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1. Dual-Transduction Electromechanical Receiver for Near-Field Wireless Power Transmission

   https://doi.org/10.1109/MEMS51782.2021.9375416  

   Smith, Spencer E. ; Halim, Miah A. ; Rendon-Hernandez, Adrian A. ; Arnold, David P. ( January 2021 , 2021 IEEE 34th International Conference on Micro Electro Michancial Systems (MEMS))

   null (Ed.)

   We report the design, fabrication, and experimental characterization of a chip-sized electromechanical micro-receiver for low-frequency, near-field wireless power transmission that employs both electrodynamic and piezoelectric transductions to achieve a high power density and high output voltage while maintaining a low profile. The 0.09 cm 3 device comprises a laser-micro-machined titanium suspension, one NdFeB magnet, two PZT-5A piezo-ceramic patches, and a precision-manufactured micro-coil with a thickness of only 1.65 mm. The device generates 520 μW average power (5.5 mW•cm -3 ) at 4 cm distance from a transmitter coil operating at 734.6 Hz and within safe human exposure limits. Compared to a previously reported piezoelectric-only prototype, this device generates ~2.5x higher power and offers 18% increased normalized power density (6.5 mW•cm -3 •mT -2 ) for potential improvement in wirelessly charging wearables and bio-implants. 

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2. Experimentation Framework for Wireless Communication Systems under Jamming Scenarios Dataset

   https://doi.org/10.5281/zenodo.4749668  

   Jacovic, Marko ( January 2021 , Zenodo)

   Data files were used in support of the research paper titled "“Experimentation Framework for Wireless
   Communication Systems under Jamming Scenarios" which has been submitted to the IET Cyber-Physical Systems: Theory & Applications journal. 

   Authors: Marko Jacovic, Michael J. Liston, Vasil Pano, Geoffrey Mainland, Kapil R. Dandekar
   Contact: krd26@drexel.edu

   ---------------------------------------------------------------------------------------------

   Top-level directories correspond to the case studies discussed in the paper. Each includes the sub-directories: logs, parsers, rayTracingEmulation, results. 

   --------------------------------

   logs:    - data logs collected from devices under test
       - 'defenseInfrastucture' contains console output from a WARP 802.11 reference design network. Filename structure follows '*x*dB_*y*.txt' in which *x* is the reactive jamming power level and *y* is the jaming duration in samples (100k samples = 1 ms). 'noJammer.txt' does not include the jammer and is a base-line case. 'outMedian.txt' contains the median statistics for log files collected prior to the inclusion of the calculation in the processing script. 
       - 'uavCommunication' contains MGEN logs at each receiver for cases using omni-directional and RALA antennas with a 10 dB constant jammer and without the jammer. Omni-directional folder contains multiple repeated experiments to provide reliable results during each calculation window. RALA directories use s*N* folders in which *N* represents each antenna state. 
       - 'vehicularTechnologies' contains MGEN logs at the car receiver for different scenarios. 'rxNj_5rep.drc' does not consider jammers present, 'rx33J_5rep.drc' introduces the periodic jammer, in 'rx33jSched_5rep.drc' the device under test uses time scheduling around the periodic jammer, in 'rx33JSchedRandom_5rep.drc' the same modified time schedule is used with a random jammer. 

   --------------------------------

   parsers:    - scripts used to collect or process the log files used in the study
           - 'defenseInfrastructure' contains the 'xputFiveNodes.py' script which is used to control and log the throughput of a 5-node WARP 802.11 reference design network. Log files are manually inspected to generate results (end of log file provides a summary). 
           - 'uavCommunication' contains a 'readMe.txt' file which describes the parsing of the MGEN logs using TRPR. TRPR must be installed to run the scripts and directory locations must be updated. 
           - 'vehicularTechnologies' contains the 'mgenParser.py' script and supporting 'bfb.json' configuration file which also require TRPR to be installed and directories to be updated. 

   --------------------------------

   rayTracingEmulation:    - 'wirelessInsiteImages': images of model used in Wireless Insite
               - 'channelSummary.pdf': summary of channel statistics from ray-tracing study
               - 'rawScenario': scenario files resulting from code base directly from ray-tracing output based on configuration defined by '*WI.json' file 
               - 'processedScenario': pre-processed scenario file to be used by DYSE channel emulator based on configuration defined by '*DYSE.json' file, applies fixed attenuation measured externally by spectrum analyzer and additional transmit power per node if desired
               - DYSE scenario file format: time stamp (milli seconds), receiver ID, transmitter ID, main path gain (dB), main path phase (radians), main path delay (micro seconds), Doppler shift (Hz), multipath 1 gain (dB), multipath 1 phase (radians), multipath 1 delay relative to main path delay (micro seconds), multipath 2 gain (dB), multipath 2 phase (radians), multipath 2 delay relative to main path delay (micro seconds)
               - 'nodeMapping.txt': mapping of Wireless Insite transceivers to DYSE channel emulator physical connections required
               - 'uavCommunication' directory additionally includes 'antennaPattern' which contains the RALA pattern data for the omni-directional mode ('omni.csv') and directional state ('90.csv')

   --------------------------------

   results:    - contains performance results used in paper based on parsing of aforementioned log files
    

    

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3. High surface area reverse electrowetting energy harvesting with power conditioning circuitry for self-powered motion sensors

   https://doi.org/10.1117/12.2587541  

   Adhikari, Pashupati R. ; Biswas, Dipon K. ; Tasneem, Nishat T. ; Reid, Russell C. ; Mahbub, Ifana ( April 2021 , SPIE Defense + Commercial Sensing)

   Dutta, Achyut K. ; Balaya, Palani ; Xu, Sheng (Ed.)

