Abstract: Systems, methods, and data storage devices for image grouping in an end user device using trained machine learning group classifiers are described. The end user device may include an image group classifier configured to classify new image data objects using an image classification algorithm and set of machine learning parameters previously trained for a specific image group. The end user device may determine embeddings that quantify features of the target image object and use those embeddings and the image group classifier to selectively associate group identifiers with each new image data object received or generated by the end user device. Calibration, including selection and training, of the image group classifiers and ranking of classified images are also described.

Abstract: An archival data storage system library includes magnetic-recording disk media, a storage enclosure in which the media are housed, data storage devices (DSDs) configured to write to and read from the media, an automated disk handling mechanism configured to transfer at least some of the media between the storage enclosure and the DSD, and a hermetic enclosure containing the foregoing components. The storage system may further include a gas circulation system for filtering contaminants from the hermetic enclosure. Such a storage system may be implemented as a rack-mountable unit, or as an entire cleanroom facility, as well as in intermediate form factors between those. In the context of a cleanroom storage system, the system may further include an automated disk handling shuttle to receive the media from the disk handling mechanism and an automated disk loading mechanism for accessing the media from the shuttle for installing into the DSD.

Abstract: A digital video camera architecture for updating an object identification and tracking model deployed with the camera is disclosed. The invention comprises optics, a processor, a memory, and an artificial intelligence logic which may further comprise artificial neural networks. The architecture identifies objects according to a first confidence threshold of the model and identifies candidate objects according to the first confidence threshold and a second confidence threshold. The model may track the motion of the candidate objects within a visual field, separate the candidate objects into false positive candidate objects and false negative candidate objects according to their tracked motions, and present at least a portion of the false positive candidate objects and false negative candidate objects for further annotation.

Abstract: A digital video camera architecture for updating an object identification and tracking model deployed with the camera is disclosed. The invention comprises optics, a processor, a memory, and an artificial intelligence logic which may further comprise artificial neural networks. The architecture may identify objects according to a confidence threshold of a model. The confidence threshold may be monitored over time, and the model may be updated if the confidence threshold drops below an acceptable level. The data for retraining is ideally generated substantially internal to the camera. A filter is generated to process the entire field data set stored on the camera to create a field data subset also stored on the camera. The field data subset may be run through the model to generate cases that may be used in further monitoring, training, and updating of the model.

Abstract: A removable disk clamp assembly for mounting disk media on a motor-driven spindle of a magnetic read-write device includes a disk clamp and a mechanism for generating a predetermined force to press the disk media to a flange of the spindle such that the predetermined force can be repeatedly overcome by an applied counterforce to remove the disk clamp from engagement with a hub of the spindle. Such a mechanism may include a spring-loaded removable disk clamp assembly or a magnetic removable disk clamp assembly, and whereby a vacuum-driven chuck may be employed for disk and clamp handling purposes, all of which are suitable for implementation and use in a read-write device configured for use in an archival data storage system library such as in a cleanroom storage system.

Abstract: A magnetic read-write device includes a disk spindle on which disk media are rotatably mounted, an enclosure in which the spindle is housed, an opening/closing mechanism such as a cover configured for opening to provide access to the spindle and for closing to contain particles that may be generated during operation of the device, and a seal configured for sealing the enclosure when the opening/closing mechanism is closed. The device may further include a pre-loaded hinge coupling the cover with the enclosure, and a loading mechanism such as a spring coupled with the hinge and configured for biasing the cover to a closed position and for applying a sealing force to the seal at an interface of the cover and the enclosure. This type of device may be implemented for use in an archival data storage system library such as in a cleanroom storage system.

Abstract: An archival data storage system library includes magnetic-recording disk media, a storage enclosure in which the media are housed, data storage devices (DSDs) configured to write to and read from the media, an automated disk handling mechanism configured to transfer at least some of the media between the storage enclosure and the DSD, and a hermetic enclosure containing the foregoing components. The storage system may further include a gas circulation system for filtering contaminants from the hermetic enclosure. Such a storage system may be implemented as a rack-mountable unit, or as an entire cleanroom facility, as well as in intermediate form factors between those. In the context of a cleanroom storage system, the system may further include an automated disk handling shuttle to receive the media from the disk handling mechanism and an automated disk loading mechanism for accessing the media from the shuttle for installing into the DSD.

Abstract: A device configured for more efficiently processing video images within a set of video image data to detect objects is described herein. The device may include a processor configured to execute a neural network such as a convolutional neural network. The device can receive video image data from a plurality of cameras, such as stationary cameras. The device can acquire a set of sample images from a stationary camera and submit them to a specialized neural network for processing to generate an attention mask. The attention mask can be generated from a variety of methods and is applied to each of the subsequently acquired images form the camera to narrow down areas where the convolutional neural network should process data. The application of attention masks to images within video image data creates masked images that can be processed to detect objects with much greater accuracy and fewer computational resources required.

Abstract: Systems and methods described herein provide for the use and storage of intermediate layer data within a neural network processing system. A first neural network, such as an object detection neural network may receive and process raw video image data to generate output utilized for metadata creation. Secondary neural networks may be configured to accept input data from one or more intermediate layers of the first neural network instead of the raw video image data. In this way, the initial data processed by the intermediate layers of the first neural network can be stored and utilized as a shortcut for processing additional features or attributes within the video image data. This alleviates the need to process video image data multiple times in different neural networks. The intermediate layer data can be stored in a different and often cheaper storage system and recalled faster and with fewer resources for future use.

