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This article discusses the challenges and opportunities of managing group delay dispersion (GDD) and its relation to the performance standards of future sixth-generation (6G) wireless communication systems utilizing terahertz frequency waves. The unique susceptibilities of 6G systems to GDD are described, along with a quantitative description of the sources of GDD, including multipath, rough surface scattering, intelligent reflecting surfaces, and propagation through the atmosphere. An experimental case-study is presented that confirms previous models quantifying the impact of atmospheric GDD. Several GDD manipulation strategies are presented illustrating their hindered effectiveness in the 6G context. Conversely, some benefits of leveraging GDD to enhance 6G systems, such as improved security and simplified hardware, are also discussed. Finally, a perspective on using photonic GDD control devices is provided, revealing quantitative benefits that may unburden existing equalization schemes. The article argues that GDD will uniquely and significantly impact some 6G systems, but that its careful consideration along with new mitigation strategies, including photonic devices, will help optimize system performance. The conclusion provides a perspective to guide future research in this area., Comment: 7 pages, 4 figures, 2 tables. This work has been submitted to the IEEE for possible publication

Channel turbulence presents a formidable obstacle for free-space optical (FSO) communication. Anticipation of turbulence levels is highly important for mitigating disruptions. We study the application of machine learning (ML) to FSO data streams to rapidly predict channel turbulence levels with no additional sensing hardware. An optical bit stream was transmitted through a controlled channel in the lab under six distinct turbulence levels, and the efficacy of using ML to classify turbulence levels was examined. ML-based turbulence level classification was found to be >98% accurate with multiple ML training parameters, but highly dependent upon the timescale of changes between turbulence levels., Comment: 5 pages, 4 figures, 1 table. Submitted to IEEE letters

Radio frequency (RF) communications offer reliable but low data rates and energy-inefficient satellite links, while free-space optical (FSO) promises high bandwidth but struggles with disturbances imposed by atmospheric effects. A hybrid RF/FSO architecture aims to achieve optimal reliability along with high data rates for space communications. Accurate prediction of dynamic ground-to-satellite FSO link availability is critical for routing decisions in low-earth orbit constellations. In this paper, we propose a system leveraging ubiquitous RF links to proactively forecast FSO link degradation prior to signal drops below threshold levels. This enables pre-calculation of rerouting to maximally maintain high data rate FSO links throughout the duration of weather effects. We implement a supervised learning model to anticipate FSO attenuation based on the analysis of RF patterns. Through the simulation of a dense lower earth orbit (LEO) satellite constellation, we demonstrate the efficacy of our approach in a simulated satellite network, highlighting the balance between predictive accuracy and prediction duration. An emulated cloud attenuation model is proposed which provides insight into the temporal profiles of RF signals and their correlation to FSO channel dynamics. Our investigation sheds light on the trade-offs between prediction horizon and accuracy arising from RF beacon proximity, achieving a prediction accuracy of 86\% with 16 RF beacons., Comment: This work has been submitted to the IEEE for possible publication

In this study, we present a deep learning-based approach for time-series respiration data classification. The dataset contains regular breathing patterns as well as various forms of abnormal breathing, obtained through non-contact incoherent light-wave sensing (LWS) technology. Given the one-dimensional (1D) nature of the data, we employed a 1D convolutional neural network (1D-CNN) for classification purposes. Genetic algorithm was employed to optimize the 1D-CNN architecture to maximize classification accuracy. Addressing the computational complexity associated with training the 1D-CNN across multiple generations, we implemented transfer learning from a pre-trained model. This approach significantly reduced the computational time required for training, thereby enhancing the efficiency of the optimization process. This study contributes valuable insights into the potential applications of deep learning methodologies for enhancing respiratory anomaly detection through precise and efficient respiration classification., Comment: 7 pages, 8 figures, accepted in International Conference on Fuzzy Systems, Soft Computing, and Explainable AI (NAFIPS2024)