   Monitoring human health in real-time using wearable and implantable electronics (WIE) has become one of the most promising and rapidly growing technologies in the healthcare industry. In general, these electronics are powered by batteries that require periodic replacement and maintenance over their lifetime. To prolong the operation of these electronics, they should have zero post-installation maintenance. On this front, various energy harvesting technologies to generate electrical energy from ambient energy sources have been researched. Many energy harvesters currently available are limited by their power output and energy densities. With the miniaturization of wearable and implantable electronics, the size of the harvesters must be miniaturized accordingly in order to increase the energy density of the harvesters. Additionally, many of the energy harvesters also suffer from limited operational parameters such as resonance frequency and variable input signals. In this work, low frequency motion energy harvesting based on reverse electrowetting-ondielectric (REWOD) is examined using perforated high surface area electrodes with 38 µm pore diameters. Total available surface area per planar area was 8.36 cm2 showing a significant surface area enhancement from planar to porous electrodes and proportional increase in AC voltage density from our previous work. In REWOD energy harvesting, high surface area electrodes significantly increase the capacitance and hence the power density. An AC peak-to-peak voltage generation from the electrode in the range from 1.57-3.32 V for the given frequency range of 1-5 Hz with 0.5 Hz step is demonstrated. In addition, the unconditioned power generated from the harvester is converted to a DC power using a commercial off-theshelf Schottky diode-based voltage multiplier and low dropout regulator (LDO) such that the sensors that use this technology could be fully self-powered. The produced charge is then converted to a proportional voltage by using a commercial charge amplifier to record the features of the motion activities. A transceiver radio is also used to transmit the digitized data from the amplifier and the built-in analog-to-digital converter (ADC) in the micro-controller. This paper proposes the energy harvester acting as a self-powered motion sensor for different physical activities for wearable and wireless healthcare devices. 

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4. A Wirelessly Rechargeable AA Battery Using Electrodynamic Wireless Power Transmission

   https://doi.org/10.3390/en14092368  

   Smith, Spencer E. ; Halim, Miah A. ; Chyczewski, Stasiu T. ; Rendon-Hernandez, Adrian A. ; Arnold, David P. ( May 2021 , Energies)

   null (Ed.)

   We report the design, fabrication, and characterization of a prototype that meets the form, fit, and function of a household 1.5 V AA battery, but which can be wirelessly recharged without removal from the host device. The prototype system comprises a low-frequency electrodynamic wireless power transmission (EWPT) receiver, a lithium polymer energy storage cell, and a power management circuit (PMC), all contained within a 3D-printed package. The EWPT receiver and overall system are experimentally characterized using a 238 Hz sinusoidal magnetic charging field and either a 1000 µF electrolytic capacitor or a lithium polymer (LiPo) cell as the storage cell. The system demonstrates a minimal operating field as low as 50 µTrms (similar in magnitude to Earth’s magnetic field). At this minimum charging field, the prototype transfers a maximum dc current of 50 µA to the capacitor, corresponding to a power delivery of 118 µW. The power effectiveness of the power management system is approximately 49%; with power effectiveness defined as the ratio between actual output power and the maximum possible power the EWPT receiver can transfer to a pure resistive load at a given field strength. 

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5. 100 pT/cm single-point MEMS magnetic gradiometer from a commercial accelerometer

   https://doi.org/10.1038/s41378-020-0173-z  

   Javor, Josh ; Stange, Alexander ; Pollock, Corey ; Fuhr, Nicholas ; Bishop, David J. ( December 2020 , Microsystems & Nanoengineering)

   Abstract Magnetic sensing is present in our everyday interactions with consumer electronics and demonstrates the potential for the measurement of extremely weak biomagnetic fields, such as those of the heart and brain. In this work, we leverage the many benefits of microelectromechanical system (MEMS) devices to fabricate a small, low-power, and inexpensive sensor whose resolution is in the range of biomagnetic fields. At present, biomagnetic fields are measured only by expensive mechanisms such as optical pumping and superconducting quantum interference devices (SQUIDs), suggesting a large opportunity for MEMS technology in this work. The prototype fabrication is achieved by assembling micro-objects, including a permanent micromagnet, onto a postrelease commercial MEMS accelerometer using a pick-and-place technique. With this system, we demonstrate a room-temperature MEMS magnetic gradiometer. In air, the sensor’s response is linear, with a resolution of 1.1 nT cm −1 , spans over 3 decades of dynamic range to 4.6 µT cm −1 , and is capable of off-resonance measurements at low frequencies. In a 1 mTorr vacuum with 20 dB magnetic shielding, the sensor achieves a 100 pT cm −1 resolution at resonance. This resolution represents a 30-fold improvement compared with that of MEMS magnetometer technology and a 1000-fold improvement compared with that of MEMS gradiometer technology. The sensor is capable of a small spatial resolution with a magnetic sensing element of 0.25 mm along its sensitive axis, a >4-fold improvement compared with that of MEMS gradiometer technology. The calculated noise floor of this platform is 110 fT cm −1  Hz −1/2 , and thus, these devices hold promise for both magnetocardiography (MCG) and magnetoencephalography (MEG) applications. 

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