Abstract: Methods, systems, and devices for wireless communication are provided for mobility management for wireless communications systems that utilize a flexible bandwidth carrier. Some embodiments include approaches for determining bandwidth information, such as one or more bandwidth scaling factors N and/or flexible bandwidths, at a user equipment (UE), where the bandwidth information may not be signaled to the UE. Embodiments for determining bandwidth information include: random ordered bandwidth scaling factor approaches, delay ordered bandwidth scaling factor approaches, storing bandwidth scaling factor value in UE Neighbor Record approaches, spectrum measurement approaches, spectrum calculation approaches, and/or a priori approaches. Flexible bandwidth carrier systems may utilize spectrum portions that may not be big enough to fit a normal waveform.

Abstract: Cameras with large field-of-view lenses can cause significant geometric distortions of the images acquired. Training for object detection normally takes place on undistorted images. Thus, in order to detect objects of interest within the acquired images, an undistortion procedure is applied on the acquired images and an object detection is then performed on the undistorted images. Unfortunately, such undistortion procedure is too computationally expensive to be run on some edge devices. To remove the need to perform the undistortion procedure, it is proposed to train for object detection directly from distorted acquired images.

Abstract: Apparatuses, methods, and systems are presented for sensing scene-based occurrences. Such an apparatus may comprise a vision sensor system comprising a first processing unit and dedicated computer vision (CV) computation hardware configured to receive sensor data from at least one sensor array comprising a plurality of sensor pixels and capable of computing one or more CV features using readings from neighboring sensor pixels. The vision sensor system may be configured to send an event to be received by a second processing unit in response to processing of the one or more computed CV features by the first processing unit. The event may indicate possible presence of one or more irises within a scene.

Abstract: Use of pupillary response to visible light for iris authentication is disclosed. One example involves (a) capturing an initial image of an eye including an inner circular boundary between a pupil region and an iris region and an outer circular boundary between the iris region and a sclera region, (b) determining a first size measurement indicative of the inner circular boundary, (c) responsive to at least the first size measurement, modulating one or more visible light sources to output visible light toward the eye of the user, (d) capturing a subsequent image of the eye of the user during a period of pupillary response, (e) obtaining an iris data record of the user, and (f) comparing the iris data record of the user to one or more registered iris data records to authenticate the user.

Abstract: A small cell may include a base station that may be co-located with an edge computing device. The edge computing device may be integrated within a small cell base station or be a physically separate module communicatively coupled to and in close proximity with the small cell base station that provides edge computing resources at the small cell. The processing responsibilities associated with received data may be split or divided between the edge computing device and a target mobile device. For example, a portion of data processing may be performed by the edge computing device and the remaining portion of data processing may be performed by the target mobile device. The distribution of processing responsibilities may be based on radio link conditions or conditions (e.g., power state or processing capabilities) associated with the target mobile device.

Abstract: Methods, systems, and devices for exposure control are described, including capturing a first and second field of view with a first and second sensor. The techniques may include identifying a brightness difference and an exposure time difference between the first and second sensor, and capturing a first image and a second image, and outputting a third image including both the first and second image. Techniques may include determining an exposure bias, identifying a hypothesis total gain for the first sensor and a peer sensor total gain for the second sensor, and selecting a total gain for each sensor based on comparing the hypothesis total gain and the peer sensor total gain, and based on a maximum brightness difference between the two sensors. The total gain for each sensor may be adjusted to satisfy the maximum brightness difference and the exposure bias, or based on a region of interest.

Abstract: Pixel values may be obtained for a scene at two or more exposure levels, each exposure level corresponding to at least one of an exposure time interval different from that of another exposure level or an applied gain different from that of another exposure level. The pixel values may be aggregated into superpixel values, wherein each of the superpixel values includes a plurality of pixel values. A background model corresponding to each exposure level may be obtained. The superpixel values corresponding to each exposure level may be compared with the background model corresponding to each exposure level, to obtain an image change detection result for each exposure level. The image change detection results for each exposure level may be aggregated to obtain a combined image change detection result. A region of interest and/or a background region for the scene may be determined according to the combined image change detection result.

Abstract: Apparatuses, methods, and systems are presented for reacting to scene-based occurrences. Such an apparatus may comprise dedicated computer vision (CV) computation hardware configured to receive sensor data from a sensor array comprising a plurality of sensor pixels and capable of computing one or more CV features using readings from neighboring sensor pixels of the sensor array. The apparatus may further comprise a first processing unit configured to control operation of the dedicated CV computation hardware. The first processing unit may be further configured to execute one or more application programs and, in conjunction with execution of the one or more application programs, communicate with at least one input/output (I/O) device controller, to effectuate an I/O operation in reaction to an event generated based on operations performed on the one or more computed CV features.

Abstract: A small cell may include a base station that may be co-located with an edge computing device. The edge computing device may be integrated within a small cell base station or be a physically separate module communicatively coupled to and in close proximity with the small cell base station that provides edge computing resources at the small cell. The processing responsibilities associated with received data may be split or divided between the edge computing device and a target mobile device. For example, a portion of data processing may be performed by the edge computing device and the remaining portion of data processing may be performed by the target mobile device. The distribution of processing responsibilities may be based on radio link conditions or conditions (e.g., power state or processing capabilities) associated with the target mobile device.

