Search

Biolayer interferometry (BLI) is a well-established laboratory technique for studying biomolecular interactions important for applications such as drug development. Currently, there are interesting opportunities for expanding the use of BLI in other fields, including the development of rapid diagnostic tools. To date, there are no detailed frameworks for implementing BLI in target-recognition studies that are pivotal for developing point-of-need biosensors. Here, we attempt to bridge these domains by providing a framework that connects output(s) of molecular interaction studies with key performance indicators used in the development of point-of-need biosensors. First, we briefly review the governing theory for protein-ligand interactions, and we then summarize the approach for real-time kinetic quantification using various techniques. The 2020 PRISMA guideline was used for all governing theory reviews and meta-analyses. Using the information from the meta-analysis, we introduce an experimental framework for connecting outcomes from BLI experiments (KD, kon, koff) with electrochemical (capacitive) biosensor design. As a first step in the development of a larger framework, we specifically focus on mapping BLI outcomes to five biosensor key performance indicators (sensitivity, selectivity, response time, hysteresis, operating range). The applicability of our framework was demonstrated in a study of case based on published literature related to SARS-CoV-2 spike protein to show the development of a capacitive biosensor based on truncated angiotensin-converting enzyme 2 (ACE2) as the receptor. The case study focuses on non-specific binding and selectivity as research goals. The proposed framework proved to be an important first step toward modeling/simulation efforts that map molecular interactions to sensor design.

We introduce a novel audio processing architecture, the Open Voice Brain Model (OVBM), improving detection accuracy for Alzheimer's (AD) longitudinal discrimination from spontaneous speech. We also outline the OVBM design methodology leading us to such architecture, which in general can incorporate multimodal biomarkers and target simultaneously several diseases and other AI tasks. Key in our methodology is the use of multiple biomarkers complementing each other, and when two of them uniquely identify different subjects in a target disease we say they are orthogonal. We illustrate the OBVM design methodology by introducing sixteen biomarkers, three of which are orthogonal, demonstrating simultaneous above state-of-the-art discrimination for two apparently unrelated diseases such as AD and COVID-19. Depending on the context, throughout the paper we use OVBM indistinctly to refer to the specific architecture or to the broader design methodology. Inspired by research conducted at the MIT Center for Brain Minds and Machines (CBMM), OVBM combines biomarker implementations of the four modules of intelligence: The brain OS chunks and overlaps audio samples and aggregates biomarker features from the sensory stream and cognitive core creating a multi-modal graph neural network of symbolic compositional models for the target task. In this paper we apply the OVBM design methodology to the automated diagnostic of Alzheimer's Dementia (AD) patients, achieving above state-of-the-art accuracy of 93.8% using only raw audio, while extracting a personalized subject saliency map designed to longitudinally track relative disease progression using multiple biomarkers, 16 in the reported AD task. The ultimate aim is to help medical practice by detecting onset and treatment impact so that intervention options can be longitudinally tested. Using the OBVM design methodology, we introduce a novel lung and respiratory tract biomarker created using 200,000+ cough samples to pre-train a model d

Foodborne pathogens are a major concern for public health. We demonstrate for the first time a partially automated sensing system for rapid (~17 min), label-free impedimetric detection of Escherichia coli spp. in food samples (vegetable broth) and hydroponic media (aeroponic lettuce system) based on temperature-responsive poly(N-isopropylacrylamide) (PNIPAAm) nanobrushes. This proof of concept (PoC) for the Sense-Analyze-Respond-Actuate (SARA) paradigm uses a biomimetic nanostructure that is analyzed and actuated with a smartphone. The bio-inspired soft material and sensing mechanism is inspired by binary symbiotic systems found in nature, where low concentrations of bacteria are captured from complex matrices by brush actuation driven by concentration gradients at the tissue surface. To mimic this natural actuation system, carbon-metal nanohybrid sensors were fabricated as the transducer layer, and coated with PNIPAAm nanobrushes. The most effective coating and actuation protocol for E. coli detection at various temperatures above/below the critical solution temperature of PNIPAAm was determined using a series of electrochemical experiments. After analyzing nanobrush actuation in stagnant media, we developed a flow through system using a series of pumps that are triggered by electrochemical events at the surface of the biosensor. SARA PoC may be viewed as a cyber-physical system that actuates nanomaterials using smartphone-based electroanalytical testing of samples. This study demonstrates thermal actuation of polymer nanobrushes to detect (sense) bacteria using a cyber-physical systems (CPS) approach. This PoC may catalyze the development of smart sensors capable of actuation at the nanoscale (stimulus-response polymer) and macroscale (non-microfluidic pumping).

