Abstract: A machine-readable symbol reader can capture a cubic data set of an object carrying a machine-readable symbol. The machine-readable symbol reader can include a hyperspectral sensor, and the cubic data set may include image data for the machine-readable symbol within multiple, different wavelengths of the electromagnetic spectrum. The cubic data may be separated into portions that are each analyzed by specific processors, and those separate analyses can be used to increase the capabilities and efficiency compared to known machine-readable symbol readers.

Abstract: Systems and methods for applying and detecting cross dependent marks incorporated into an electronic or digital image to form a watermark. The electronic or digital image may include encoded information for example a machine-readable symbol. The watermarking may include an encoding and insertion sub-process that inserts one or more marks into an image at a first point in time for form a marked image, an extraction sub-process that extracts the marks at a second point in time, and a detection sub-process 108 that determines if any modifications have been made to the marked image. The marked image may be formed by determining a first original descriptor and first original mark within the image, determining a second original descriptor and second original mark within the image, and incorporating the first original mark into the second original descriptor and incorporating the second original mark into the first original descriptor.

Abstract: Embodiments disclosed herein describe systems and methods for authenticating a new device to operate on a network using peer-to-peer protocol key exchange. An existing network node sharing common secret seed information may initially exchange public keys with the new device. After the initial exchange, the network node and the new device may exchange one or more protocol messages. A received protocol message may include a pseudo-random number generated based upon a challenge position in a previously sent protocol message. If the network node determines a match between the received pseudo-random number at the challenge position and a locally generated pseudo-random number, the network node may authenticate the new device to the network.

Abstract: Systems and methods for providing reliability and redundancy of data for networked nodes (e.g., sensors and/or actuators) is provided. Each sensor may operate as network node in the sensor network that may operate as a peer to peer (P2P) consensus network. Each network node may maintain its local copy of a data chain and may generate a data block for updating the data chain. After a threshold number of network nodes generate the same consensus block from the data block, each network node may update its local data chain by appending the consensus block thereto.

Abstract: Embodiments disclosed herein describe systems and methods for authenticating a new device to operate on a network using peer-to-peer protocol key exchange. An existing network node sharing common secret seed information may initially exchange public keys with the new device. After the initial exchange, the network node and the new device may exchange one or more protocol messages. A received protocol message may include a pseudo-random number generated based upon a challenge position in a previously sent protocol message. If the network node determines a match between the received pseudo-random number at the challenge position and a locally generated pseudo-random number, the network node may authenticate the new device to the network.

Abstract: Systems and methods for providing reliability and redundancy of data for networked nodes (e.g., sensors and/or actuators) is provided. Each sensor may operate as network node in the sensor network that may operate as a peer to peer (P2P) consensus network. Each network node may maintain its local copy of a data chain and may generate a data block for updating the data chain. After a threshold number of network nodes generate the same consensus block from the data block, each network node may update its local data chain by appending the consensus block thereto.

Abstract: Systems, methods, and computer-readable storage media are provided for an embedded, scalable, predictive tool capable of detecting in-field anomalies and trends in advance of productivity losses on single devices, device clusters, and/or multi-cluster architectures. In-field and in real-time, sets of barcode signal sequences associated with respective barcode symbols are collected in time series (that is, at successive time intervals). A quality index measure in computed for each of the barcode signal sequence sets such that each quality index measure is associated with a barcode symbol. Patterns among the sets are identified therefrom and compared to barcode symbol patterns that are known to be associated with particular trends or anomalies and appropriately classified as such.

Abstract: Systems and methods for applying and detecting cross dependent marks incorporated into an electronic or digital image to form a watermark. The electronic or digital image may include encoded information for example a machine-readable symbol. The watermarking may include an encoding and insertion sub-process that inserts one or more marks into an image at a first point in time for form a marked image, an extraction sub-process that extracts the marks at a second point in time, and a detection sub-process 108 that determines if any modifications have been made to the marked image. The marked image may be formed by determining a first original descriptor and first original mark within the image, determining a second original descriptor and second original mark within the image, and incorporating the first original mark into the second original descriptor and incorporating the second original mark into the first original descriptor.

Abstract: Systems, methods, and computer-readable storage media are provided for an embedded, scalable, predictive tool capable of detecting in-field anomalies and trends in advance of productivity losses on single devices, device clusters, and/or multi-cluster architectures. In-field and in real-time, sets of barcode signal sequences associated with respective barcode symbols are collected in time series (that is, at successive time intervals). A quality index measure in computed for each of the barcode signal sequence sets such that each quality index measure is associated with a barcode symbol. Patterns among the sets are identified therefrom and compared to barcode symbol patterns that are known to be associated with particular trends or anomalies and appropriately classified as such.