Vibration Propagation on the Skin of the Arm

Vibrotactile interfaces are an inexpensive and non-invasive way to provide performance feedback to body-machine interface users. Interfaces for the upper extremity have utilized a multi-channel approach using an array of vibration motors placed on the upper extremity. However, for successful perception of multi-channel vibrotactile feedback on the arm, we need to account for vibration propagation across the skin. If two stimuli are delivered within a small distance, mechanical propagation of vibration can lead to inaccurate perception of the distinct vibrotactile stimuli. This study sought to characterize vibration propagation across the hairy skin of the forearm. We characterized vibration propagation by measuring accelerations at various distances from a source vibration of variable intensities (100–240 Hz). Our results showed that acceleration from the source vibration was present at a distance of 4 cm at intensities \u3e150 Hz. At distances greater than 8 cm from the source, accelerations were reduced to values substantially below vibrotactile discrimination thresholds for all vibration intensities. We conclude that in future applications of vibrotactile interfaces, stimulation sites should be separated by a distance of at least 8 cm to avoid potential interference in vibration perception caused by propagating vibrations

Reaching Performance in Heathy Individuals and Stroke Survivors Improves after Practice with Vibrotactile State Feedback

Stroke causes deficits of cognition, motor, and/or somatosensory functions. These deficits degrade the capability to perform activities of daily living (ADLs). Many research investigations have focused on mitigating the motor deficits of stroke through motor rehabilitation. However, somatosensory deficits are common and may contribute importantly to impairments in the control of functional arm movement. This dissertation advances the goal of promoting functional motor recovery after stroke by investigating the use of a vibrotactile feedback (VTF) body-machine interface (BMI). The VTF BMI is intended to improve control of the contralesional arm of stroke survivors by delivering supplemental limb-state feedback to the ipsilesional arm, where somatosensory feedback remains intact. To develop and utilize a VTF BMI, we first investigated how vibrotactile stimuli delivered on the arm are perceived and discriminated. We determined that stimuli are better perceived sequentially than those delivered simultaneously. Such stimuli can propagate up to 8 cm from the delivery site, so future applications should consider adequate spacing between stimulation sites. We applied these findings to create a multi-channel VTF interface to guide the arm in the absence of vision. In healthy people, we found that short-term practice, less than 2.5 hrs, allows for small improvements in the accuracy of horizontal planar reaching. Long-term practice, about 10 hrs, engages motor learning such that the accuracy and efficiency of reaching is improved and cognitive loading of VTF-guided reaching is reduced. During practice, participants adopted a movement strategy whereby BMI feedback changed in just one channel at a time. From this observation, we sought to develop a practice paradigm that might improve stroke survivors’ learning of VTF-guided reaching without vision. We investigated the effects of practice methods (whole practice vs part practice) in stroke survivors’ capability to make VTF-guided arm movements. Stroke survivors were able to improve the accuracy of VTF-guided reaching with practice, however there was no inherent differences between practice methods. In conclusion, practice on VTF-guided 2D reaching can be used by healthy people and stroke survivors. Future studies should investigate long-term practice in stroke survivors and their capability to use VTF BMIs to improve performance of unconstrained actions, including ADLs

Temperature And Frequency Dependence Of Thermal Impedance In Dielectrically Isolated SiGe HBTs

Silicon Germanium (SiGe) heterojunction bipolar transistors (HBTs) have been designed to cater the demand of high speed circuits having applications in wireless and optical communication. Due to higher cut-off frequencies and high speed applications, self heating plays a significant role in the performance of SiGe HBTs. This thesis paper does an extensive analysis on the effect of self heating and used different DC, time domain and frequency domain methods to extract the self heating parameters thermal resistance (Rth) and thermal capacitance (Cth). The VBIC (Vertical Bipolar Inter Company) model is used for all the simulations. Simulations are performed using ICCAP (Integrated Circuit Characterization and Analysis Program) tool. Measurements are done using HP 4142 DC source module, Agilent 6000 series oscilloscope and Agilent 8753 ES s-parameter network analyzer. National Semiconductor Corporation\u27s SiGe LV (low voltage) NPN HBT with emitter dimensions 10um×0.25um from their CBC8 process is used for getting real data

Scaling Vision-and-Language Navigation With Offline RL

The study of vision-and-language navigation (VLN) has typically relied on expert trajectories, which may not always be available in real-world situations due to the significant effort required to collect them. On the other hand, existing approaches to training VLN agents that go beyond available expert data involve data augmentations or online exploration which can be tedious and risky. In contrast, it is easy to access large repositories of suboptimal offline trajectories. Inspired by research in offline reinforcement learning (ORL), we introduce a new problem setup of VLN-ORL which studies VLN using suboptimal demonstration data. We introduce a simple and effective reward-conditioned approach that can account for dataset suboptimality for training VLN agents, as well as benchmarks to evaluate progress and promote research in this area. We empirically study various noise models for characterizing dataset suboptimality among other unique challenges in VLN-ORL and instantiate it for the VLN$\circlearrowright$BERT and MTVM architectures in the R2R and RxR environments. Our experiments demonstrate that the proposed reward-conditioned approach leads to significant performance improvements, even in complex and intricate environments.Comment: Published in Transactions on Machine Learning Research (04/2024

