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Background: Blood pressure (BP) is an essential indicator for human health and is known to be greatly influenced by lifestyle factors, like activity and sleep factors. However, the degree of impact of each lifestyle factor on BP is unknown and may vary between individuals. Our goal is to investigate the relationships between BP and lifestyle factors and provide personalized and precise recommendations to improve BP, as opposed to the current practice of general lifestyle recommendations. Method: Our proposed system consists of automated data collection using home BP monitors and wearable activity trackers and feature engineering techniques to address time-series data and enhance interpretability. We propose Random Forest with Shapley-Value-based Feature Selection to offer personalized BP modeling and top lifestyle factor identification, and subsequent generation of precise recommendations based on the top factors. Result: In collaboration with UC San Diego Health and Altman Clinical and Translational Research Institute, we performed a clinical study, applying our system to 25 patients with elevated BP or stage I hypertension for three consecutive months. Our study results validate our system’s ability to provide accurate personalized BP models and identify the top features which can vary greatly between individuals. We also validate the effectiveness of personalized recommendations in a randomized controlled experiment. After receiving recommendations, the subjects in the experimental group decreased their BPs by 3.8 and 2.3 for systolic and diastolic BP, compared to the decrease of 0.3 and 0.9 for the subjects without recommendations. Conclusion: The study demonstrates the potential of using wearables and machine learning to develop personalized models and precise lifestyle recommendations to improve BP.

We present a methodology for enhancing the throughput of semantic video segmentation tasks on an embedded cluster containing multiple embedded processing elements (ePEs). The methodology embraces a scalable master-slave hierarchy and features a global and local key management scheme for allocating video frames to different ePEs. The master ePE divides each video frame into frame regions, and dynamically distributes different regions to different slave ePEs. Each slave ePE executes either a segmentation path or a flow path: the former is highly accurate but slower, while the latter is faster but less accurate. A lightweight decision network is employed to determine the execution path for each slave ePE. We propose a global and local key management scheme to facilitate the execution of the embedded cluster, such that the average processing latency of each frame is significantly reduced. We evaluate the performance of our methodology on a real embedded cluster in terms of accuracy and frame rate, and validate its effectiveness and efficiency for various ePE configurations. We further provide a detailed latency analysis for different configurations of ePEs.

Blood pressure (BP) is the most important indicator of cardiovascular diseases. Traditional cuff-based methods for measuring BP require manual intervention and time. These methods may lead to inaccurate measurements and are not practical for continuous BP monitoring, which is crucial for detecting abnormal BP fluctuations. In this study, we propose a personalized machine learning model to estimate BP using his/her previous BPs and the photoplethysmogram (PPG) signal, the simplest and most popular tool for non-invasive diagnosis. To best utilize the information contained in the PPG signal, we propose to apply wavelet decomposition to extract features from the PPG signal. The arterial blood pressure (ABP) time series is processed with an exponentially weighted moving average (EWMA) and a peak detection technique to derive the SBP, DBP, and their corresponding trends. Finally, a random forest model is used to construct a predictive model based on these features. The MIMIC dataset is used for analysis and comparison with other BP estimation methods. Our experimental results demonstrate the proposed approach has smaller estimation error than existing methods, with mean average errors (MAE) for SBP and DBP equal to 3.43 and 1.73, respectively.

Blood pressure (BP) is one of the most important indicator of human health. In this paper, we investigate the relationship between BP and health behavior (e.g. sleep and exercise). Using the data collected from off-the-shelf wearable devices and wireless home BP monitors, we propose a data driven personalized model to predict daily BP level and provide actionable insight into health behavior and daily BP. In the proposed machine learning model using Random Forest (RF), trend and periodicity features of BP time-series are extracted to improve prediction. To further enhance the performance of the prediction model, we propose RF with Feature Selection (RFFS), which performs RF-based feature selection to filter out unnecessary features. Our experimental results demonstrate that the proposed approach is robust to different individuals and has smaller prediction error than existing methods. We also validate the effectiveness of personalized recommendation of health behavior generated by RFFS model.

