inside the parylene balloon by electrolysis. The spring structure is stretched with the parylene balloon expansion. Therefore the neural probe is moved by the actuation. The electrolysis actuator can generate large stain and pressure, requires modest electrical power and produces minimal heat. Due to the large volume expansion obtained via electrolysis, the small actuator can create a large force. The new electrolysis actuators for movable neural probes have been fabricated and validated

Electrolysis-based diaphragm actuators

This work presents a new electrolysis-based microelectromechanical systems (MEMS) diaphragm actuator. Electrolysis is a technique for converting electrical energy to pneumatic energy. Theoretically electrolysis can achieve a strain of 136 000% and is capable of generating a pressure above 200 MPa. Electrolysis actuators require modest electrical power and produce minimal heat. Due to the large volume expansion obtained via electrolysis, small actuators can create a large force. Up to 100 µm of movement was achieved by a 3 mm diaphragm. The actuator operates at room temperature and has a latching and reversing capability

Integrated parylene-cabled silicon probes for neural prosthetics

Recent advances in the field of neural prosthetics have demonstrated the thought control of a computer cursor. This capability relies primarily on electrode array surgically implanted into the brain as an acquisition source of neural activity. Various technologies have been developed for signal extraction; however most suffer from either fragile electrode shanks and bulky cables or inefficient use of surgical site areas. Here we present a design and initial testing results from high electrode density, silicon based arrays system with an integrated parylene cable. The greatly reduced flexible rigidity of the parylene cable is believed to relief possible mechanical damages due to relative motion between a brain and its skull

DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking

Recent multi-camera 3D object detectors usually leverage temporal information to construct multi-view stereo that alleviates the ill-posed depth estimation. However, they typically assume all the objects are static and directly aggregate features across frames. This work begins with a theoretical and empirical analysis to reveal that ignoring the motion of moving objects can result in serious localization bias. Therefore, we propose to model Dynamic Objects in RecurrenT (DORT) to tackle this problem. In contrast to previous global Bird-Eye-View (BEV) methods, DORT extracts object-wise local volumes for motion estimation that also alleviates the heavy computational burden. By iteratively refining the estimated object motion and location, the preceding features can be precisely aggregated to the current frame to mitigate the aforementioned adverse effects. The simple framework has two significant appealing properties. It is flexible and practical that can be plugged into most camera-based 3D object detectors. As there are predictions of object motion in the loop, it can easily track objects across frames according to their nearest center distances. Without bells and whistles, DORT outperforms all the previous methods on the nuScenes detection and tracking benchmarks with 62.5\% NDS and 57.6\% AMOTA, respectively. The source code will be released

Accurate Single Stage Detector Using Recurrent Rolling Convolution

Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement. Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive when evaluated in benchmarks consider mAP for high IoU thresholds. In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation. We achieved this by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are "deep in context". We evaluated our method in the challenging KITTI dataset which measures methods under IoU threshold of 0.7. We showed that with RRC, a single reduced VGG-16 based model already significantly outperformed all the previously published results. At the time this paper was written our models ranked the first in KITTI car detection (the hard level), the first in cyclist detection and the second in pedestrian detection. These results were not reached by the previous single stage methods. The code is publicly available.Comment: CVPR 201

Ion liquid chromatography on-a-chip with beads-packed parylene column

A parylene-MEMS ion-exchange Liquid Chromatography (LC) chip is presented here. The chip is integrated with microfluidic I/O ports, a separation column, frits/filters, and a conductivity detector. The column is packed with conventional LC stationary phase support materials, i.e. micro-beads with surface functional groups. To withstand high pressure normally encountered in high performance liquid chromatography (HPLC), a self-aligned, channel-anchoring technique is developed to increase the pressure rating of the parylene microfluidic devices from 30 to at least 800psi. On-chip injection, separation and detection of anions in water, with ~25 ppm concentration, have been successfully demonstrated. To our knowledge, this is the first demonstration of microbeads-packed column ion liquid Chromatography (LC) on a chip

PointLLM: Empowering Large Language Models to Understand Point Clouds

The unprecedented advancements in Large Language Models (LLMs) have created a profound impact on natural language processing but are yet to fully embrace the realm of 3D understanding. This paper introduces PointLLM, a preliminary effort to fill this gap, thereby enabling LLMs to understand point clouds and offering a new avenue beyond 2D visual data. PointLLM processes colored object point clouds with human instructions and generates contextually appropriate responses, illustrating its grasp of point clouds and common sense. Specifically, it leverages a point cloud encoder with a powerful LLM to effectively fuse geometric, appearance, and linguistic information. We collect a novel dataset comprising 660K simple and 70K complex point-text instruction pairs to enable a two-stage training strategy: initially aligning latent spaces and subsequently instruction-tuning the unified model. To rigorously evaluate our model's perceptual abilities and its generalization capabilities, we establish two benchmarks: Generative 3D Object Classification and 3D Object Captioning, assessed through three different methods, including human evaluation, GPT-4/ChatGPT evaluation, and traditional metrics. Experiment results show that PointLLM demonstrates superior performance over existing 2D baselines. Remarkably, in human-evaluated object captioning tasks, PointLLM outperforms human annotators in over 50% of the samples. Codes, datasets, and benchmarks are available at https://github.com/OpenRobotLab/PointLLM .Comment: 19 pages. Empowering large language models with 3D point cloud understanding, accompanied by a novel dataset and carefully designed benchmarks. Project page: https://runsenxu.com/projects/PointLL