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We have built a prototype universal radio adapter which furnishes seamless and secure wireless communication through non-cooperative indigenous 5G networks for military and government users. The adapter consists of a waveform-agnostic hardware add-on that tunnels DoD terrestrial and satellite data. The adapter uses secure protocols for cross-connecting military-grade wireless RF communications equipment using spectrum in the range from UHF to Ka-band. A 5G data transport channel replaces the captured spectrum for transporting information at the IQ-sample level. In a sense, we replace the antenna-air interface and wireless channel with a transparent 5G data network. A plurarity of legacy military systems can operate through modern 5G networks in a seamless way without any knowledge of the characteristics of military waveforms. The adapter incorporates AI/ML based methods for smart spectrum sensing and autonomous radio reconfiguration. This enables intelligent interconnection of a number of military radios through non-cooperative (potentially adversarial) 5G commercial cellular networks. The adapter is built on four technical pillars: 1) ultra-wideband apertures for multi-functional and flexible software-defined radios (SDRs) with agile, wideband, and dual-band tunable RF transceivers for FR1/FR2 bands; 2) physical layer operation that involve device authentication via deep-learning based RF fingerprinting and compression of acquired IQ data; 3) secure and reconfigurable cryptographic co-processors employing the new quantum-safe algorithms selected by NIST to achieve authentication, key exchange, and encryption with focus on resource-constrained low size, weight, power, and cost (SWaP-C) devices; and 4) generative artificial intelligence and spread-spectrum steganography to hide DoD traffic passed through 5G networks and improve resiliency against real-time traffic analysis by nation-state carriers and intelligence agencies.

Ontheblock is an online marketplace designed specifically for Virginia Tech students to buy, sell, or exchange household items safely and efficiently. The platform provides an easy-to-use interface that allows students to browse through various items, create listings for their own goods, communicate with potential buyers or sellers, rate their experience with a user, report a user, share a product on the internet and browse future available products so that buyers and sellers can transact on a future date and on their own terms. The goal of Ontheblock is to provide a convenient solution for students who are often on tight budgets and need to acquire household items without breaking the bank. Additionally, the platform offers a safe and secure way to contact potential buyers or sellers, as all profiles are verified “vt.edu” domain backed by secure OPT validations by phone and email. This reduces the problem/risk of scammers scamming unsuspecting students of their precious money and resources. Ontheblock is an ideal solution for any Virginia Tech student looking for an affordable and safe way to buy or sell household items.

Miniaturization of the sensor active areas to length scales of the order of the analyte has been sought as means to reduce device footprints, and to capitalize on the novel optical and electronic enhancements that arise at this scale. However, little is known on what to expect of the sensor behaviour if the sensor footprints are shrunk to the dimensions of the order of analyte themselves. Our results demonstrate the densities and kinetics of adsorption of analyte to significantly increase with decrease in the sensor footprints to dimensions of the order of few multiples of analyte dimensions. Such increase is found to be generic, irrespective of the nature of interactions that drive adsorption, exhibiting qualitative similarity for electrostatic adsorption of nanoparticles, chemisorption of primary oligonucleotides, or complementary base pairing with target nucleotides. The carryover of these benefits onto a macroscopic sensor however requires high density nanopatterns exhibiting significant fill factors withtout compromising inter-feature isolation. The impact of feature dimensions, pattern fill factors, analyte concentrations, presence of convective flow, or the density of receptors are investigated using quantitative and real-time measurements of the nanostructure-analyte interactions using nanopatterned QCM sensors. The results indicate significant opportunities for rational design of nanopatterned macroscopic sensors, as well as nanoscopic sensors with sensor active areas of the order of analyte dimensions, e.g., electromagnetic hot-spots, or nanowire sensors.

Nanoplasmonic sensors based on surface-enhanced spectroscopies carry profound promise toward ultrasensitive detection of biomolecular analytes within miniaturized measurement footprints. High sensitivity in these sensors is achieved by intense electromagnetic (EM) enhancements at hot spots and colocalization of analytes with such hot spots. However, EM hot spots exhibiting high EM enhancements often present confined areas that render them inaccessible to large analytes such as biomolecules. Addressing this requires rational engineering of a nanoplasmonic interface that factors in the analyte surface concentrations and how they are distributed with respect to the EM hot spots. Here we demonstrate combination of metal-enhanced fluorescence (MEF) with a quartz crystal microbalance (QCM) through fabrication of highly resolved plasmonic nanoarrays directly on the QCM sensor. QCM and MEF are simultaneously employed to monitor in situ, real-time binding of fluorescently labeled protein to receptor-functionalized plasmonic arrays. The correlation between the QCM and MEF responses is used to quantify surface density of protein that contributes to the MEF signal intensities and obtain analyte distribution with respect to the EM hot spots by geometric modeling. Results further reveal the MEF assays to be sensitive down to ∼2 zmol of protein within measurement footprints that are 8 orders of magnitude smaller than that of the QCM.