Abstract: A field deployable sensing device includes a housing with a permeable skin or holes on its outer surface and at least one sensor embedded within or affixed to the housing. The sensor is configured to respond to the presence of gases or volatile substances. The device may be thrown, launched, or deployed by unmanned vehicles, and can withstand impact forces. Multiple sensor types may be included to detect various environmental hazards, including gases, vapors, aerosols, and radiation. The device may include data storage, transmission capabilities, and visual indicators for real-time monitoring. Additional features may include self-righting mechanisms, neutralizing agent dispensers, and multi-chamber designs for independent environmental sampling.

Abstract: The disclosed sensor comprises multiple types of graphene ink. The graphene ink may be applied via individual layers, where each layer is of a different type of graphene. Additionally, the graphene ink may be applied via a layer where the graphene ink is a mixture of two or more types of graphene. In either scenario, conductive patches of graphene and low conductivity interstitial carbon material may be created in the resulting material of the sensor. The low conductivity interstitial carbon material bridges the large, conductive patches of graphene, providing a connection between them. The graphene ink includes a first type of graphene configured for conductivity and a second type of graphene configured for wear and tear resistance. The sensor can be a resonant sensor, a vapor or gas sensor, a biosensor, or a printed label sensor, among others.

Abstract: Disclosed herein is a sensors-as-a-service ecosystem. In use, the system includes functions for receiving first sensor data at a sensors as a service platform, where the first sensor data corresponds to a first level of capabilities for a first sensor. The system also receives a selection of a sensor upgrade for the first sensor and provisions enhanced sensor capabilities for the sensor upgrade based on the selection. Furthermore, the system sends a sensor update with the enhanced sensor capabilities from the sensors as a service platform to the first sensor. Finally, the system receives second sensor data from the first sensor at the sensors as a service platform, where the second sensor data corresponds to a second level of capabilities for the first sensor.

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

Abstract: Disclosed herein is a sensors-as-a-service ecosystem. In use, the system includes functions for receiving first sensor data at a sensors as a service platform, where the first sensor data corresponds to a first level of capabilities for a first sensor. The system also receives a selection of a sensor upgrade for the first sensor and provisions enhanced sensor capabilities for the sensor upgrade based on the selection. Furthermore, the system sends a sensor update with the enhanced sensor capabilities from the sensors as a service platform to the first sensor. Finally, the system receives second sensor data from the first sensor at the sensors as a service platform, where the second sensor data corresponds to a second level of capabilities for the first sensor.

Abstract: Resonant sensors for environmental health risk detection are disclosed. A mechanical member may include at least one meso-scale or micro-scale resonator disposed on a surface of the mechanical member. Additionally, the at least one meso-scale or micro-scale resonator may include a plurality of first carbon particles configured to uniquely resonate in response to an electromagnetic ping based at least in part on a concentration level of the first carbon particles within the at least one meso-scale or micro-scale resonator. Further, the at least one meso-scale or micro-scale resonator may be configured to resonate at a first frequency in response to the electromagnetic ping when the mechanical member is in a first state, and may be configured to resonate at a second frequency in response to the electromagnetic ping when the mechanical member is in a second state.

Abstract: Disclosed herein is a sensors-as-a-service ecosystem. In use, the system includes functions for receiving first sensor data at a sensors as a service platform, where the first sensor data corresponds to a first level of capabilities for a first sensor. The system also receives a selection of a sensor upgrade for the first sensor and provisions enhanced sensor capabilities for the sensor upgrade based on the selection. Furthermore, the system sends a sensor update with the enhanced sensor capabilities from the sensors as a service platform to the first sensor. Finally, the system receives second sensor data from the first sensor at the sensors as a service platform, where the second sensor data corresponds to a second level of capabilities for the first sensor.

Abstract: Resonant sensors for environmental health risk detection are disclosed. A mechanical member may include at least one meso-scale or micro-scale resonator disposed on a surface of the mechanical member. Additionally, the at least one meso-scale or micro-scale resonator may include a plurality of first carbon particles configured to uniquely resonate in response to an electromagnetic ping based at least in part on a concentration level of the first carbon particles within the at least one meso-scale or micro-scale resonator. Further, the at least one meso-scale or micro-scale resonator may be configured to resonate at a first frequency in response to the electromagnetic ping when the mechanical member is in a first state, and may be configured to resonate at a second frequency in response to the electromagnetic ping when the mechanical member is in a second state.

