The following papers have been published for the NSF CNS Smart Farm project<\/p>\n\n\n\n
Analysis of Deep Learning Models Towards High Rajesh Kudupudi, Fariborz Lohrabi Pour, Dong Sam Ha, Sook Shin Ha, and Keyvan Ramezanpour Abstract<\/strong>: This paper investigates direct and indirect learning methods to develop deep learning digital predistortion (DL-DPD) models and apply the models to improve the linearity of a power amplifier (PA). The two methods are applied to class-AB and class-F\u22121 PAs designed with gallium nitride (GaN) on silicon carbide (SiC) high electron mobility transistors (HEMTs). The simulation results show that both direct and indirect DL-DPD methods improve the linearity of the class-AB PA by about 12 dB and the class-F^\u22121 PA by 11 dB, while the indirect method offers marginally better performance. The paper shows the direct learning method leads to significant improvement of the DL-DPD method based over the memory polynomial. It also presents the advantages of a BiLSTM based on the neural network architecture to design direct\/indirect DPDs. Finally, it demonstrates that the DL-DPD can improve the linearity of class-AB and class-F^-1 PAs without architectural changes.<\/p>\n\n\n\n Click to view the paper<\/a><\/p>\n\n\n\n R. Kudupudi, F. Lohrabi Pour, D. S. Ha, S. S. Ha, and K. Ramezanpour, \u201cAnalysis of Deep Learning Models Towards High Performance Digital Predistortion for RF Power Amplifiers,\u201d International Symposium on Circuits and Systems (ISCAS), 5 pages, May 2022.<\/p>\n\n\n\n Power Efficient Wireless Sensor Node through Edge Abhishek Priyadarshan Damle*, Sook Shin Ha*, Zhuqing Zhao *, Barbara Roqueto dos Reis**, Robin White**, and Dong Sam Ha* Abstract<\/strong>: Edge intelligence can reduce power dissipation to enable power-hungry long-range wireless applications. This work applies edge intelligence to quantify the reduction in power dissipation. We designed a wireless sensor node with a LoRa radio and implemented a decision tree classifier, in situ, to classify behaviors of cattle. We estimate that employing edge intelligence on our wireless sensor node reduces its average power dissipation by up to a factor of 50, from 20.10 mW to 0.41 mW. We also observe that edge intelligence increases the link budget without significantly affecting average power dissipation.<\/p>\n\n\n\n Click to view the paper<\/a><\/p>\n\n\n\n A. P. Damle, S. S. Ha, Z. Zhao, B. R. dos Reis, R. White, and D. S. Ha, \u201cPower Efficient Wireless Sensor Node through Edge Intelligence,\u201d International Symposium on Circuits and Systems (ISCAS), 5 pages, May 2022.<\/p>\n\n\n\n An Attack-Resilient and Energy-Adaptive Qisheng Zhang\u2020, Yash Mahajan\u2020, Ing-Ray Chen\u2020, Dong Sam Ha\u2021, and Jin-Hee Cho\u2020 Abstract<\/strong>: In this work, we propose an energy-adaptive monitoring system for a solar sensor-based smart animal farm (e.g., cattle). The proposed smart farm system aims to maintain high-quality monitoring services by solar sensors with limited and fluctuating energy against a full set of cyberattack behaviors including false data injection, message dropping, or protocol non-compliance. We leverage Subjective Logic (SL) as the belief model to consider different types of uncertainties in opinions about sensed data. We develop two Deep Reinforcement Learning (DRL) schemes leveraging the design concept of uncertainty maximization in SL for DRL agents running on gateways to collect high-quality sensed data with low uncertainty and high freshness. We assess the performance of the proposed energy-adaptive smart farm system in terms of accumulated reward, monitoring error, system overload, and battery maintenance level. We compare the performance of the two DRL schemes developed (i.e., multi-agent deep Q-learning, MADQN, and multi-agent proximal policy optimization, MAPPO) with greedy and random baseline schemes in choosing the set of sensed data to be updated to collect high-quality sensed data to achieve resilience against attacks. Our experiments demonstrate that MAPPO with the uncertainty maximization technique outperforms its counterparts.<\/p>\n\n\n\n Click to view the paper<\/a><\/p>\n\n\n\n Click here to view code<\/a><\/p>\n\n\n\n Q. Zhang, Y. Mahajan, I.R. Chen, D. Ha, and J.H. Cho, \u201cAn Attack-Resilient and Energy-Adaptive Monitoring System for Smart Farms,\u201d IEEE GLOBECOM, Dec. 2022<\/p>\n\n\n\n MiLTOn: Sensing Product Integrity without Opening the Box using Non-Invasive Acoustic Vibrometry<\/strong><\/p>\n\n\n\n Akshay Gadre Abstract<\/strong>: This paper asks: \u201cCan we detect whether a fragile product, made of porcelain or glass is damaged as it travels along the supply chain, without opening its packaging?\u201d We ask this question in the context of the multi-billion dollar global supply chain industry of fragile products that experience large overheads due to product returns. This paper presents MiLTOn, a novel acoustic and mm-wave based solution for through-box non-invasive product integrity sensing that is sensitive to even minute sub-mm cracks in the object. MiLTOn is inspired by acoustic vibrometry used for instance to monitor cracks in railroads. Unlike traditional vibrometry, MiLTOn is unique in its ability to sense products non-invasively using an external transducer and microphone, neither of which are in direct physical contact of the object within the box. MiLTOn processes measurements from the microphone to design a robust and environment-independent product signature that can be used to sense presence of product defects. Our extensive evaluation on a large number of fragile products of diverse materials demonstrates 97% accuracy in identifying product damage.<\/p>\n\n\n\n
Performance Digital Predistortion for RF Power
Amplifiers<\/strong><\/p>\n\n\n\n
Multifunctional Integrated Circuits and System (MICS) Group
Bradley Department of Electrical and Computer Engineering
Virginia Tech, Blacksburg, Virginia, 24061, USA
E-mail: {rajeshk19, fariborzlp, ha, sook, rkeyvan8} @vt.edu<\/p>\n\n\n\n
Intelligence<\/strong><\/p>\n\n\n\n
*Multifunctional Integrated Circuits and System (MICS) Group
Bradley Department of Electrical and Computer Engineering
**Dept of Animal and Poultry Sciences
Virginia Tech, Blacksburg, Virginia, 24061, USA
E-mail: {adamle, sook, zhuqing8, barbarar, rrwhite, ha} @vt.edu<\/p>\n\n\n\n
Monitoring System for Smart Farms<\/strong><\/p>\n\n\n\n
\u2020Department of Computer Science, Virginia Tech, Falls Church, VA, USA
\u2021Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
{qishengz19, yashmahajan, irchen, dha, jicho}@vt.edu<\/p>\n\n\n\n
Carnegie Mellon University
agadre@andrew.cmu.edu
Deepak Vasisht
Microsoft and UIUC
deepakv@illinois.edu
Nikunj Raghuvanshi, Bodhi Priyantha, Manikanta Kotaru
{nikunjr,bodhip,mkotaru}@microsoft.com
Swarun Kumar
Carnegie Mellon University
swarun@cmu.edu
Ranveer Chandra
Microsoft
ranveer@microsoft.com<\/p>\n\n\n\n