Abstract: Systems and methods for site-based calibration of object detection values, such as for surveillance video cameras, are described. Video data from a video image sensor may be processed using an object detector to determine object data and a confidence score for a detected object. The object data and confidence score may be post-processed to apply calibration values based on the camera location to one or more parameters used for determining detection events. Event notifications may be sent for detection events. The calibration values may be determined from a calibration period where a verification object detector is used to verify the object detections and failure analysis is applied to determine calibration values for the camera location.

Abstract: A data storage device including, in one implementation, a non-volatile memory device having a memory block including a number of memory dies, and a controller coupled to the non-volatile memory device. The controller is configured to monitor a temperature of the data storage device and determine whether the monitored temperature exceeds a first temperature threshold. The controller is also configured to perform a default thermal throttling operation based on the monitored temperature exceeding the first temperature threshold, determine whether the monitored temperature exceeds a second temperature threshold, and perform a customized thermal throttling operation based on the monitored temperature exceeding the second temperature threshold.

Abstract: Systems and methods for processing surveillance video streams using image classification and object detection are described. Video data from a video image sensor may be processed using an image classifier to determine whether an object type is present in a video frame. If the object type is present, the video frame and/or subsequent video frames may be processed using an object detector to provide additional object data, such as position information, for use in other video surveillance processes. In some examples, an event message may be generated and sent to a video surveillance application in response to selective object detection.

Abstract: Systems and methods for surveillance video streams with embedded object data from another video camera are described. At least two video cameras are configured with fields of view to provide images of an object from alternative views. Video data for a primary video stream is received from one camera and secondary object data for the object from the other camera is embedded in the primary video stream. The primary video stream is sent to an analytics engine for processing the primary video and embedded secondary object data, such as performing facial recognition on a better image of a human face and/or feature vectors therefrom that are embedded in the primary video stream.

Abstract: A device configured for more efficiently processing video images within a set of video image data to detect objects is described herein. The device may include a processor configured to execute a neural network such as a convolutional neural network. The device can receive video image data from a plurality of cameras, such as stationary cameras. The device can acquire a set of sample images from a stationary camera and submit them to a specialized neural network for processing to generate an attention mask. The attention mask can be generated from a variety of methods and is applied to each of the subsequently acquired images form the camera to narrow down areas where the convolutional neural network should process data. The application of attention masks to images within video image data creates masked images that can be processed to detect objects with much greater accuracy and fewer computational resources required.

Abstract: An example system and method may provide for important data files to be stored in differently reliable storage medium based upon an importance score that is representative of the importance of the data file. A data file importance score is generated based on the data file. The data file is stored in a first data storage zone of a storage medium in a data storage device, in response to the data file importance score being greater than a data file importance threshold. The data file is stored in a second data storage zone of the storage medium in the data storage device, in response to the data file importance score being less than the data file importance threshold.

Abstract: A data storage system may include multiple data storage devices, such as solid-state drives, an enclosure housing the devices, and a thermal bridge positioned in a gap between and in contact with each of the enclosure and a device, where the enclosure is cooler than the device. Thus, heat is conducted away from a hot spot of the device and to the enclosure. The thermal bridge may be flexible enough to bridge different sized gaps, while stiff enough to generate contact forces applied to the enclosure and the device. For example, the thermal bridge may be constructed primarily of copper and configured to function like a compression spring.

Abstract: An example system and method may provide an importance score for a data file based on the content of the data file. An importance score may be used to determine whether to store the data file in a regular reliability storage media or in a higher reliability storage media. A controller generates a document vector for a data file based on content processed from a data file. The data file includes metadata and the content. The controller generates, using an artificial intelligence (AI) model and the document vector, a data file importance score for the data file. The controller then stores the data file in one of the first data storage zone and the second data storage zone based on the data file importance score.

Abstract: Systems and methods for surveillance video streams with embedded object data from another video camera are described. At least two video cameras are configured with fields of view to provide images of an object from alternative views. Video data for a primary video stream is received from one camera and secondary object data for the object from the other camera is embedded in the primary video stream. The primary video stream is sent to an analytics engine for processing the primary video and embedded secondary object data, such as performing facial recognition on a better image of a human face and/or feature vectors therefrom that are embedded in the primary video stream.

Abstract: A data storage device is disclosed comprising a non-volatile storage medium (NVSM), and a head configured to access the NVSM. During a first interval, the head is used to write first data to a first segment of the NVSM, and during a second interval, the head is used to read the first data from the first segment of the NVSM and erase at least part of the first data from the first segment of the NVSM.

Abstract: This disclosure relates to an apparatus for object detection. The apparatus comprises a video camera, an object detector, and a controller. The video camera may be configured to generate a video stream of frames. The object detector may be configured to accept the video stream as input data and to perform object detection. The controller may be coupled to the video camera and the object detector. The controller may be configured to manage object detection in order to satisfy a performance metric and/or operate within an operational constraint.