Communication range in passive radio-frequency identification (RFID) front-end devices is a critical barrier in the real-world implementation of this low-cost technology. Purely passive RFID tags power up by harvesting the limited RF energy transmitted by the interrogator, and communicate by backscattering the incident signal. This mode of communication keeps manufacturing costs below a few cents per tag, but the limited power available at the tag undermines long-range deployment. In this paper, we present an approach to use photovoltaics (PV) to augment the available energy at the tag to improve read range and sensing capabilities. We provide this extra-energy to the RFID integrated circuit (IC) using minimum additional electronics yet enabling persistent sensor-data acquisition. Current and emerging thin-film PV technologies have significant potential for being very low-cost, hence eliminating the barrier for implementation and making of PV-RFID wireless sensors. We reduce the long-range PV-RFID idea to practice by creating functional prototypes of: 1) a wireless building environment sensor to monitor temperature and 2) an embedded tracker to find lost golf balls. The read range of PV-RFID is enhanced eight times compared to conventional passive devices. In addition, the PV-RFID tags persistently transmit large volumes of sensor data (>0.14 million measurements per day) without using batteries. For communication range and energy persistence, we observe good agreement between calculated estimates and experimental results. We have also identified avenues for future research to develop low-cost PV-RFID devices for wireless sensing in the midst of the other competitive wireless technologies, such as Bluetooth, ZigBee, long range (LoRa) backscatter etc.

Goal: We hypothesized that COVID-19 subjects, especially including asymptomatics, could be accurately discriminated only from a forced-cough cell phone recording using Artificial Intelligence. To train our MIT Open Voice model we built a data collection pipeline of COVID-19 cough recordings through our website (opensigma.mit.edu) between April and May 2020 and created the largest audio COVID-19 cough balanced dataset reported to date with 5,320 subjects. Methods: We developed an AI speech processing framework that leverages acoustic biomarker feature extractors to pre-screen for COVID-19 from cough recordings, and provide a personalized patient saliency map to longitudinally monitor patients in real-time, non-invasively, and at essentially zero variable cost. Cough recordings are transformed with Mel Frequency Cepstral Coefficient and inputted into a Convolutional Neural Network (CNN) based architecture made up of one Poisson biomarker layer and 3 pre-trained ResNet50's in parallel, outputting a binary pre-screening diagnostic. Our CNN-based models have been trained on 4256 subjects and tested on the remaining 1064 subjects of our dataset. Transfer learning was used to learn biomarker features on larger datasets, previously successfully tested in our Lab on Alzheimer's, which significantly improves the COVID-19 discrimination accuracy of our architecture. Results: When validated with subjects diagnosed using an official test, the model achieves COVID-19 sensitivity of 98.5% with a specificity of 94.2% (AUC: 0.97). For asymptomatic subjects it achieves sensitivity of 100% with a specificity of 83.2% . Conclusions: AI techniques can produce a free, non-invasive, real-time, any-time, instantly distributable, large-scale COVID-19 asymptomatic screening tool to augment current approaches in containing the spread of COVID-19. Practical use cases could be for daily screening of students, workers, and public as schools, jobs, and transport reopen, or for pool testing to qui

In this manuscript, we discuss relevant socioeconomic factors for developing and implementing Sensor aNAlytic Point Solutions (SNAPS) as point-of-care tools to serve impoverished communities. The distinct economic, environmental, cultural, and ethical paradigms that affect economically disadvantaged users add complexity to the process of technology development and deployment beyond the science and engineering issues. We begin by contextualizing the environmental burden of disease in select low-income regions around the world, including environmental hazards at work, home, and the broader community environment, where SNAPS may be helpful in the prevention and mitigation of human exposure to harmful biological vectors and chemical agents. We offer examples of SNAPS designed for economically disadvantaged users, specifically for supporting decision-making in cases of tuberculosis (TB) infection and mercury exposure. We follow-up by discussing the economic challenges that are involved in the phased implementation of diagnostic tools in low-income markets and describe a micropayment-based systems-as-a-service approach (pay-a-penny-per-use--PAPPU), which may be catalytic for the adoption of low-end, low-margin, low-research, and the development SNAPS. Finally, we provide some insights into the social and ethical considerations for the assimilation of SNAPS to improve health outcomes in marginalized communities. Keywords: sensor analytic point solutions (SNAPS); environmental health; poverty; pay-a-penny-per-use (PAPPU); public health, USDA National Institute of Food and Agriculture (grant no. 2018-67016-27578)