Contribution: Case study of a rural-focused educational program with two components: 1) introducing high school students and teachers to Smart and Connected Technologies (SCTs) that can be used to solve local problems; 2) engaging the local community in supporting local technology-driven entrepreneurship. Background: Rural communities typically lag behind in terms of participation in the digital economy, and use of technology in general. Yet they often have the most to gain, due to high rates of self-employment and lower private-sector job opportunities. Research Questions: Can a broadly-scoped rural technology education program lead to improvements in 1) student and teacher SCT awareness, 2) SCT skills, 3) aspirations for future SCT use directed toward entrepreneurship and overall community wellbeing? Methodology: Our multidisciplinary team used a mixed-methods approach to engage a rural high school robotics team as well as the local community. Over the course of one year, students took part in hands-on-training with SCTs ("PETE-Kits" and associated curriculum) and brainstormed entrepreneurial projects via ideation events. Community members were involved at the beginning and end of the project, including judging a "shark-tank" style event where student business ideas using SCT were presented. Findings: Results from student pre / post activity assessments suggest that the program was effective at increasing comfort with technology and combining technical skills with entrepreneurial opportunities. Post surveys from community members, including teachers, demonstrated clear support for the program and an appreciation of how SCTs / digital skills could benefit the local economy and wellbeing.

In this study, we present a non-contact respiratory anomaly detection method using incoherent light-wave signals reflected from the chest of a mechanical robot that can breathe like human beings. In comparison to existing radar and camera-based sensing systems for vitals monitoring, this technology uses only a low-cost ubiquitous infrared light source and sensor. This light-wave sensing system recognizes different breathing anomalies from the variations of light intensity reflected from the chest of the robot within a 0.5m-1.5m range with an average classification accuracy of up to 96.6% using machine learning., Comment: 1 page poster paper, 1 figure, 2 tables, accepted and presented in 23rd Wireless Telecommunications Symposium 2024. Symposium proceedings link: https://wtsconference.org/documents/WTS%202024%20-%20Program.pdf . Full version at 2311.01367v1

An experimental testbed has been constructed to assess the capabilities of Light-Wave Sensing, a promising new vitals monitoring approach. A Light-Wave Sensing apparatus utilizes infrared radiation to contactlessly monitor the subtle respiratory motions of a subject from meters away. A respiration-simulating robot was programmed to produce controllable, humanlike chest displacement patterns for accuracy analysis. Estimation of respiration rate within tenths of a breath per minute has been demonstrated with the testbed, establishing the tenability of the method for use in commercial non-contact respiration monitoring equipment, and setting practical expectations on the usable range of this sensing modality. An analytical model is then presented to guide hardware selection, and used to derive the absolute range limitations of Light-Wave Sensing., Comment: 12 pages, 11 figures (except photos of authors)

The increasing demand for wireless sensing systems has led to the exploration of alternative technologies to overcome the spectrum scarcity of traditional approaches based on radio frequency (RF) waves or microwaves. Incoherent light sources such as light-emitting diodes (LED), paired with light sensors, have the potential to become an attractive option for wireless sensing due to their energy efficiency, longer lifespan, and lower cost. Although coherent light or laser may present safety risks to human eyes and skin, incoherent visible and infrared light has low intensity, and does not harm the human body. Incoherent light has the potential to supersede other wireless sensing technologies, namely RF, laser and camera, by providing many additional benefits including easy implementation, wide bandwidth, reusable frequency, minimum interference, enhanced privacy and simpler data processing. However, the application of incoherent light in the wireless sensing domain is still in its infancy and is an emerging research topic. This study explores the enormous potential and benefits of incoherent visible and infrared light in wireless sensing through various indoor and outdoor applications including speed estimation of vehicles, human vitals monitoring, blood glucose sensing, gesture recognition, occupancy estimation and structural health monitoring., Comment: 11 pages, 9 figures including photos of authors, submitted to IEEE Circuits and Systems Magazine

In this study, we present a wireless (non-contact) gesture recognition method using only incoherent light wave signals reflected from a human subject. In comparison to existing radar, light shadow, sound and camera-based sensing systems, this technology uses a low-cost ubiquitous light source (e.g., infrared LED) to send light towards the subject's hand performing gestures and the reflected light is collected by a light sensor (e.g., photodetector). This light wave sensing system recognizes different gestures from the variations of the received light intensity within a 20-35cm range. The hand gesture recognition results demonstrate up to 96% accuracy on average. The developed system can be utilized in numerous Human-computer Interaction (HCI) applications as a low-cost and non-contact gesture recognition technology., Comment: 5 pages, 6 figures, 2 tables, Accepted, presented and camera-ready version submitted at ICEEE 2023 at Istanbul, Turkey. arXiv admin note: substantial text overlap with arXiv:2007.08178

