Abstract: Techniques are described for detecting and handling unauthenticated commands in a property monitoring system. In some implementations, a monitoring system may include sensors located throughout a property, a monitoring control unit, and an input device. The monitoring control unit may be configured to receive data collected by the sensors, as well as an input command detected by the input device. For an input command that does not include authentication information, the monitoring control unit may generate property state information based on the sensor data, then analyze the property state data and the input command against one or more rules that relate to authorization of unauthenticated commands. Based on the analysis, the monitoring control unit may determine whether to perform the action corresponding to the input command or whether to perform another action, for example, generating and providing a notification or authorization request to a user.

Abstract: A device includes a memory and a hardware processor communicatively coupled to the memory. The hardware processor determines that a computing device communicatively coupled to an access point performed an action with respect to the access point and in response to determining that the action causes a deviation from a multi-user uplink policy of the access point, transmits a disciplinary message to the computing device.

Abstract: The present disclosure provides systems and methods that leverage neural networks for high resolution image segmentation. A computing system can include a processor, a machine-learned image segmentation model comprising a semantic segmentation neural network and an edge refinement neural network, and at least one tangible, non-transitory computer readable medium that stores instructions that cause the processor to perform operations. The operations can include obtaining an image, inputting the image into the semantic segmentation neural network, receiving, as an output of the semantic segmentation neural network, a semantic segmentation mask, inputting at least a portion of the image and at least a portion of the semantic segmentation mask into the edge refinement neural network, and receiving, as an output of the edge refinement neural network, the refined semantic segmentation mask.

Abstract: Systems and methods are described for selecting a sound type of interest from a first (e.g., master/global) machine learning library comprising information derived from reference audio stream data acquired from a plurality of farm animal operation reference sound monitoring events, including from a first farm animal operation monitoring event of a first farm animal operation, wherein the sound type of interest is associated with a condition state of interest of a first collection of farm animals. Further, information associated with the selected sound type of interest can be included a second machine learning library, wherein the second machine learning library is operational on an edge computing device located in proximity to a second farm animal operation.

Abstract: A method for administering an exposure treatment of a cognitive behavioral therapy (CBT) uses a mobile app and a server application. A user state of a patient undergoing a first step of the CBT based on the patient's condition during the first step is detected by sensors of the patient's smartphone. A situational state of the patient's surroundings during the first step is detected by the smartphone sensors. The mobile app determines whether the patient has made progress performing the first step. A user prompt is generated based on the user state and situational state. A next step of the CBT is configured based on the user state and situational state. The characteristics of the user prompt are generated using machine learning based on past task completions by the patient and other users so as to increase the likelihood that the patient will complete the next step of the CBT.

Abstract: An example method includes determining, at an aggregated point in time, an aggregated space node motion amount detected in a plurality of node sensing areas of a plurality of the nodes. The determining the aggregated space node motion amount includes compiling a plurality of sensing records from a subset or all of the nodes created by the subset or all of the nodes at a time substantially close to the aggregated point in time. The method additionally includes computing a temporal aggregated motion amount detected in a node sensing area of a respective node over time, using the aggregated time node motion amount. The method further includes computing a spatial aggregated motion amount in a portion of a space, using the aggregated space node motion amount, wherein the portion of the space correlates to the node sensing areas of the plurality of nodes.

Abstract: Various embodiments provide systems and method for monitoring individuals.

Abstract: Methods and apparatus are described to automatically re-enable monitoring of a bypassed security sensor by a security system control device.

Abstract: A system and method for monitoring performance of a repeated activity is described. The system comprises a motion sensing system and a processing system. The motion sensing system includes sensors configured to measure or track motions corresponding to a repeated activity. The processing system is configured to process motion data received from the motion sensing system to recognize and measure cycle durations in the repeated activity. In contrast to the conventional systems and methods, which may work for repeated activities having a high level of standardization, the system advantageously enables recognition and monitoring of cycle durations for a repeated activity, even when significant abnormal motions are present in each cycle. Thus, the system can be utilized in a significantly broader set of applications, compared conventional systems and methods.

Abstract: Systems, devices, and methods related to a Deep Learning Accelerator and memory are described. An edge server may be configured on a local area network to receive sensor data of a person, such as a patient in a hospital or care center. The edge server may be implemented using an integrated circuit device having: a Deep Learning Accelerator configured to execute instructions with matrix operands; and random access memory configured to store first instructions of an Artificial Neural Network executable by the Deep Learning Accelerator and second instructions of a server application executable by a Central Processing Unit. An output of the Artificial Neural Network with the sensor data as input may identify a condition of the person, based on which the server application generates an alert, causing a central server to request intervention of the detected or predicted condition for the person.

