Xiangfang Li

Power control for device-to-device communications as an underlay to cellular system

In this work, we consider Quality of Service (QoS) guarantee for Device to Device (D2D) users co-existing with a cellular system where the D2D communication links are sharing radio spectrum resources with macrocell users in the downlink. Despite the lofty advantages associated with D2D communications, one major concern is the resulting interference from the D2D users, which should not infringe on the service quality requirements of the User Equipments (UEs). In this work, we investigate the scenario in which D2D communications operates simultaneously with downlink transmissions from the Evolved Node B (eNB). Power control problem for the D2D users is formulated in order to optimize the energy efficiency of the eNB users as well as to ensure that QoS of D2D devices and UEs does not fall below the acceptable target. The feasible conditions of the power control problem are derived and then the centralized and the distributed solutions are obtained. We further suggest jointly designed dynamic power control and channel re-allocation algorithm that will guarantee the priority of the UEs. Effectiveness of the proposed scheme is demonstrated through extensive simulations.

Characterization of Drug Efficacy Regions Based on Dosage and Frequency Schedules

This paper proposes a framework to study the drug effect at the molecular level in order to address the following question of current interest in the drug community: Given a fixed total delivered drug, which is better, frequent small or infrequent large drug dosages? A hybrid system model is proposed to link the drugs pharmacokinetic and pharmacodynamic information, and allows the drug effects for different dosages and treatment schedules to be compared. A hybrid model facilitates the modeling of continuous quantitative changes that leads to discrete transitions. An optimal dosage-frequency regimen and the necessary and sufficient conditions for the drug to be effective are obtained analytically when the drug is designed to control a target gene. Then, we extend the analysis to the case where the target gene is part of a genetic regulatory network. A crucial observation is that there exists a “sweet spot,” defined as the “drug efficacy region (DER)” in this paper, for certain dosage and frequency arrangements given the total delivered drug. This paper quantifies the therapeutic benefits of dosage regimen lying within the DER. Simulations are performed using MATLAB/SIMULINK to validate the analytical results.

Community based sensing: A test bed for environment air quality monitoring using smartphone paired sensors

Awareness of our surrounding environment is very important. Traditional stationary air quality and pollution monitoring systems which installed in dedicated locations are usually bulky and expensive, and they may not be able to measure at locations of our interest. In this study, we propose a novel framework of an environment air quality monitoring system using a community-based approach. Leveraging on the high penetration of smartphones and low cost and small form factor of certain sensors that can be carried on a key chain with a Bluetooth module, critical measurements such as air quality can be measured by each sensor carried by a member of a community, and be sent to that persons smartphones, and eventually uploaded to a web portal using a corresponding app. Then the aggregated and processed data at the server side can be visualized and shared among the community members and publish through social networks. In this project, we have designed the architecture of the proposed community sensing system, and implemented the system using commercial off-the-shelf (COTS) Sensordrone paired with Android© smartphones. Our system measure temperature, humidity, pressure, carbon monoxide, and battery charge level in real-time and it will provide location based services to users. Extra sensors such as ozone and radiation sensors can be added as well.

A Systems Biology Approach in Therapeutic Response Study for Different Dosing Regimens—a Modeling Study of Drug Effects on Tumor Growth using Hybrid Systems

Motivated by the frustration of translation of research advances in the molecular and cellular biology of cancer into treatment, this study calls for cross-disciplinary efforts and proposes a methodology of incorporating drug pharmacology information into drug therapeutic response modeling using a computational systems biology approach. The objectives are two fold. The first one is to involve effective mathematical modeling in the drug development stage to incorporate preclinical and clinical data in order to decrease costs of drug development and increase pipeline productivity, since it is extremely expensive and difficult to get the optimal compromise of dosage and schedule through empirical testing. The second objective is to provide valuable suggestions to adjust individual drug dosing regimens to improve therapeutic effects considering most anticancer agents have wide inter-individual pharmacokinetic variability and a narrow therapeutic index. A dynamic hybrid systems model is proposed to study drug antitumor effect from the perspective of tumor growth dynamics, specifically the dosing and schedule of the periodic drug intake, and a drugs pharmacokinetics and pharmacodynamics information are linked together in the proposed model using a state-space approach. It is proved analytically that there exists an optimal drug dosage and interval administration point, and demonstrated through simulation study.