Augmented Reality (AR) is known to enhance user experience, however, it remains under-adopted in industry. We present an AR interaction system improving human-machine coordination in Internet of Things (IoT) and Industry 4.0 applications including manufacturing and assembly, maintenance and safety, and other highly-interactive functions. A driver of slow adoption is the computational complexity and inaccuracy in localization and rendering digital content. AR systems may render digital content close to the associated physical objects, but traditional object recognition and localization modules perform poorly when tracking texture-less objects and complex shapes, presenting a need for robust and efficient digital content rendering techniques. We propose a method of improving IoT-AR by integrating Deep Learning with AR to increase accuracy and robustness of the target object localization module, taking both color and depth images as input and outputting the target's pose parameters. Quantitative and qualitative experiments prove this system's efficacy and show potential for fusing these emerging technologies in real-world applications.

This paper focuses on the design and analysis of an inexpensive novel chipless RFID sensing scheme for soil moisture and salinity content detection. The sensor is designed at the UHF RFID band so that it can be tweaked easily to a chip-based RFID sensor. Alongside the designed sensor, a theoretical soil model with different moisture and salinity levels is simulated here. A fabricated prototype is used to measure the variation of moisture and salinity levels in sandy soil. Both the simulated and measured results exhibit frequency shift of sensor resonance with moisture content variation at non-saline conditions. However, if the soil is saline, the resonance amplitude gets reduced with increased salinity levels and exhibits moisture content independence and hence no frequency variation.

In this review, we discuss the role of sensor analytics point solutions (SNAPS), a reduced complexity machine-assisted decision support tool. We summarize the approaches used for mobile phone-based chemical/biological sensors, including general hardware and software requirements for signal transduction and acquisition. We introduce SNAPS, part of a platform approach to converge sensor data and analytics. The platform is designed to consist of a portfolio of modular tools which may lend itself to dynamic composability by enabling context-specific selection of relevant units, resulting in case-based working modules. SNAPS is an element of this platform where data analytics, statistical characterization and algorithms may be delivered to the data either via embedded systems in devices, or sourced, in near real-time, from mist, fog or cloud computing resources. Convergence of the physical systems with the cyber components paves the path for SNAPS to progress to higher levels of artificial reasoning tools (ART) and emerge as data-informed decision support, as a service for general societal needs. Proof of concept examples of SNAPS are demonstrated both for quantitative data and qualitative data, each operated using a mobile device (smartphone or tablet) for data acquisition and analytics. We discuss the challenges and opportunities for SNAPS, centered around the value to users/stakeholders and the key performance indicators users may find helpful, for these types of machine-assisted tools. Keywords: sensor; smart systems; data analytics; cyber-physical systems; artificial reasoning tools; ART; drag and drop analytics; DADA; sensor-analytics point solutions; SNAPS; sense-analyze-respond-actuate; SARA; machine-assisted tools; MAT; machine-assisted platform; MAP; knowledge graphs; trans-disciplinary convergence, Agriculture and Food Research Initiative Competitive Grant (grant no. 2018-67016-27578), NSF (project no. 1805512), NSF (project no. 1511953)

Photovoltaic (PV) cells have the potential to serve as on-board power sources for low-power IoT devices. Here, we explore the use of perovskite solar cells to power Radio Frequency (RF) backscatter-based IoT devices with few \mu \text{W} power demand. Perovskites are suitable for low-cost, high-performance, low-temperature processing, and flexible light energy harvesting that hold the possibility to significantly extend the range and lifetime of current backscatter techniques such as Radio Frequency Identification (RFID). For these reasons, perovskite solar cells are prominent candidates for future low-power wireless applications. We report on realizing a functional perovskite-powered wireless temperature sensor with 4 m communication range. We use a 10.1% efficient perovskite PV module generating an output voltage of 4.3 V with an active area of 1.06 cm2 under 1 sun illumination, with AM 1.5G spectrum, to power a commercial off-the-shelf RFID IC, requiring 10 - 45 \mu \text{W} of power. Having an on-board energy harvester provides extra-energy to boost the range of the sensor (5 \times ) in addition to providing energy to carry out high-volume sensor measurements (hundreds of measurements per min). Our evaluation of the prototype suggests that perovskite photovoltaic cells are able to meet the energy needs to enable fully power-autonomous low-power RF backscatter applications of the future. We conclude with an outlook into a range of applications that we envision to leverage the synergies offered by combining perovskite photovoltaics and RFID.