Abstract: An intelligent defecation device for living creature includes a device body, a supporting portion, an image module, and a first analysis module. The supporting portion is formed within the inner side of the device body for accommodating a moisture absorption member so as to allow the living creature to leave over its excrement therein. The image module is also arranged at the device body for dynamically capturing the images of the excrement in the supporting portion and outputting the image. The first analysis module is arranged in the device body and connected with the image module to analyze and calculate the defecation mode with the image based on preset or accumulated data, so as to generate a signal when an abnormal defecation mode is diagnosed.

Abstract: A light fixture for a public transportation train system powers an antenna used for train signaling. The antenna can provide to the train a location marker associated with the location of the antenna. The antenna can provide to the train other data communicated to the light fixture through the light fixture communication system.

Abstract: Systems and methods for closed subscriber group identity (CSG ID) allocation for radio access network (RAN) sharing between different wireless telecommunication network providers can include a second CSG administration server receiving a unique first list of CSG IDs from a first CSG administration server, comparing the CSG IDs of the unique first list with a unique second list of CSG IDs associated with the second CSG administration server, and automatically adding to the unique second list ones of the CSG IDs from the unique first list that are absent from the unique second list. If a first one of the CSG IDs from the unique first list is already assigned in the unique second list, a new E-UTRAN Cell Global Identifier (ECGI) is added for use with the first one of the CSG IDs and sent therewith to the first CSG administration server to be added to the unique first list.

Abstract: The present disclosure provides a method, performed in an accessory device, for communicating a leakage state of an ostomy appliance, wherein the accessory device comprises an interface configured to communicate with one or more devices of an ostomy system, the one or more devices comprising a monitor device, and/or the ostomy appliance configured to be placed on a skin surface of a user. The method comprises obtaining monitor data from the one or more devices, the monitor data being indicative of presence of fluid at a proximal side of a first adhesive layer of the ostomy appliance towards the skin surface; determining a leakage state at the proximal side of the first adhesive layer of the ostomy appliance based on the monitor data, and communicating the leakage state of the ostomy appliance via the interface.

Abstract: The present disclosure presents at least one communication interface for prediction of data related to health conditions, wherein the at least one communication interface is configured to receive health-related data, preferably comprising measurement data, from a first user device of a first user, associated with a first time and a first location. The at least one communication interface additionally comprises at least one processor configured to utilize said received health-related data to generate predictive data to be indicated to a second user device of a second user associated with a second time and a second location.

Abstract: There are provided measures for enabling/realizing network slice discovery in an overlapping network slice deployment. Such measures exemplarily comprise registration of a network slice mapping between a network slice identification information and a network slice type information for at least one service-related network slice in an overlapping network slice deployment, said network slice type information being indicative of a service-related capability of the at least one service-related network slice, and identification of a service-related network slice in the overlapping network slice deployment on the basis of the registered network slice mapping and a service.

Abstract: A locating system includes a mobile smart device and/or central server to receive and process data; a device to be secured or assigned to a person or asset, the device having an RFID tag with communication technology to receive commands from one or more smart devices, such that location is updated without the need for real-time infrastructure.

Abstract: This brain activity feature amount extraction method includes acquiring a combination of a plurality of data items in order of higher effectiveness for discriminating whether a subject is a healthy person or a non-healthy person, among the plurality of data items, on the basis of the index value serving as the index indicating effectiveness or non-effectiveness for discriminating the group of healthy persons and the group of non-healthy persons from each other, and displaying the acquired combination of the plurality of data items in order of higher effectiveness.

Abstract: For establishing network connections via a trusted gateway function, a method includes: relaying a first set of messages of a first type between a remote unit and a mobile communication network (“MCN”) via a non-3GPP access, sending a connection setup request to the remote unit via the non-3GPP AN, and relaying subsequent messages of the first type between the remote unit and the MCN via the non-3GPP access, wherein the first set of messages are encapsulated with a first encapsulation protocol and wherein the first set of messages initiate a registration procedure to the MCN, wherein the connection request setup contains information for activating a second encapsulation protocol, wherein the subsequent messages are encapsulated with the second encapsulation protocol, and wherein the subsequent messages complete the registration procedure.

Abstract: Some embodiments are directed to a dermoscope and a method for forming an image of a part of a skin having one or more at least partly transparent structures, such as at least partly transparent hair. The dermoscope has a light source, a light shaping unit, and an image forming lens. The light source is arranged to provide light to the light shaping unit. The light shaping unit is arranged to direct at least part of the light via an immersion fluid to the part of the skin having the one or more at least partly transparent structures to be imaged at incident angles in the immersion fluid larger than 70 degrees relative to the optical axis of the dermoscope. The image forming lens is arranged to receive at least light refracted from the one or more at least partly transparent structures.