OBJECTIVE: Utilizing passive radio frequency identification (RFID) tags embedded in knitted smart-garment devices, we wirelessly detect the respiratory state of a subject using an ensemble-based learning approach over an augmented Kalman-filtered time series of RF properties. METHODS: We propose a novel approach for noise modeling using a "reference tag," a second RFID tag worn on the body in a location not subject to perturbations due to respiratory motions that are detected via the primary RFID tag. The reference tag enables modeling of noise artifacts yielding significant improvement in detection accuracy. The noise is modeled using autoregressive moving average (ARMA) processes and filtered using state-augmented Kalman filters. The filtered measurements are passed through multiple classification algorithms (naive Bayes, logistic regression, decision trees) and a new similarity classifier that generates binary decisions based on current measurements and past decisions. RESULTS: Our findings demonstrate that state-augmented Kalman filters for noise modeling improves classification accuracy drastically by over 7.7% over the standard filter performance. Furthermore, the fusion framework used to combine local classifier decisions was able to predict the presence or absence of respiratory activity with over 86% accuracy. CONCLUSION: The work presented here strongly indicates the usefulness of processing passive RFID tag measurements for remote respiration activity monitoring. The proposed fusion framework is a robust and versatile scheme that once deployed can achieve high detection accuracy with minimal human intervention. SIGNIFICANCE: The proposed system can be useful in remote noninvasive breathing state monitoring and sleep apnea detection.

We have applied passive Radio Frequency Identification (RFID), typically used for inventory management, to implement a novel knit fabric strain gauge assembly using conductive thread. As the fabric antenna is stretched, the strength of the received signal varies, yielding potential for wearable, wireless, powerless smart-garment devices based on small and inexpensive passive RFID technology. Knit fabric sensors and other RFID biosensors can enable comfortable, continuous monitoring of biofeedback, but requires an integrated framework consisting of antenna modeling and fabrication, signal processing and machine learning on the noisy wireless signal, secure HIPAA- compliant data storage, visualization and human factors, and integration with existing medical devices and electronic health records (EHR) systems. We present a multidisciplinary, end-to-end framework to study, model, develop, and deploy RFID-based biosensors.

This study reports the progress and impact of STEM education through innovative learning by having students answer curriculum based questions and participate in inquiry-based activities. This practice is based on the Drexel University National Science Foundation STEM K-12 program titled "Catalyzing STEM Education via the National Academy of Engineering (NAE) Grand Challenges." In this program, collaboration between an engineering graduate fellow and a 9th grade high school biochemistry teacher results in engineering modules that include brief lessons, and hands-on activities to teach students about the NAE Grand Challenges. Students are asked open-ended questions in reference to proposing solutions to Grand Challenges, while also being encouraged to sketch quickly-made schematics to further illustrate their ideas. At the end of each module, students are tasked to complete brief surveys, for which the data are used to assess the effect of the modules on student perspectives of engineering, the Grand Challenges, and the fellow. This study reports on three dimensions of this initiative: the collaborative process between the high school teacher and the fellow when generating lessons; the descriptive details of several lesson modules and relation to the Next Generation Science Standards (NGSS); and an analysis of data collected through student survey.

This paper describes the development of a new Software Defined Communications (SDC) testbed architecture. SDC aims to generalize the area of software defined radio to include propagation media not exclusively limited to radio frequencies (optical, ultrasonic, etc.). This SDC platform leverages existing and custom hardware in combination with reference software applications in order to provide a complete research and development platform. This platform can be used to implement current and future standards that make use of highly demanding communications techniques, including ultrawideband (UWB) radio and free-space optical communications. This paper describes the commercial and custom hardware that is being integrated into the platform, including the baseband hardware and the modular transceiver frontends. Furthermore, the paper describes the software development currently in progress with this platform, including the integration of available open source designs into the platform, and the development of custom IP for scalable OFDM PHY implementations in radio and optical communications. We seek to create a complete research platform for the commercial and academic wireless communities, capable of delivering the highest possible performance and flexibility while providing the necessary development tools and reference designs in order to minimize system learning curve and development cost.

Multiple antenna communication systems are a solution to meet the demand for pervasive computing and ubiquitous wireless communications. As multiple antenna communication systems become more broadly deployed, integrating unobtrusive antennas into various form factors is becoming increasingly important. Antennas that are flexible and transparent can ease this design constraint. In this paper, we present a dipole conductive polymer antenna that is flexible and transparent. The focus of this paper is to show how well this antenna works in a communications network by evaluating channel capacity and packet error rate.

We present a component-based multi-level mixed-signal design and simulation environment for microsystems spanning the domains of electronics, mechanics, and optics. The environment provides a solution to the problem of accurate modeling and simulation of multi-domain devices at the system level. This is achieved by partitioning the system into components that are modeled by analytic expressions. These expressions are reduced via linearization into regions of operation for each element of the component and solved with modified nodal analysis in the frequency domain, which guarantees convergence. Feedback among components is managed by a discrete event simulator sending composite signals between components. For electrical, and mechanical components, interaction is via physical connectivity while optical signals are modeled using complex scalar wavefronts, providing the accuracy necessary to model micro-optical components. Simulation speed vs. simulation accuracy can be tuned by controlling the granularity of the regions of operation of the devices, sample density of the optical wavefronts, or the time steps of the discrete event simulator. The methodology is specifically optimized for loosely coupled systems of complex components such as are found in multi-domain microsystems.

As designers become more aggressive in introducing optical components to micro-systems, rigorous optical models are required for system-level simulation tools. Common optical modeling techniques and approximations are not valid for most optical micro-systems, and those techniques that provide accurate simulation are computationally slow. In this paper, we introduce an angular frequency optical propagation technique that greatly reduces computation time while achieving the accuracy of a full scalar formulation. We present simulations of a diffractive optical MEM grating light valve to show the advantages of this optical propagation method and the integration of the technique into a system-level multi-domain CAD tool.

Chatoyant models free-space opto-electronic components and systems and performs simulations and analyses that allow designers to make informed system level trade-offs.Recently, the use of MEM bulk and surface micro-machining technology has enabled the fabrication of micro-optical-mechanical systems.This paper presents our models for diffractive optics and new analysis techniques which extend Chatoyant to support optical MEMS design.We show these features in the simulation of two optical MEM systems.

This paper presents a system capable of static and dynamic simulationsof heterogeneous opto-electronic systems. It is capable ofmodeling Gaussian optical signal propagation with mechanicaltolerancing at the system level. We present results which demonstratethe system's ability to predict the effects of various componentparameters, such as detector geometry, and system levelparameters, such as alignment tolerances, on system performance.