In contrast to the strict requirements of Eruchin pledges found in Leviticus 27:1–8, Damim (monetary) donations are not closely regulated by the Torah and can take many forms. One may not, for example, offer an Erech that corresponds to some fraction of a human being, as Erech donations can be made only on an integer number of persons. To use language that could refer to anything but the entirety of a person renders the Erech attempt failed (Eruchin Chapter 5 Mishna 2). Damim is more flexible as the giver is free to offer almost any value, flexibly comprised. Beyond specifying a donation in the local currency, the Talmud quotes two cases where people offered either their entire value on the slave market, or the more complicated value of a particular body part such as a hand, which requires two evaluations to solve the equation ‘Value of one’s Hand’ = ‘Value of that Person’ – ‘Value of (Person without his Hand).’ A similar situation arises when one offers to contribute the value of his weight in a material. A simple measurement is needed when the entire weight is specified, but if it’s only the weight of a particular limb, the Mishna (Eruchin19a) details an elaborate procedure that involves measuring the volume of the limb through a water displacement exercise, and estimating the density of human flesh using a combination of donkey bone, blood and meat.

This paper presents a novel communication method for semi-passive RFID based sensors. The new method uses a digitally reconfigurable UHF RFID tag antenna to modulate sensed information at an RFID tag on to the received signal strength indicator (RSSI) response perceived at an RFID reader. This technique is completely compatible with the existing class 1 generation 2 UHF air interface protocol thereby enabling the use of existing RFID reader infrastructure to decode the additional sensed information. The effect of read distance, environment and bit duration on the performance of the communication method is examined through measurements obtained from a prototype. Through experimental verification, it is demonstrated that error free transmission of sensor information can be achieved up to 3.5 meters in different environments with a bit duration of 500 ms. Prospective future research directions are also discussed.

Demonstrations of focusing with metamaterial lenses have predominantly featured two dimensional structures or stacks of planar elements, both limited by losses which hinder realized gain near the focal region. In this study, we present a plano-concave lens built from a 3D self-supporting metamaterial structure featuring a negative refractive index between 10 and 12 GHz. Fabricated using macroscopic layered prototyping, the lens curvature, negative index and low loss contribute to a recognizable focus and free space gains above 13 dB.

Demonstrations of focusing with metamaterial lenses have predominantly featured two dimensional structures or stacks of planar elements, both limited by losses which hinder realized gain near the focal region. In this study, we present a plano-concave lens built from a 3D self-supporting metamaterial structure featuring a negative refractive index between 10 and 12 GHz. Fabricated using macroscopic layered prototyping, the lens curvature, negative index and low loss contribute to a recognizable focus and free space gains above 13 dB.

While of increasing importance for the real-time enterprise, deployments of Internet of Things infrastructures such as RFID remain complex and expensive. In this paper, we illustrate these challenges by studying the applications of the EPC Network which is an RFID standards framework that aims to facilitate interoperability and application development. We show how the use of blueprints that were successful on the Web can help to make the adoption of these standards less complex. We discuss in particular how Cloud Computing, RESTful interfaces, Real-time Web (Websockets and Comet) and Web 2.0 Mashups can simplify application development, deployments and maintenance in a common RFID application. Our analysis also illustrates that RFID/EPC Network applications are an excellent play- ground for Web of Things technologies and that further re- search in this eld can signi cantly contribute to making real-world applications less complex and cost-intensive.

Authentication schemes relying on per-packet signature and per-signature verification introduce heavy cost for computation and communication. Due to its constraint resources, smart grid's authentication requirement cannot be satisfied by this scheme. Most importantly, it is a must to underscore smart grid's demand for high availability. In this paper, we present an efficient and robust approach to authenticate data aggregation in smart grid via deploying signature aggregation, batch verification and signature amortization schemes to less communication overhead, reduce numbers of signing and verification operations, and provide fault tolerance. Corresponding fault diagnosis algorithms are contributed to pinpoint forged or error signatures. Both experimental result and performance evaluation demonstrate our computational and communication gains.