Industrial Human Activity Prediction and Detection Using Sequential Memory Networks

Prediction of human activity and detection of subsequent actions is crucial for improving the interaction between humans and robots during collaborative operations. Deep-learning techniques are being applied to recognize human activities, including industrial applications. However, the lack of sufficient dataset in the industrial domain and complexities of some industrial activities such as screw driving, assembling small parts, and others affect the model development and testing of human activities. The InHard dataset (Industrial Human Activity Recognition Dataset) was recently published to facilitate industrial human activity recognition for better human-robot collaboration, which still lacks extended evaluation. We propose an activity recognition method using a combined convolutional neural network (CNN) and long short-term memory (LSTM) techniques to evaluate the InHard dataset and compare it with a new dataset captured in a lab environment. This method improves the success rate of activity recognition by processing temporal and spatial information. Accordingly, the accuracy of the dataset is tested using labeled lists of activities from IMU and video data. A model is trained and tested for nine low-level activity classes with approximately 400 samples per class. The test result shows 88% accuracy for IMU-based skeleton data, 77% for RGB spatial video, and 63% for RGB video-based skeleton. The result has been verified using a previously published region-based activity recognition. The proposed approach can be extended to push the cognition capability of robots in human-centric workplaces

Power-Derived Thermal Characterization of SiGe HBTs and Design of Timing Circuit using a Phase Locked-Loop

Silicon Germanium (SiGe) hetero-junction bipolar transistors (HBTs) have been designed to cater the demand of high speed circuits having speed applications in wireless, optical communication and space electronics. Due to higher cut-off frequencies and high speed applications, self heating plays a significant role in the performance of SiGe HBTs. This dissertation paper does an extensive analysis on the effect of self heating and used DC measurement methods to extract the self heating parameter, thermal resistance (Rth). A non-linear thermal model is proposed to take into account the effects of the non-linearity in device parameters with increase in device temperature. The HICUM (HIgh CUrrent Model) model is used for all the simulations. Simulations are performed using IC-CAP (Integrated Circuit Characterization and Analysis Program) tool. The linear thermal model attached at the thermal node of the HICUM model was replaced with a Current Controlled Voltage Source (CCVS) in order to implement the non-linear thermal model for simulations. Measurements are done using HP 4142B modular DC source/module connected to the computer via Hewlett-Packard Interface Bus (HPIB). Texas Instruments, Incorporation’s SiGe LV (Low Voltage) HBTs with varying emitter lengths are used for measurements. The second project of this research is to design a timing circuit with an output jitter of the order of ~ 5 ps. The timing circuit was designed using a Phase Locked Loop (PLL). The basic concept of this system is based on the similar system designed at Stanford Linear Accelerator (SLAC). The input reference signal is provided by the Timing, Trigger and Control (TTC) Systems for the Large Hadron Collider (LHC) at Conseil Europeen por la Recherche Nucleaire (CERN). The frequency of the reference signal is ~ 40 MHz. Jitter content of this signal prevents it to be used as a timing signal for other electronics. In order to re-use the basic design from SLAC, this reference signal is up-converted to ~ 480 MHz using a two step up-conversion process. This up-converted signal is then stabilized using a PLL. After stabilization (jitter reduction) the 480 MHz signal is down-converted using a divider with division factor of 12. The jitter in the final output signal is of the order of ~ 2 ps

Accelerating Coupled Cluster Calculations with Nonlinear Dynamics and Shallow Machine Learning

The dynamics associated with the time series of the iteration scheme of coupled cluster theory has been analysed. The phase space analysis indicates the presence of a few significant cluster amplitudes, mostly involving valence excitations, which dictate the dynamics, while all other amplitudes are enslaved. Starting with a few initial iterations to establish the inter-relationship among the cluster amplitudes, a supervised Machine Learning scheme with polynomial Kernel Ridge Regression model has been employed to express each of the enslaved variables uniquely in terms of the master amplitudes. The subsequent coupled cluster iterations are restricted to a reduced dimension only to determine those significant excitations, and the enslaved variables are determined through the already established functional mapping. We will show that our scheme leads to tremendous reduction in computational time without sacrificing the accuracy.Comment: 8 pages, 8 figures (including 1 Tikz Cartoon