Non-invasive authentication of mail packages using nuclear quadrupole resonance spectroscopy.

The international postal network is one of the most widely used methods for correspondence throughout the world. Most postal traffic across the globe consists of legitimate interpersonal, business-consumer, and business-business communications. However, the global postal system is also utilized for criminal activity. In particular, it is often utilized to ship and distribute contraband, including illegal psychoactive drugs such as fentanyl and heroin, to consumers. Existing technological solutions are capable of identifying synthetic opioids and other illegal drugs within packages, but are accompanied by several disadvantages that make them unsuitable for large-scale authentication of international mail traffic. This paper presents a novel method for non-invasive authentication of mail packages that overcomes these challenges. The approach uses nuclear quadrupole resonance (NQR) spectroscopy to detect and quantify the presence of known active pharmaceutical ingredients (APIs) within the package. It has been experimentally demonstrated using a bench top prototype. Test results from a variety of package types demonstrate the effectiveness of the proposed authentication approach.

Addressing the range anxiety of battery electric vehicles with charging en route.

Battery electric vehicles (BEVs) have emerged as a promising alternative to traditional internal combustion engine (ICE) vehicles due to benefits in improved fuel economy, lower operating cost, and reduced emission. BEVs use electric motors rather than fossil fuels for propulsion and typically store electric energy in lithium-ion cells. With rising concerns over fossil fuel depletion and the impact of ICE vehicles on the climate, electric mobility is widely considered as the future of sustainable transportation. BEVs promise to drastically reduce greenhouse gas emissions as a result of the transportation sector. However, mass adoption of BEVs faces major barriers due to consumer worries over several important battery-related issues, such as limited range, long charging time, lack of charging stations, and high initial cost. Existing solutions to overcome these barriers, such as building more charging stations, increasing battery capacity, and stationary vehicle-to-vehicle (V2V) charging, often suffer from prohibitive investment costs, incompatibility to existing BEVs, or long travel delays. In this paper, we propose Peer-to-Peer Car Charging (P2C2), a scalable approach for charging BEVs that alleviates the need for elaborate charging infrastructure. The central idea is to enable BEVs to share charge among each other while in motion through coordination with a cloud-based control system. To re-vitalize a BEV fleet, which is continuously in motion, we introduce Mobile Charging Stations (MoCS), which are high-battery-capacity vehicles used to replenish the overall charge in a vehicle network. Unlike existing V2V charging solutions, the charge sharing in P2C2 takes place while the BEVs are in-motion, which aims at minimizing travel time loss. To reduce BEV-to-BEV contact time without increasing manufacturing costs, we propose to use multiple batteries of varying sizes and charge transfer rates. The faster but smaller batteries are used for charge transfer between vehicles, while the slower but larger ones are used for prolonged charge storage. We have designed the overall P2C2 framework and formalized the decision-making process of the cloud-based control system. We have evaluated the effectiveness of P2C2 using a well-characterized simulation platform and observed dramatic improvement in BEV mobility. Additionally, through statistical analysis, we show that a significant reduction in carbon emission is also possible if MoCS can be powered by renewable energy sources.

A smart mask for active defense against airborne pathogens.

Face masks are a primary preventive measure against airborne pathogens. Thus, they have become one of the keys to controlling the spread of the COVID-19 virus. Common examples, including N95 masks, surgical masks, and face coverings, are passive devices that minimize the spread of suspended pathogens by inserting an aerosol-filtering barrier between the user's nasal and oral cavities and the environment. However, the filtering process does not adapt to changing pathogen levels or other environmental factors, which reduces its effectiveness in real-world scenarios. This paper addresses the limitations of passive masks by proposing ADAPT, a smart IoT-enabled "active mask". This wearable device contains a real-time closed-loop control system that senses airborne particles of different sizes near the mask by using an on-board particulate matter (PM) sensor. It then intelligently mitigates the threat by using mist spray, generated by a piezoelectric actuator, to load nearby aerosol particles such that they rapidly fall to the ground. The system is controlled by an on-board micro-controller unit that collects sensor data, analyzes it, and activates the mist generator as necessary. A custom smartphone application enables the user to remotely control the device and also receive real-time alerts related to recharging, refilling, and/or decontamination of the mask before reuse. Experimental results on a working prototype confirm that aerosol clouds rapidly fall to the ground when the mask is activated, thus significantly reducing PM counts near the user. Also, usage of the mask significantly increases local relative humidity levels.