Dynamical modeling of drug effect using hybrid systems

Drug discovery today is a complex, expensive, and time-consuming process with high attrition rate. A more systematic approach is needed to combine innovative approaches in order to lead to more effective and efficient drug development. This article provides systematic mathematical analysis and dynamical modeling of drug effect under gene regulatory network contexts. A hybrid systems model, which merges together discrete and continuous dynamics into a single dynamical model, is proposed to study dynamics of the underlying regulatory network under drug perturbations. The major goal is to understand how the system changes when perturbed by drugs and give suggestions for better therapeutic interventions. A realistic periodic drug intake scenario is considered, drug pharmacokinetics and pharmacodynamics information being taken into account in the proposed hybrid systems model. Simulations are performed using MATLAB/SIMULINK to corroborate the analytical results.

Multisensor change detection on the basis of big time‐series data and Dempster‐Shafer theory

With the proliferation of the Internet of Things, numerous sensors are deployed to monitor a phenomenon that in many cases can be modeled by an underlying stochastic process. The goal is to detect change in the process with tolerable false alarm rate. In practice, sensors may have different accuracy and sensitivity range, or they decay along time. As a result, the sensed data will contain uncertainties and sometimes they are conflicting. In this study, we propose a novel framework to take advantage of Dempster‐Shafer theorys capability of representation of uncertainty to detect change and effectively deal with complementary hypotheses. Specifically, Kullback‐Leibler divergence is used as the metric to find the distances between the estimated distribution with the before and after change distributions. Mass functions are calculated on the basis of those distance values for each sensor independently, and Dempster‐Shafer combination rule is applied to combine the mass values among all sensors. In the case of high conflict in various sensor readings, Dezert‐Smarandache combination rule is applied, and the belief, plausibility, and pignistic probability are obtained for decision making. Simulation results using both synthetic data and real data demonstrate the effectiveness of the proposed schemes.

Modeling treatment and drug effects at the molecular level using hybrid system theory

In this paper, we propose to study the treatment and drug effects at the molecular level using a hybrid system model. Specifically, we propose a generic piecewise linear model to analyze drug effects on the state of the genes in a genetic regulatory network. We intend to answer the following question: given an initial state, would a treatment or drug (control input) drive the target gene to a new desired state that are not reachable without the treatment or drug? assuming that the concentration level of the drug remains constant. In other words, we try to identify whether there is a chance that the treatment or drug will be effective for changing gene expressions at all. We provide detailed analysis for two cases. In the first case, there is only one target gene; while in the second case, there is also another gene interacting with the target gene. The relationships between various parameters (of the genetic regulatory network and the design of the drug) and the convergence and the steady state of the controlled genes are derived analytically and discussed in detail. Simulations are performed using MATLAB/SIMULINK and the results confirmed our analytical findings.

Review of stochastic hybrid systems with applications in biological systems modeling and analysis

Stochastic hybrid systems (SHS) have attracted a lot of research interests in recent years. In this paper, we review some of the recent applications of SHS to biological systems modeling and analysis. Due to the nature of molecular interactions, many biological processes can be conveniently described as a mixture of continuous and discrete phenomena employing SHS models. With the advancement of SHS theory, it is expected that insights can be obtained about biological processes such as drug effects on gene regulation. Furthermore, combining with advanced experimental methods, in silico simulations using SHS modeling techniques can be carried out for massive and rapid verification or falsification of biological hypotheses. The hope is to substitute costly and time-consuming in vitro or in vivo experiments or provide guidance for those experiments and generate better hypotheses.

Distributed spectrum monitoring and surveillance using a cognitive radio based testbed

This paper described the development and implementation of a distributed spectrum monitoring and surveillance testbed for identifying and locating RF signals using the Universal Software Radio Peripheral (USRP). We use a centralized RF trace collection testbed to establish the baseline, and we focus on distributed RF trace collection. Challenges associated with synchronization is identified and candidate solutions are discussed. The distributed testbed was implemented using NI USRPs (293×/295×) with LabVIEW. The complex nature of implementing the distributed case necessitate the choice of LabVIEW because of the versatile features provided. Potential applications are discussed and sample traces are demonstrated.

Femtocell as a relay with application of physical layer network coding

This study takes advantage of the ability of Physical-Layer Network Coding (PNC) to embrace electromagnetic interference, improve spectral efficiency and achieve throughput optimality in a femtocell operated as a relay. Specifically, we make a case for a femtocell operating as a relay and utilizing PNC to eliminate the interference that exists between macrocells user equipments (MUEs) and femtocells home users equipments (HUEs) in an Opened Subscriber Group (OSG) co-channel deployment. With PNC implemented at the HNB relay, it becomes possible for the relay to take advantage of the information signals transmitted from both the HUE and MUE rather than treating the MUEs signals as interference. To guarantee the Service Quality (QoS) of femtocell users, we develop the closed-form expressions of the cummulative distributions function (CDF) of the received signal-to-noise ratios (SNR) and the outage probability of the HUE analytically. Simulation results match the analytical results and it is demonstrated that PNC is suitable for a relay operated femtocell network.