Abstract: Systems and methods are disclosed for recognizing a multi-component gesture within a sequence of image frames. A method comprises selecting a first object model as the selected object model, wherein the first object model corresponds to a first component of the multi-component gesture, receiving a frame in the sequence of frames and analyzing the frame to determine whether the frame includes a first object that matches the selected first object model, selecting a second object model as the selected object model, wherein the second object model corresponds to a second component of the multi-component gesture, receiving at least one subsequent frame and analyzing the at least one subsequent frame to determine whether the at least one subsequent frame includes a second object that matches the selected second object model, and recognizing that the multi-component gesture is present in the sequence of image frames.

Abstract: Methods, systems, and devices for gesture detection are described. A device may identify a set of configured gestures and determine a set of minimum frame rates, where each minimum frame rate may correspond to at least one configured gesture in the set of configured gestures. The device may set an initial frame rate for a sensor based at least in part on the set of minimum frame rates. For example, the device may identify a maximum frame rate of the set of minimum frame rates and set the initial frame rate to be equal to or greater than the maximum frame rate. The device may capture a set of images using an initial frame rate and detect a configured gesture of the set of configured gestures based on the set of images.

Abstract: Techniques are presented for detecting a visual marker. A first image containing the visual marker may be captured at a first time under a first lighting condition. A first image-based detection for the visual marker may be performed based on the first image, using a first detector, to produce a first set of results. A second image containing the visual marker may be captured at a second time under a second lighting condition different from the first lighting condition. Based on the first set of results, a second image-based detection for the visual marker may be performed based on the second image, using a second detector different from the first detector, to produce a second set of results.

Abstract: Apparatus, methods, systems, and instructions stored on computer-readable medium are presented for performing classification. A hardware engine may be configurable to implement multiple stages of a cascade classifier including a first stage and a second stage. The hardware engine may be configurable to (a) access a value indicative of whether to accumulate, and (b) responsive to the value indicative of whether to accumulate meeting a continue evaluation condition, (i) access a first numeric value obtained from evaluation of the first stage of the cascade classifier, (ii) accumulate the first numeric value with a second numeric value obtained from evaluation of the second stage of the cascade classifier to generate an accumulated value, and (iii) utilize the accumulated value to determine an outcome for the second stage of the cascade classifier.

Abstract: Apparatus, methods, systems, and instructions stored on computer-readable medium are presented for performing classification. A hardware engine may be configurable to implement a plurality of cascade classifiers comprising at least a first cascade classifier and a second cascade classifier, each including one or more stages. The first cascade classifier may be configured to perform a classification different from a classification performed by the second cascade classifier. The hardware engine may be capable of conditionally branching from the first cascade classifier to the second cascade classifier based on an outcome condition associated with evaluating of at least one stage of the first cascade classifier.

Abstract: Methods, systems, computer-readable media, and apparatuses for image processing and utilization are presented. In some embodiments, an image containing at a face of a user may be obtained using a mobile device. An orientation of the face of the user within the image may be determined using the mobile device. The orientation of the face of the user may be determined using multiple stages: (a) a rotation stage for controlling a rotation applied to a portion of the image, to generate a portion of rotated image, and (b) an orientation stage for controlling an orientation applied to orientation-specific feature detection performed on the portion of rotated image. The determined orientation of the face of the user may be utilized as a control input to modify a display rotation of the mobile device.

Abstract: Pixel values may be obtained for a scene at two or more exposure levels, each exposure level corresponding to at least one of an exposure time interval different from that of another exposure level or an applied gain different from that of another exposure level. The pixel values may be aggregated into superpixel values, wherein each of the superpixel values includes a plurality of pixel values. A background model corresponding to each exposure level may be obtained. The superpixel values corresponding to each exposure level may be compared with the background model corresponding to each exposure level, to obtain an image change detection result for each exposure level. The image change detection results for each exposure level may be aggregated to obtain a combined image change detection result. A region of interest and/or a background region for the scene may be determined according to the combined image change detection result.

Abstract: Aspects of the present disclosure provide a scheduling entity and methods of operating the scheduling entity such that the scheduling entity reconfigures/allocates its resources based on device capabilities of the wireless devices. The scheduling entity utilizes a first resource configuration to provide communications service to one or more wireless devices associated with the scheduling entity. The scheduling entity determines a change of the one or more wireless devices, wherein the change include at least one of a capability change of a wireless device, a quality of service (QoS) requirement change of a wireless device, an addition of a wireless device, or a removal of a wireless device. Based on at least one of a predetermined time of a day or the determined change, the scheduling entity reconfigures to a second resource configuration to facilitate resource utilization of the first wireless cell.

Abstract: Disclosed is a method and apparatus for enhanced availability of network-assisted services. The method may include receiving, from at least one peripheral device, one or more provisioning requests to provide service to the at least one peripheral device. The method may also include determining whether to handle one or more provisioning requests based on capabilities of the mobile gateway. Furthermore, the method may include, in response to the determination to handle the one or more provisioning requests, requesting, from a server, at least one application based on the one or more provisioning requests, wherein the at least one application enables the mobile gateway to service the at least one peripheral device.

Abstract: Methods, systems, computer-readable media, and apparatuses for remote patient monitoring and event detection are presented. For example, one method includes receiving, by a computing device via wireless communication, one or more sensor signals from a sensor associated with a patient; obtaining a patient condition based on the one or more sensor signals using a trained machine-learning (“ML”) model; and responsive to detecting an emergency condition based on the patient condition, providing an indication of the emergency condition.

Abstract: Internet of Things (IoT) systems and related methods are disclosed. A method comprises determining whether a first condition holds, the first condition being a condition of the IoT system, performing a function of the IoT system in response to a trigger, wherein the trigger is a determination that the first condition holds, recognizing a gesture based on image data received from an image sensor, and reconfiguring the IoT system for a future performance of the function based on the user feedback data.