Detecting Dye-Contaminated Vegetables Using Low-Field NMR Relaxometry.

Dyeing vegetables with harmful compounds has become an alarming public health issue over the past few years. Excessive consumption of these dyed vegetables can cause severe health hazards, including cancer. Copper sulfate, malachite green, and Sudan red are some of the non-food-grade dyes widely used on vegetables by untrusted entities in the food supply chain to make them look fresh and vibrant. In this study, the presence and quantity of dye-based adulteration in vegetables are determined by applying 1H-nuclear magnetic resonance (NMR) relaxometry. The proposed technique was validated by treating some vegetables in-house with different dyes and then soaking them in various solvents. The resulting solutions were collected and analyzed using NMR relaxometry. Specifically, the effective transverse relaxation time constant, T2,eff, of each solution was estimated using a Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence. Finally, the estimated time constants (i.e., measured signatures) were compared with a library of existing T2,eff data to detect and quantify the presence of unwanted dyes. The latter consists of data-driven models of transverse decay times for various concentrations of each water-soluble dye. The time required to analyze each sample using the proposed approach is dye-dependent but typically no longer than a few minutes. The analysis results can be used to generate warning flags if the detected dye concentrations violate widely accepted standards for food dyes. The proposed low-cost detection approach can be used in various stages of a produce supply chain, including consumer household.

NQR sensitive embedded signatures for authenticating additively manufactured objects.

Automatic recognition of unique characteristics of an object can provide a powerful solution to verify its authenticity and safety. It can mitigate the growth of one of the largest underground industries-that of counterfeit goods-flowing through the global supply chain. In this article, we propose the novel concept of material biometrics, in which the intrinsic chemical properties of structural materials are used to generate unique identifiers for authenticating individual products. For this purpose, the objects to be protected are modified via programmable additive manufacturing of built-in chemical "tags" that generate signatures depending on their chemical composition, quantity, and location. We report a material biometrics-enabled manufacturing flow in which plastic objects are protected using spatially-distributed tags that are optically invisible and difficult to clone. The resulting multi-bit signatures have high entropy and can be non-invasively detected for product authentication using [Formula: see text]Cl nuclear quadrupole resonance (NQR) spectroscopy.

Clandestine nanoelectromechanical tags for identification and authentication.

The realization of truly unclonable identification and authentication tags is the key factor in protecting the global economy from an ever-increasing number of counterfeit attacks. Here, we report on the demonstration of nanoscale tags that exploit the electromechanical spectral signature as a fingerprint that is characterized by inherent randomness in fabrication processing. Benefiting from their ultraminiaturized size and transparent constituents, these clandestine nanoelectromechanical tags provide substantial immunity to physical tampering and cloning. Adaptive algorithms are developed for digital translation of the spectral signature into binary fingerprints. A large set of tags fabricated in the same batch is used to estimate the entropy of the corresponding fingerprints with high accuracy. The tags are also examined under repetitive measurements and temperature variations to verify the consistency of the fingerprints. These experiments highlight the potential of clandestine nanoelectromechanical tags for the realization of secure identification and authentication methodologies applicable to a wide range of products and consumer goods.

Single-shot spatially-localized NQR using field-dependent relaxation rates.

Nuclear quadrupole resonance (NQR) is commonly used to characterize solid materials containing quadrupolar nuclei. For example, NQR is a promising technique for detecting plastic explosives and other forbidden substances as well as for authenticating pharmaceutical products. Spatially-resolved NQR measurements are of particular interest for enabling automated sample positioning, evaluation of sample heterogeneity, and chemometric authentication of objects. This paper proposes a rapid "single-shot" method for spatially-resolved NQR with the potential to benefit such applications. The proposed method takes advantage of the fact that certain NQR relaxation rates are field-dependent: the observed field dependence is used to convert relaxation time distributions measured in a static field gradient (estimated via Laplace inversion of time-domain data) into spatial distributions. The method was validated using 35Cl and 37Cl NQR of sodium chlorate and other compounds. Effective spatial resolution was also improved by using machine learning (ML) to classify the measured spatial distributions. In particular, experimental results demonstrate accurate ML-based classification of 3D-printed objects containing arbitrary binary distributions of sodium chlorate. Such distributions can thus be used as NQR-based "embedded barcodes" for authenticating high-value objects.