This paper shows the viability of Solar-powered Road Side Units (SRSU), consisting of small cell base stations and Mobile Edge Computing (MEC) servers, and powered solely by solar panels with battery, to provide connected vehicles with a low- latency, easy-to-deploy and energy-efficient communication and edge computing infrastructure. However, SRSU may entail a high risk of power deficiency, leading to severe Quality of Service (QoS) loss due to spatial and temporal fluctuation of solar power generation. Meanwhile, the data traffic demand also varies with space and time. The mismatch between solar power generation and SRSU power consumption makes optimal use of solar power challenging. In this paper, we model the above problem with three sub-problems, the SRSU power consumption minimization problem, the temporal energy balancing problem and spatial energy balancing problem. Three algorithms are proposed to solve the above sub-problems, and they together provide a complete joint battery charging and user association control algorithm to minimize the QoS loss under delay constraint of the computing tasks. Results with a simulated urban environment using actual solar irradiance and vehicular traffic data demonstrates that the proposed solution reduces the QoS loss significantly compared to greedy approaches.

The use of solar photovoltaic (PV) power is a promising solution to reduce grid power consumption and carbon dioxide emissions. However, the benefit of utilizing solar PV power is limited by its highly intermittent and unreliable nature. The non-stationary and non-linear characteristic of solar irradiance makes solar PV difficult to predict by traditional time series and artificial intelligence (AI) approaches. To address the above challenges, we propose a novel technique integrating stationary wavelet transform (SWT) and random forest models. Instead of conventional decompose-and-reconstruct process in SWT, we only apply the wavelet decomposition to extract the information from raw data with better time-frequency resolutions. We also propose a bias-compensation technique to minimize the prediction error. Our experimental results using sensors data from the on-campus microgrid demonstrate the proposed approach is robust to different forecast time horizons and has smaller prediction error.

The use of renewable energy (RE) sources is a promising solution to reduce grid power consumption and carbon dioxide emissions (CO2e) of cellular networks. However, the benefit of utilizing RE is limited by its highly intermittent and unreliable nature leading to mismatch between generation and base station (BS) needs, resulting in low savings in grid power. To address the above challenges, we propose a novel base station time resource allocation technique using data storage at user equipments (UEs) to optimally utilize renewable energy to reduce grid power consumption. The proposed approach transforms the surplus RE (in excess of the BS power requirements) to excess data delivered to the users and stored in UE data storages, to draw from in deficit periods (when RE generation is lesser than BS requirements) to reduce grid power. Though the proposed approach is applicable to any application that utilizes UE data buffer, we formulate the problem for mobile video and propose a algorithm for RE aware BS resource allocation during mobile video download, as the latter will dominate wireless traffic and hence BS power consumption. Our experimental results using sample solar and BS utilization traces demonstrate the ability of the proposed approach to reduce grid power consumption by increasing solar power utilization while satisfying user QoS requirements.

A 206 ∼ 220 GHz varactor-free voltage-controlled oscillator (VCO) is proposed to improve the output power and tuning range. The topology of the VCO is push-push crosscoupled pair. By using body-bias technique, the parasitic capacitor of the cross-coupled pair can be adjusted, and the varactors in the conventional VCO design can be removed. The proposed VCO was designed and implemented in TSMC 65-nm CMOS technology with a chip size of 0.25 × 0.25 mm2. The tuning range is 6.6% (from 206.2 to 220 GHz). The measured phase noise is −66 dBc/Hz at 1 MHz offset frequency. The output power is 1.3 dBm at 216 GHz with 54-mW dc power consumption and its maximum dc-to-RF efficiency is 2.1%. To the best of our knowledge, this varactor-free VCO achieves the highest dc-to-RF efficiency among published CMOS VCOs around 200 GHz.

A heterogeneous network (HetNet), which consists of macro cells and small cells, is an effective tool to improve the system spectral efficiency by exploiting the benefit of spatial reuse. However, the benefit of HetNet is limited by the asymmetry of transmit power between macro base stations (BSs) and pico BSs. Traditional solutions lead to a tradeoff between received signal strength (RSS) and load balancing. To tackle this problem, we propose a novel mechanism with joint macro BS transmit power control and load-aware user association. As such, we can realize traffic offloading from the macro cell to pico cells in a two-tier HetNet and mitigate inter-cell interference (ICI) in the network. We formulate this problem as a nonlinear integer programming problem, and prove that it is NP-hard. We then propose a polynomial-time heuristic. The simulation results show that our algorithm is indeed an efficient mechanism with better network performance in terms of average throughput and fairness.

A 53-to-84 GHz wideband power amplifier (PA) is implemented in 65-nm GP CMOS technology. This PA exhibits a measured small signal gain of 29.1 ± 1.5 dB from 53 to 84 GHz and a P sat of 10.8 dBm, P 1dB of 7.2 dBm, power added efficiency (PAE) of 9.7% at 80 GHz at drain voltage of 1.2 V. The DC consumption is 123 mW.