Highway work zones are susceptible to undue accumulation of motorized vehicles which calls for dynamic work zone warning signs to prevent accidents. The work zone signs are placed according to the location of the end-of-queue of vehicles which usually changes rapidly. The detection of moving objects in video captured by Unmanned Aerial Vehicles (UAV) has been extensively researched so far, and is used in a wide array of applications including traffic monitoring. Unlike the fixed traffic cameras, UAVs can be used to monitor the traffic at work zones in real-time and also in a more cost-effective way. This study presents a method as a proof of concept for detecting End-of-Queue (EOQ) of traffic by processing the real-time video footage of a highway work zone captured by UAV. EOQ is detected in the video by image processing which includes background subtraction and blob detection methods. This dynamic localization of EOQ of vehicles will enable faster and more accurate relocation of work zone warning signs for drivers and thus will reduce work zone fatalities. The method can be applied to detect EOQ of vehicles and notify drivers in any other roads or intersections too where vehicles are rapidly accumulating due to special events, traffic jams, construction, or accidents., Comment: 13 pages, 7 figures excluding photos of authors, Published in International Journal of Intelligent Transportation Systems Research. Link to the published version: https://link.springer.com/article/10.1007/s13177-023-00374-0

Advancements in lighting systems and photodetectors provide opportunities to develop viable alternatives to conventional communication and sensing technologies, especially in the vehicular industry. Most of the studies that propose visible light in communication or sensing adopt the Lambertian propagation (path loss) model. This model requires knowledge and utilization of multiple parameters to calculate the path loss such as photodetector area, incidence angle, and distance between transmitter and receiver. In this letter, a simplified path loss model that is mathematically more tractable is proposed for vehicular sensing and communication systems that use visible light technology. Field measurement campaigns are conducted to validate the performance and limits of the developed path loss model. The proposed model is used to fit the data collected at different ranges of incident angles and distances. Further, this model can be used for designing visible light-based communication and sensing systems to minimize the complexity of the Lambertian path loss model, particularly for cases where the incident angle between transmitter and receiver is relatively small., Comment: 5 pages, 6 figures, submitted to IEEE conference

Human respiratory rate and its pattern convey essential information about the physical and psychological states of the subject. Abnormal breathing can indicate fatal health issues leading to further diagnosis and treatment. Wireless light-wave sensing (LWS) using incoherent infrared light shows promise in safe, discreet, efficient, and non-invasive human breathing monitoring without raising privacy concerns. The respiration monitoring system needs to be trained on different types of breathing patterns to identify breathing anomalies.The system must also validate the collected data as a breathing waveform, discarding any faulty data caused by external interruption, user movement, or system malfunction. To address these needs, this study simulated normal and different types of abnormal respiration using a robot that mimics human breathing patterns. Then, time-series respiration data were collected using infrared light-wave sensing technology. Three machine learning algorithms, decision tree, random forest and XGBoost, were applied to detect breathing anomalies and faulty data. Model performances were evaluated through cross-validation, assessing classification accuracy, precision and recall scores. The random forest model achieved the highest classification accuracy of 96.75% with data collected at a 0.5m distance. In general, ensemble models like random forest and XGBoost performed better than a single model in classifying the data collected at multiple distances from the light-wave sensing setup., Comment: 12 pages, 15 figures, published in IEEE Transactions on Human-Machine Systems