Tunable and Lightweight On-Chip Event Detection for Implantable Bladder Pressure Monitoring Devices.

Lower urinary tract dysfunctions, such as urinary incontinence and overactive bladder, are conditions that greatly affect the quality of life for millions of individuals worldwide. For those with more complex pathophysiologies, diagnosis of these conditions often requires a urodynamics study, providing physicians with a snapshot view of bladder mechanics. Recent advancements in implantable bladder pressure monitors and advanced data analysis techniques have made diagnosis through chronic monitoring a promising prospect. However, implants targeted at treatment must remain in the bladder for long periods of time, making minimizing power consumption a primary design objective. Currently, much of the typical implant's power draw is due to data transmission. Previous work has demonstrated an adaptive rate transmission technique to reduce power consumption. However, the ultimate reduction in power consumption can only be attained when the device does not transmit bladder pressure samples, but rather bladder events. In this paper, we present an algorithm and circuit level implementation for on-chip bladder pressure data compression and event detection. It is designed to be a complete, tunable, and lightweight diagnosis and treatment framework for bladder pressure monitoring implants, capable of selectively transmitting compressed bladder pressure data with tunable quality, "snapshots" of significant bladder events, or simply indicate events occurred for the highest energy efficiency. The design aims to minimize area through resource reuse, leading to a total area of 1.75 , and employs advanced VLSI techniques for power reduction. With compression and event detection enabled, the design consumes roughly 2.6 nW in TSMC technology. With only event detection, this reduces to 2.1 nW, making this approach ideal for long-life implantable bladder pressure monitoring devices.

Broadband quantitative NQR for authentication of vitamins and dietary supplements.

We describe hardware, pulse sequences, and algorithms for nuclear quadrupole resonance (NQR) spectroscopy of medicines and dietary supplements. Medicine and food safety is a pressing problem that has drawn more and more attention. NQR is an ideal technique for authenticating these substances because it is a non-invasive method for chemical identification. We have recently developed a broadband NQR front-end that can excite and detect 14N NQR signals over a wide frequency range; its operating frequency can be rapidly set by software, while sensitivity is comparable to conventional narrowband front-ends over the entire range. This front-end improves the accuracy of authentication by enabling multiple-frequency experiments. We have also developed calibration and signal processing techniques to convert measured NQR signal amplitudes into nuclear spin densities, thus enabling its use as a quantitative technique. Experimental results from several samples are used to illustrate the proposed methods.

Authentication of Medicines Using Nuclear Quadrupole Resonance Spectroscopy.

The production and sale of counterfeit and substandard pharmaceutical products, such as essential medicines, is an important global public health problem. We describe a chemometric passport-based approach to improve the security of the pharmaceutical supply chain. Our method is based on applying nuclear quadrupole resonance (NQR) spectroscopy to authenticate the contents of medicine packets. NQR is a non-invasive, non-destructive, and quantitative radio frequency (RF) spectroscopic technique. It is sensitive to subtle features of the solid-state chemical environment and thus generates unique chemical fingerprints that are intrinsically difficult to replicate. We describe several advanced NQR techniques, including two-dimensional measurements, polarization enhancement, and spin density imaging, that further improve the security of our authentication approach. We also present experimental results that confirm the specificity and sensitivity of NQR and its ability to detect counterfeit medicines.

Real-time, autonomous bladder event classification and closed-loop control from single-channel pressure data.

Urinary incontinence, or the loss of bladder control, is a debilitating condition affecting millions worldwide, which significantly reduces quality of life. Neuromodulation of lower urinary tract nerves can be used to treat sensations of urgency in many subjects, including those with Spinal Cord Injury (SCI). Event driven, or conditional stimulation has been investigated as a possible improvement to the state-of-the-art open-loop stimulation systems available today. However, this requires a robust, adaptive, and noise-tolerant method of classifying bladder function from real-time bladder pressure measurements. Context-Aware Thresholding (CAT) has been previously shown to work well on prerecorded single contraction urodynamic data. In this work, for the first time, we present real-time detection of multiple serial bladder contractions using urodynamic recordings from human subjects with SCI and Neurogenic Detrusor Overactivity (NDO). CAT demonstrated a high degree of accuracy and noise tolerance on prerecorded data from 15 human subjects, with a mean accuracy of 92% and average false positive rate of 0.3 false positives per contraction. Analysis of event detection latencies showed that CAT identified and responded to events 1.4 seconds faster than the original human experimenter. Finally, we present a case study in which CAT was used live for real-time autonomous, closed-loop bladder control in a single human subject with SCI and NDO, successfully inhibiting four consecutive unwanted bladder contractions and increasing bladder capacity by 40%.