Abstract: A resonant sensor is embedded within or applied to a component of a medical diagnostic apparatus. The resonant sensor is formed from a composite material. The resonant sensor undergoes a change of permittivity and/or change in permeability due to metabolic activity of a microorganism that is involved in the medical diagnostic and proximal to the resonant sensor. The medical diagnostic apparatus may be a blood culture bottle that is configured to contain a blood culture medium. The resonant sensor may be embedded in or applied to the exterior or interior wall of the blood culture bottle. The resonant sensor may undergo a change in permittivity and/or a change in permeability due to production of carbon dioxide by the microorganism. The composite material may comprise a carbonaceous material such as graphene.

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

Abstract: Resonant sensors for environmental health risk detection are disclosed. A mechanical member may include at least one meso-scale or micro-scale resonator disposed on a surface of the mechanical member. Additionally, the at least one meso-scale or micro-scale resonator may include a plurality of first carbon particles configured to uniquely resonate in response to an electromagnetic ping based at least in part on a concentration level of the first carbon particles within the at least one meso-scale or micro-scale resonator. Further, the at least one meso-scale or micro-scale resonator may be configured to resonate at a first frequency in response to the electromagnetic ping when the mechanical member is in a first state, and may be configured to resonate at a second frequency in response to the electromagnetic ping when the mechanical member is in a second state.

Abstract: Resonant sensors for environmental health risk detection are disclosed. An adhesive may include at least one meso-scale or micro-scale resonator embedded within a material that comprises at least a portion of the adhesive. The at least one meso-scale or micro-scale resonator may be formed from a composite material. Additionally, the at least one meso-scale or micro-scale resonator may include a plurality of first carbon particles configured to uniquely resonate in response to an electromagnetic ping based at least in part on a concentration level of the first carbon particles within the at least one meso-scale or micro-scale resonator.

Abstract: A disclosed component may include at least one split-ring resonator, which may be embedded within a material. The split ring resonator may be formed from a three-dimensional (3D) monolithic carbonaceous growth and may detect an electromagnetic ping emitted from a user device. The split ring resonator may generate an electromagnetic return signal in response to the electromagnetic ping. The electromagnetic return signal may indicate a state of the material in a position proximate to a respective split ring resonator. In some aspects, the split-ring resonator may resonate at a first frequency in response to the electromagnetic ping when the material is in a first state, and may resonate at a second frequency in response to the electromagnetic ping when the material is in a second state. A resonant frequency of the 3D monolithic carbonaceous growth may be based on physical characteristics of the material.

Abstract: Resonant sensors for environmental health risk detection are disclosed. An adhesive may include at least one meso-scale or micro-scale resonator embedded within a material that comprises at least a portion of the adhesive. The at least one meso-scale or micro-scale resonator may be formed from a composite material. Additionally, the at least one meso-scale or micro-scale resonator may include a plurality of first carbon particles configured to uniquely resonate in response to an electromagnetic ping based at least in part on a concentration level of the first carbon particles within the at least one meso-scale or micro-scale resonator.

Abstract: A disclosed component may include at least one split-ring resonator, which may be embedded within a material. The split ring resonator may be formed from a three-dimensional (3D) monolithic carbonaceous growth and may detect an electromagnetic ping emitted from a user device. The split ring resonator may generate an electromagnetic return signal in response to the electromagnetic ping. The electromagnetic return signal may indicate a state of the material in a position proximate to a respective split ring resonator. In some aspects, the split-ring resonator may resonate at a first frequency in response to the electromagnetic ping when the material is in a first state, and may resonate at a second frequency in response to the electromagnetic ping when the material is in a second state. A resonant frequency of the 3D monolithic carbonaceous growth may be based on physical characteristics of the material.

Abstract: A disclosed vehicle component may include at least one split-ring resonator, which may be embedded within a material. The split ring resonator may be formed from a three-dimensional (3D) monolithic carbonaceous growth and may detect an electromagnetic ping emitted from a user device. The split ring resonator may generate an electromagnetic return signal in response to the electromagnetic ping. The electromagnetic return signal may indicate a state of the material in a position proximate to a respective split ring resonator. In some aspects, the split-ring resonator may resonate at a first frequency in response to the electromagnetic ping when the material is in a first state, and may resonate at a second frequency in response to the electromagnetic ping when the material is in a second state. A resonant frequency of the 3D monolithic carbonaceous growth may be based on physical characteristics of the material.