A do-it-yourself (DIY) light-wave sensing (LWS) and communication project was developed to generate interest and clarify basic electromagnetic (EM) and wireless communication concepts among students at different education levels from middle school to undergraduate. This paper demonstrates the nature of the project and its preliminary effectiveness. Wireless sensing/communication concepts are generally considered hard to comprehend being underpinned only by theoretical coursework and occasional simulations. Further, K-12 schools and small academic institutions may not have the resources necessary to produce tangible demonstrations for clarification. The consequent lack of affordable hands-on experiences fails to motivate and engage students. The DIY-LWS is intended to make wireless concepts more understandable and less esoteric by linking fundamental concepts with familiar technologies such as solar cells, visible lights, and smartphones. It is also intended to pique student interest by allowing them to personally assemble, operate, and explore a light-based wireless communication system. A preliminary assessment is used to determine the student base knowledge level and enthusiasm for wireless and related core topics. Students are instructed to assemble and test their own DIY-LWS hardware to provide a hands-on experience and stimulate further exploration. Short lectures are given to link conceptual ideas to the real-world phenomena. Finally, students are re-assessed to quantify any change in conceptual understanding. The DIY-LWS kits have been used in multiple events with students at different levels from secondary to high schools to college. Pre- and post-assessments revealed pronounced improvements (the number of correct answers doubled) in student understanding of EM concepts. Instructors observed tremendous interest and excitement among the students during and after the experiments., Comment: 6 pages, 12 figures, IEEE Transactions on Education

In this paper, we demonstrate the ability to recognize hand gestures in a non-contact, wireless fashion using only incoherent light signals reflected from a human subject. Fundamentally distinguished from radar, lidar and camera-based sensing systems, this sensing modality uses only a low-cost light source (e.g., LED) and sensor (e.g., photodetector). The light-wave-based gesture recognition system identifies different gestures from the variations in light intensity reflected from the subject's hand within a short (20-35 cm) range. As users perform different gestures, scattered light forms unique, statistically repeatable, time-domain signatures. These signatures can be learned by repeated sampling to obtain the training model against which unknown gesture signals are tested and categorized. Performance evaluations have been conducted with eight gestures, five subjects, different distances and lighting conditions, and with visible and infrared light sources. The results demonstrate the best hand gesture recognition performance of infrared sensing at 20 cm with an average of 96% accuracy. The developed gesture recognition system is low-cost, effective and non-contact technology for numerous Human-computer Interaction (HCI) applications., Comment: 12 pages, 25 figures including 6 author's photographs, revised version submitted to IEEE Transactions on Human-Machine Systems

We analyze single and multilayered metamaterials by modeling each layer as a metasurface with effective surface electric and magnetic susceptibility derived through a thin film approximation. Employing a transfer matrix method, these metasurfaces can be assembled into multilayered metamaterials to realize certain functionalities. We demonstrate numerically that this approach provides an alternative interpretation of metamaterial-based perfect absorption, showing that the underlying mechanism is a modified Fabry-Perot resonance. This method provides a general approach applicable for decoupled or weakly coupled multilayered metamaterials., Comment: submitted to Physical Review B

In this Letter we present resonance properties in terahertz metamaterials consisting of a split-ring resonator array made from high temperature superconducting films. By varying the temperature, we observed efficient metamaterial resonance switching and frequency tuning with some features not revealed before. The results were well reproduced by numerical simulations of metamaterial resonance using the experimentally measured complex conductivity of the superconducting film. We developed a theoretical model that explains the tuning features, which takes into account the resistive resonance damping and additional split-ring inductance contributed from both the real and imaginary parts of the temperature-dependent complex conductivity. The theoretical model further predicted more efficient resonance switching and frequency shifting in metamaterials consisting of a thinner superconducting split-ring resonator array, which were also verified in experiments., Comment: 5 pages, 3 figures, submitted to Physical Review Letters

We characterize planar electric terahertz metamaterials fabricated on thin, flexible substrates using terahertz time-domain spectroscopy. Quasi-three-dimensional metamaterials are formed by stacking multiple metamaterial layers. Transmission measurements reveal resonant band-stop behavior that becomes stronger with an increasing number of layers. Extracted metamaterial dielectric functions are shown to be independent of the number of layers, validating the effective medium approximation. Limitations of this approximation are discussed., Comment: 4 pages, 3 eps figures, uses revtex4

We describe the electronic control of extraordinary terahertz transmission through subwavelength metal hole arrays fabricated on doped semiconductor substrates. The hybrid metal-semiconductor forms a Schottky diode structure, where the active depletion region modifies the substrate conductivity in real-time by applying an external voltage bias. This enables effective control of the resonance enhanced terahertz transmission. Our proof of principle device achieves an intensity modulation depth of 52% by changing the voltage bias between 0 and 16 volts. Further optimization may result in improvement of device performance and practical applications. This approach can be also translated to the other optical frequency ranges., Comment: 8 pages, 4 figures, submitted to Optics Express