Real-Time Classification of Bladder Events for Effective Diagnosis and Treatment of Urinary Incontinence.

Diagnosis of lower urinary tract dysfunction with urodynamics has historically relied on data acquired from multiple sensors using nonphysiologically fast cystometric filling. In addition, state-of-the-art neuromodulation approaches to restore bladder function could benefit from a bladder sensor for closed-loop control, but a practical sensor and automated data analysis are not available. We have developed an algorithm for real-time bladder event detection based on a single in situ sensor, making it attractive for both extended ambulatory bladder monitoring and closed-loop control of stimulation systems for diagnosis and treatment of bladder overactivity. Using bladder pressure data acquired from 14 human subjects with neurogenic bladder, we developed context-aware thresholding, a novel, parameterized, user-tunable algorithmic framework capable of real-time classification of bladder events, such as detrusor contractions, from single-sensor bladder pressure data. We compare six event detection algorithms with both single-sensor and two-sensor systems using a metric termed Conditional Stimulation Score, which ranks algorithms based on projected stimulation efficacy and efficiency. We demonstrate that adaptive methods are more robust against day-to-day variations than static thresholding, improving sensitivity and specificity without parameter modifications. Relative to other methods, context-aware thresholding is fast, robust, highly accurate, noise-tolerant, and amenable to energy-efficient hardware implementation, which is important for mapping to an implant device.

Implantable ultrasonic imaging assembly for automated monitoring of internal organs.

An implantable miniaturized imaging device can be attractive in many clinical applications. They include automated, periodic, high-resolution monitoring of susceptible organs for early detection of an anomalous growth. In this paper, we propose an implantable ultrasonic imager capable of online high-resolution imaging of a region inside the body. A feasibility analysis is presented, with respect to design of such a system and its application to online monitoring of tumor growth in deep internal organs. We use ultrasound (US) imaging technology, as it is safe, low-cost, can be easily miniaturized, and amenable for long-term, point-of-care (POC) monitoring. The design space of the proposed system has been explored including form factor, transducer specifications and power/energy requirements. We have analyzed the effectiveness of the system in timely detection of anomalous growth in a case study through software simulations using a widely-accepted ultrasonic platform (Field II). Finally, through experimental studies using medical grade phantoms and an ultrasound scanner, we have evaluated the system with respect to its major imaging characteristics. It is observed that interstitial imaging under area/power constraints would achieve significantly better imaging quality in terms of contrast sensitivity and spatial resolution than existing techniques in deep, internal body parts, while maintaining the automated monitoring advantages.

Implantable ultrasonic dual functional assembly for detection and treatment of anomalous growth.

High Intensity Focused Ultrasound (HIFU) is emerging as an accurate, noninvasive method for ablation of certain primary and metastatic tumors. Typically, ablation is performed with an external therapeutic transducer. However, external HIFU treatment suffers from limitations of low therapeutic efficiency for ablation of tumors, deep in internal organs such as liver, kidney and brain. Interstitial HIFU through an internal transducer, implanted locally near the organ of interest, could alleviate some of these limitations. Furthermore, it can be attractive for point-of-care (POC) treatment. In this paper, we propose the design of a dual-functional implantable assembly for image-guided HIFU treatment of anomalous growth. It is realized by effective integration of a central HIFU array with two ultrasonic imaging arrays for high-resolution online monitoring and efficient treatment. We explore the design space for the implant and identify the major design parameters including the power requirement. Using a widely used simulation platform, we show that the proposed implant, besides providing a potential POC solution, achieves a better therapeutic performance for certain tumor positions in internal organs, than the extracorporeal HIFU treatment.

Low-power implantable ultrasound imager for online monitoring of tumor growth.

