Multicell Coordinated Beamforming with Rate Outage Constraint--Part I: Complexity Analysis

This paper studies the coordinated beamforming (CoBF) design in the multiple-input single-output interference channel, assuming only channel distribution information given a priori at the transmitters. The CoBF design is formulated as an optimization problem that maximizes a predefined system utility, e.g., the weighted sum rate or the weighted max-min-fairness (MMF) rate, subject to constraints on the individual probability of rate outage and power budget. While the problem is non-convex and appears difficult to handle due to the intricate outage probability constraints, so far it is still unknown if this outage constrained problem is computationally tractable. To answer this, we conduct computational complexity analysis of the outage constrained CoBF problem. Specifically, we show that the outage constrained CoBF problem with the weighted sum rate utility is intrinsically difficult, i.e., NP-hard. Moreover, the outage constrained CoBF problem with the weighted MMF rate utility is also NP-hard except the case when all the transmitters are equipped with single antenna. The presented analysis results confirm that efficient approximation methods are indispensable to the outage constrained CoBF problem.Comment: submitted to IEEE Transactions on Signal Processin

Precipitation, Circulation, and Cloud Variability Over the Past Two Decades

To better understand the variability of precipitation, circulation, and cloud, we examine the precipitation, vertical velocity, total cloud fraction, condensed water path, and ice water path from observations and 13 Coupled Model Intercomparison Project 5 (CMIP5) models over 1988–2008. All variables are averaged over wet areas and dry areas to investigate temporal variations of different variables over these regions. We found that all models demonstrate similar temporal variations of precipitation as the observational data from the Global Precipitation Climatology Project, with positive trend over wet areas (6.22 ± 3.75 mm/mon/decade) and negative trend over dry areas (−0.77 ± 0.54 mm/mon/decade). Positive trends of vertical velocity, total cloud fraction, condensed water path, and ice water path are also found in the observations and models over the wet areas. Observations also demonstrate decreasing trends of vertical velocity, total clouds, condensed water path, and ice water path over the dry areas, which can be simulated by most models with a few exceptions. The qualitatively consistent trends in these variables (i.e., vertical velocity, cloud, liquid, and ice water contents) as revealed from the observations and CMIPS models provide a clearer picture of the dynamics and physics behind the temporal variations of precipitation over different areas

Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie

Multi-modality action recognition based on dual feature shift in vehicle cabin monitoring

Driver Action Recognition (DAR) is crucial in vehicle cabin monitoring systems. In real-world applications, it is common for vehicle cabins to be equipped with cameras featuring different modalities. However, multi-modality fusion strategies for the DAR task within car cabins have rarely been studied. In this paper, we propose a novel yet efficient multi-modality driver action recognition method based on dual feature shift, named DFS. DFS first integrates complementary features across modalities by performing modality feature interaction. Meanwhile, DFS achieves the neighbour feature propagation within single modalities, by feature shifting among temporal frames. To learn common patterns and improve model efficiency, DFS shares feature extracting stages among multiple modalities. Extensive experiments have been carried out to verify the effectiveness of the proposed DFS model on the Drive\&Act dataset. The results demonstrate that DFS achieves good performance and improves the efficiency of multi-modality driver action recognition

Featureless blood pressure estimation based on photoplethysmography signal using CNN and BiLSTM for IoT devices

Continuous blood pressure (BP) acquisition is critical to health monitoring of an individual. Photoplethysmography (PPG) is one of the most popular technologies in the last decade used for measuring blood pressure noninvasively. Several approaches have been carried out in various ways to utilize features extracted from PPG. In this study, we develop a continuous systolic and diastolic blood pressure (SBP and DBP) estimation mechanism without the need for any feature engineering. The raw PPG signal only got preprocessed before being fed to our model which mainly consists of one-dimensional convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) network. We evaluate the resulting SBP and DBP value by the root-mean-squared error (RMSE) and mean absolute error (MAE). This study addresses the effectiveness of the model by outperforming the previous feature engineering-based methods. We achieve RMSE of 11.503 and 6.525 for SBP and DBP, respectively, and MAE of 7.849 and 4.418 for SBP and DBP, respectively. The proposed method is expected to substantially enhance the current efficiency of healthcare IoT (Internet of Things) devices in BP monitoring using PPG signals only

Continuous blood pressure estimation using exclusively photopletysmography by LSTM-based signal-to-signal translation

Monitoring continuous BP signal is an important issue, because blood pressure (BP) varies over days, minutes, or even seconds for short-term cases. Most of photoplethysmography (PPG)-based BP estimation methods are susceptible to noise and only provides systolic blood pressure (SBP) and diastolic blood pressure (DBP) prediction. Here, instead of estimating a discrete value, we focus on different perspectives to estimate the whole waveform of BP. We propose a novel deep learning model to learn how to perform signal-to-signal translation from PPG to arterial blood pressure (ABP). Furthermore, using a raw PPG signal only as the input, the output of the proposed model is a continuous ABP signal. Based on the translated ABP signal, we extract the SBP and DBP values accordingly to ease the comparative evaluation. Our prediction results achieve average absolute error under 5 mmHg, with 70% confidence for SBP and 95% confidence for DBP without complex feature engineering. These results fulfill the standard from Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) with grade A. From the results, we believe that our model is applicable and potentially boosts the accuracy of an effective signal-to-signal continuous blood pressure estimation

Efficient (k, n) : threshold secret sharing method with cheater prevention for QR code application

To protect secret message, secret sharing technique divides it into n shares and distributes them to n involved participants. However, it is hardly to prevent a dishonest participant to cheat other by providing a fake share. To overcome this weakness, this paper presents an efficient (k, n)-threshold secret sharing approach with the functionality of cheater identification using meaningful QR codes. The secret message would be split into k pieces, and used as the coefficients of polynomial function to generate n shares. These shares would be concealed into cover QR codes based on its fault tolerance to generate meaningful QR code shares. The meaningful QR code shares are helpful to reduce the curiosity of unrelated persons when transmitted in public channel. The legitimacy of QR code share would be verified before secret reconstruction to prevent cheater in secret revealing procedure. Some experiments were done to evaluate the performance of the proposed scheme. The experimental results show that the proposed scheme is efficient, highly secure and highly robust, and it also achieves a higher embedding capacity compared to previous methods