Radio Frequency Identification (RFID) technology allows automatic data capture from tagged objects moving in a supply chain. This data can be very useful if it is used to answer traceability queries, however it is distributed across many different repositories, owned by different companies. Discovery Services (DS) are designed to assist in retrieving the RFID data relevant for traceability queries while enforcing sharing policies that are defined and required by participating companies to prevent sensitive data from being exposed. In this paper we define an interface for Supply Chain Authorization (SC-Az) and describe the implementation of two visibility restriction mechanisms based on Access Control Lists (ACLs) and Capabilities. Both approaches were converted to the standard eXtensible Access Control Markup Language (XACML) and their correctness and performance was evaluated for supply chains with increasing size.

Previous studies have shown that many consumer goods manufacturers believe that their promotions are not well executed in retail stores. One common concern is that in-store promotions are not synchronized with television and online advertising campaigns. In recent years, RFID has been actively promoted as a tool to improve in-store promotion execution. This paper presents the results of a study in which promotional displays were tagged with RFID tags and tracked as they moved from a retail distribution center to a number of different retail stores. The analysis shows that only 28% of all displays are placed on the shop floor within +/-3 days of the official promotion launch. Our analysis suggests that by adjusting the timing of the delivery to the store the performance could be improved such that 87% of all displays are present on the shop floor within this time frame. The results confirm that RFID is a useful diagnostics tool to measure promotion execution performance and to identify business process shortcomings. The results also suggest that a continuous measurement via RFID is not required to improve performance.

Radio-frequency identification (RFID) has been well established as an effective technology for track and trace applications. In this paper, we go beyond the ID in RFID, and discuss the potential for RFID tags to be used as low-cost sensors by mapping a change in some physical parameter of interest to a controlled change in RFID tag antenna electrical properties. We will also show that it is possible to design the tag antenna to suffer a permanent change in case of violation of a critical threshold in the parameter of interest thereby creating a low-cost threshold sensing mechanism. This can be achieved by inducing controlled changes to the tag antenna geometry parameters or to the antenna boundary conditions, in effect creating a nonelectric memory to monitor state. After identifying the application space for which this class of sensing is well suited, we present details into the design and testing of three different kinds of sensors based on this sensing paradigm. We demonstrate how we use this concept to sense displacements, temperature thresholds, and fluid levels. We will show that RFID-tag-antenna-based sensing has the potential to revolutionize application domains in which there is a need for low-cost, long-lasting, ubiquitous sensors.

The following topics are dealt with: tag circuits; measurement method; simulation method; health care; protocol; sensors; open source RFID software; privacy; security; reader design; antenna; propagation; near-field electromagnetic ranging.

Security and privacy issues are critically important in RFID networks because EPC data and their associated business events are valuable assets. In this paper, an Interoperable Internet-Scale Security (IISS) framework for RFID networks is proposed. The IISS framework performs authentication and authorization based on an aggregation of business rules, enterprise information, and RFID tag information. IISS provides a protocol for several authentication schemes and identity techniques. It also provides an engine for reasoning over business rules from different domains. Moreover, the IISS framework is able to resolve provenance information of RFID tags, which can identify the history of a particular piece of EPC data through the supply chain. The IISS framework and the IISS ontologies to model the information in RFID networks are also described, and how the IISS framework can be developed for access control in RFID enabled supply chains is discussed. Finally, the IISS framework's efficiency is tested using a supply chain EPC simulator as the testing platform, which allows optimization of the IISS protocol's performance.

adio-frequency identification (RFID) poses a number of research challenges, such as interference mitigation, throughput optimization and security over the RF channel. A number of new approaches to address these issues have been proposed recently, but due to the highly integrated nature of passive RFID tags, it is difficult to evaluate them in real-world scenarios. In this paper, we present an RFID simulation engine, RFIDSim, which implements the ISO 18000-6C communication protocol and supports pathloss, fading, backscatter, capture, and tag mobility models. This paper also shows that our implementation of RFIDSim that relies on a discrete event simulator can be used to simulate large populations featuring thousands of RFID tags. RFIDSim also simulates the deep fades that lead to frequent power losses of the battery-less RFID tags by modeling the multipath effects statistically.