Secure Massive MIMO Communication with Low-resolution DACs

In this paper, we investigate secure transmission in a massive multiple-input multiple-output (MIMO) system adopting low-resolution digital-to-analog converters (DACs). Artificial noise (AN) is deliberately transmitted simultaneously with the confidential signals to degrade the eavesdropper's channel quality. By applying the Bussgang theorem, a DAC quantization model is developed which facilitates the analysis of the asymptotic achievable secrecy rate. Interestingly, for a fixed power allocation factor $\phi$, low-resolution DACs typically result in a secrecy rate loss, but in certain cases they provide superior performance, e.g., at low signal-to-noise ratio (SNR). Specifically, we derive a closed-form SNR threshold which determines whether low-resolution or high-resolution DACs are preferable for improving the secrecy rate. Furthermore, a closed-form expression for the optimal $\phi$ is derived. With AN generated in the null-space of the user channel and the optimal $\phi$, low-resolution DACs inevitably cause secrecy rate loss. On the other hand, for random AN with the optimal $\phi$, the secrecy rate is hardly affected by the DAC resolution because the negative impact of the quantization noise can be compensated for by reducing the AN power. All the derived analytical results are verified by numerical simulations.Comment: 14 pages, 10 figure

Cross-Modal Health State Estimation

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geospatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.Comment: Accepted to ACM Multimedia 2018 Conference - Brave New Ideas, Seoul, Korea, ACM ISBN 978-1-4503-5665-7/18/1

Hsa-miRNA-765 as a key mediator for inhibiting growth, migration and invasion in fulvestrant-treated prostate cancer

Fulvestrant (ICI-182,780) has recently been shown to effectively suppress prostate cancer cell growth in vitro and in vivo. But it is unclear whether microRNAs play a role in regulating oncogene expression in fulvestrant-treated prostate cancer. Here, this study reports hsa-miR-765 as the first fulvestrant-driven, ERβ-regulated miRNA exhibiting significant tumor suppressor activities like fulvestrant, against prostate cancer cell growth via blockage of cell-cycle progression at the G2/M transition, and cell migration and invasion possibly via reduction of filopodia/intense stress-fiber formation. Fulvestrant was shown to upregulate hsa-miR-765 expression through recruitment of ERβ to the 5′-regulatory-region of hsa-miR-765. HMGA1, an oncogenic protein in prostate cancer, was identified as a downstream target of hsa-miR-765 and fulvestrant in cell-based experiments and a clinical study. Both the antiestrogen and the hsa-miR-765 mimic suppressed HMGA1 protein expression. In a neo-adjuvant study, levels of hsa-miR-765 were increased and HMGA1 expression was almost completely lost in prostate cancer specimens from patients treated with a single dose (250 mg) of fulvestrant 28 days before prostatectomy. These findings reveal a novel fulvestrant signaling cascade involving ERβ-mediated transcriptional upregulation of hsa-miR-765 that suppresses HMGA1 protein expression as part of the mechanism underlying the tumor suppressor action of fulvestrant in prostate cancer. © 2014 Leung et al

Crowdsensing Route Reconstruction using Portable Bluetooth Beacon-based two-way network

[[abstract]]There have been plenty of R&D efforts to achieve precise indoor positioning of users. With weak or no GPS signals, Wifi / Bluetooth and other approaches such as ultrasonic and accelerometers has been used for indoor positioning. In this demo we focus on reconstructing visitors route using the combination of Bluetooth proximity tags and custom made Bluetooth Beacons. Bluetooth Beacons are usually used to emitting simple identification information for retrieval by a mobile app, which will in turn use this information to get own position from a mobile network. Our custom Bluetooth beacons has the capability to form a two-way network between Bluetooth Beacons, making it highly portable and very easy for deployment. We'll show how to setup the system and how to identify and reconstruct route information of visitors. The information can be used to improve user experience.[[notice]]補正完

Using Smartphone Sensors for Improving Energy Expenditure Estimation.

Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. Accurate and real-time EE estimation utilizing small wearable sensors is a difficult task, primarily because the most existing schemes work offline or use heuristics. In this paper, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs, or downstairs) of a typical smartphone user. We used built-in smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately estimate EE. Using a barometer sensor, in addition to an accelerometer sensor, greatly increases the accuracy of EE estimation. Using bagged regression trees, a machine learning technique, we developed a generic regression model for EE estimation that yields upto 96% correlation with actual EE. We compare our results against the state-of-the-art calorimetry equations and consumer electronics devices (Fitbit and Nike+ FuelBand). The newly developed EE estimation algorithm demonstrated superior accuracy compared with currently available methods. The results were calibrated against COSMED K4b2 calorimeter readings