Clinicians all over the world agree that the most effective way to deal with a malignant tumor growth within internal organs is to detect it early. In most cases, early detection requires automated localized high resolution scanning of a region of interest--such as lungs, brain, small intestine, and gastro-intestinal tract. External or endoscopic ultrasound technologies are often not effective for imaging deep inside organs due to lack of adequate spatial resolution. In this paper, we propose using a miniature, low power implantable ultrasound imager for online monitoring of tumor growth in internal body parts. We explore the design space for such an implantable ultrasonic imaging system targeted to early detection or post-surgery monitoring of a malignant growth. The system can be placed locally in a susceptible region or for post-operative monitoring of relapse. The proposed system is capable of providing high-resolution image of a volume of interest at periodic intervals, using a relatively safe imaging technology, thus providing a chronic, reliable, and cost-effective monitoring option.

Implantable electronics: emerging design issues and an ultra light-weight security solution.

Implantable systems that monitor biological signals require increasingly complex digital signal processing (DSP) electronics for real-time in-situ analysis and compression of the recorded signals. While it is well-known that such signal processing hardware needs to be implemented under tight area and power constraints, new design requirements emerge with their increasing complexity. Use of nanoscale technology shows tremendous benefits in implementing these advanced circuits due to dramatic improvement in integration density and power dissipation per operation. However, it also brings in new challenges such as reliability and large idle power (due to higher leakage current). Besides, programmability of the device as well as security of the recorded information are rapidly becoming major design considerations of such systems. In this paper, we analyze the emerging issues associated with the design of the DSP unit in an implantable system. Next, we propose a novel ultra light-weight solution to address the information security issue. Unlike the conventional information security approaches like data encryption, which come at large area and power overhead and hence are not amenable for resource-constrained implantable systems, we propose a multilevel key-based scrambling algorithm, which exploits the nature of the biological signal to effectively obfuscate it. Analysis of the proposed algorithm in the context of neural signal processing and its hardware implementation shows that we can achieve high level of security with ∼ 13X lower power and ∼ 5X lower area overhead than conventional cryptographic solutions.

Electromechanical computing at 500 degrees C with silicon carbide.

Logic circuits capable of operating at high temperatures can alleviate expensive heat-sinking and thermal-management requirements of modern electronics and are enabling for advanced propulsion systems. Replacing existing complementary metal-oxide semiconductor field-effect transistors with silicon carbide (SiC) nanoelectromechanical system (NEMS) switches is a promising approach for low-power, high-performance logic operation at temperatures higher than 300 degrees C, beyond the capability of conventional silicon technology. These switches are capable of achieving virtually zero off-state current, microwave operating frequencies, radiation hardness, and nanoscale dimensions. Here, we report a microfabricated electromechanical inverter with SiC complementary NEMS switches capable of operating at 500 degrees C with ultralow leakage current.

A preferential design approach for energy-efficient and robust implantable neural signal processing hardware.

For implantable neural interface applications, it is important to compress data and analyze spike patterns across multiple channels in real time. Such a computational task for online neural data processing requires an innovative circuit-architecture level design approach for low-power, robust and area-efficient hardware implementation. Conventional microprocessor or Digital Signal Processing (DSP) chips would dissipate too much power and are too large in size for an implantable system. In this paper, we propose a novel hardware design approach, referred to as "Preferential Design" that exploits the nature of the neural signal processing algorithm to achieve a low-voltage, robust and area-efficient implementation using nanoscale process technology. The basic idea is to isolate the critical components with respect to system performance and design them more conservatively compared to the noncritical ones. This allows aggressive voltage scaling for low power operation while ensuring robustness and area efficiency. We have applied the proposed approach to a neural signal processing algorithm using the Discrete Wavelet Transform (DWT) and observed significant improvement in power and robustness over conventional design.

Wavelet-based neural pattern analyzer for behaviorally significant burst pattern recognition.

Closed-loop neural prosthesis systems rely on accurately recording neural data from multiple neurons and detecting behaviorally meaningful patterns before representing them in a highly compressed form for wireless transmission over a limited-bandwidth link. We present a novel wavelet-based approach for detecting spikes, grouping them as bursts and building a dynamic vocabulary of meaningful burst patterns. Simulation results on pre-recorded in vivo multi-channel extracellular neural data from the buccal ganglion of Aplysia demonstrate the feasibility of behavior recognition as well as data compression (>500X) by the proposed approach.