An Inductive Sensor for Real Time Measurement of Plantar Normal and Shear Forces Distribution

Goal: The objective of this article is to demonstrate a multiplexed inductive force sensor for simultaneously measuring normal force and shear forces on a foot. Methods: The sensor measures the normal force and shear forces by monitoring the inductance changes of three planar sensing coils. Resonance frequency division multiplexing was applied to signals from the multiple sensing coils, making it feasible to simultaneously measure the three forces (normal force, shear forces in x and y axis) on a foot using only one set of measurement electronics with high sensitivity and resolution. Results: The testing results of the prototype sensor have shown that the sensor is capable of measuring normal force ranging from 0 to 800 N and shear forces ranging from 0 to 130 N in real time. Conclusion: With its high resolution, high sensitivity and the capability of monitoring forces at different positions of a foot simultaneously, this sensor can be potentially used for real time measurement of plantar normal force and shear forces distribution on diabetes patient’s foot. Significance: Real time monitoring of the normal force and shear forces on diabetes patient’s foot can provide useful information for physicians and diabetes patients to take actions in preventing foot ulceration

Two Essays on Corporate Default Risk

Ph.DDOCTOR OF PHILOSOPH

Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception

Existing research usually utilizes side information such as social network or item attributes to improve the performance of collaborative filtering-based recommender systems. In this paper, the knowledge graph with user perception is used to acquire the source of side information. We proposed KGUPN to address the limitations of existing embedding-based and path-based knowledge graph-aware recommendation methods, an end-to-end framework that integrates knowledge graph and user awareness into scientific and technological news recommendation systems. KGUPN contains three main layers, which are the propagation representation layer, the contextual information layer and collaborative relation layer. The propagation representation layer improves the representation of an entity by recursively propagating embeddings from its neighbors (which can be users, news, or relationships) in the knowledge graph. The contextual information layer improves the representation of entities by encoding the behavioral information of entities appearing in the news. The collaborative relation layer complements the relationship between entities in the news knowledge graph. Experimental results on real-world datasets show that KGUPN significantly outperforms state-of-the-art baselines in scientific and technological news recommendation

Expressions of Hippocampal Mineralocorticoid Receptor (MR) and Glucocorticoid Receptor (GR) in the Single-Prolonged Stress-Rats

Post-traumatic stress disorder (PTSD) is a stress-related mental disorder caused by traumatic experience. Single-prolonged stress (SPS) is one of the animal models proposed for PTSD. Rats exposed to SPS showed enhanced inhibition of the hypothalamo-pituitary-adrenal (HPA) axis, which has been reliably reproduced in patients with PTSD. Mineralocorticoid receptor (MR) and glucocorticoid receptor (GR) in the hippocampus regulate HPA axis by glucocorticoid negative feedback. Abnormalities in negative feedback are found in PTSD, suggesting that GR and MR might be involved in the pathophysiology of these disorders

Capacity-CRB Tradeoff in OFDM Integrated Sensing and Communication Systems

Integrated sensing and communication (ISAC) has emerged as a key technology for future communication systems. In this paper, we provide a general framework to reveal the fundamental tradeoff between sensing and communication in OFDM systems, where a unified ISAC waveform is exploited to perform both tasks. In particular, we define the Capacity-Bayesian Cramer Rao Bound (BCRB) region in the asymptotically case when the number of subcarriers is large. Specifically, we show that the asymptotically optimal input distribution that achieves the Pareto boundary point of the Capacity-BCRB region is Gaussian and the entire Pareto boundary can be obtained by solving a convex power allocation problem. Moreover, we characterize the structure of the sensing-optimal power allocation in the asymptotically case. Finally, numerical simulations are conducted to verify the theoretical analysis and provide useful insights

Can Transformers Learn Optimal Filtering for Unknown Systems?

Transformers have demonstrated remarkable success in natural language processing; however, their potential remains mostly unexplored for problems arising in dynamical systems. In this work, we investigate the optimal output estimation problem using transformers, which generate output predictions using all the past ones. We train the transformer using various systems drawn from a prior distribution and then evaluate its performance on previously unseen systems from the same distribution. As a result, the obtained transformer acts like a prediction algorithm that learns in-context and quickly adapts to and predicts well for different systems - thus we call it meta-output-predictor (MOP). MOP matches the performance of the optimal output estimator, based on Kalman filter, for most linear dynamical systems even though it does not have access to a model. We observe via extensive numerical experiments that MOP also performs well in challenging scenarios with non-i.i.d. noise, time-varying dynamics, and nonlinear dynamics like a quadrotor system with unknown parameters. To further support this observation, in the second part of the paper, we provide statistical guarantees on the performance of MOP and quantify the required amount of training to achieve a desired excess risk during test-time. Finally, we point out some limitations of MOP by identifying two classes of problems MOP fails to perform well, highlighting the need for caution when using transformers for control and estimation