Abstract: Data is received describing a local model of a first device generated by the first device based on sensor readings at the first device and a global model is updated that is hosted remote from the first device based on the local model and modeling devices in a plurality of different asset taxonomies. A particular operating state affecting one or more of a set of devices deployed in a particular machine-to-machine network is detected and the particular machine-to-machine network is automatically reconfigured based on the global model.

Abstract: A set of samples are returned by radio frequency identifier (RFID) reader corresponding to the readings of signals emitted from a particular RFID tag, each sample including a respective set of features identifying values of the attributes of the signals as detected. At least some of the features are provided as inputs to a random forest of decision trees, each providing a prediction that the particular RFID tag is located in one of a plurality of defined zones in a particular environment. From outputs of the plurality of decision trees based on the set of samples, it can be determined that the particular RFID tag is located in a particular one of the plurality of zones at a first instance in time.

Abstract: A plurality of devices are detected within range of a particular device, and capabilities of each of the plurality of devices are determined, as well as a respective taxonomy to be associated with each device based on the device's capabilities. A set of asset abstractions are identified, referenced by a particular software application configured to manage a particular machine-to-machine network. Each asset abstraction can correspond to a respective one of the set of taxonomies, and the particular machine-to-machine network can be built from an instance of each one of the set of asset abstractions. A subset of the plurality of devices can be selected for deployment in the particular machine-to-machine network based on the associated taxonomies, the subset of devices representing an instance of each one of the set of asset abstractions. The subset of devices are then automatically deployed to implement the instance of the particular machine-to-machine network.

Abstract: A voice-monitoring system includes a monitoring center, at least one monitoring device, and a control module. The monitoring center is configured to respond to voice instructions. The control module is configured to receive the voice instructions from staff in monitoring center, convert the voice instructions to voice letters, and determine whether the voice instructions are legal. When the voice instructions are determined to be legal in terms of a recognized task and in terms of the authority of a voiceprint-recognized monitoring staff member, the control module controls the monitoring center to execute operations according to the control instructions, thus the voice-monitoring system can provide control of emergency operations when emergency happens. A voice-monitoring method is also provided.

Abstract: An adaptive video end-to-end network is described that uses local abstraction. One example includes an image sensor to generate a sequence of images, a processor coupled to the image sensor to analyze the sequence of images to detect an event, to select images related to the event and to generate metadata regarding the event, and a communications interface coupled to the processor to send the metadata information through a network connection to a central node.

Abstract: A lifting handle (1, 2) for enhancing the convenience of carrying containers. The lifting handle (1, 2) comprises: a base board (11, 21), the base board (11, 21) comprising two end edges (111, 211) and two side edges (112, 212); the two end edges (111, 211) form the two ends of the base board (11, 21), and the two ends of the two side edges (112, 212) respectively connect to the two end edges (111, 211); the base board (11, 21) is provided with at least one cutting part (12, 22), and the at least one cutting part (12, 22) extending from the position of one end edge (111, 211) near the base board (11, 21) to the position of the other end edge (111, 211) near the base board (11, 21), such that the base board (11, 21) forms two carrying parts (13, 23) and a lifting handle part (14, 24).

Abstract: An electron-beam lithography method includes, computing and outputting a development time of a positive-tone electron-sensitive layer and a parameter recipe of an electron-beam device by using a pattern dimension simulation system, performing a low-temperature treatment to chill a developer solution, utilizing an electron-beam to irradiate an exposure region of the positive-tone electron-sensitive layer based on the parameter recipe, and utilizing the chilled developer solution to develop a development region of the positive-tone electron-sensitive layer based on the development time. The development region is present within the exposure region, and an area of the exposure region is smaller than that of the first portion. As a result, the electron-beam lithography method may control a dimension of a development pattern of the positive-tone electron-sensitive layer more accurately, and may also shrink a minimum dimension of the development pattern of the positive-tone electron-sensitive layer.

Abstract: Various systems and methods for generating navigation route plans based on user intents are described herein. A navigation system is disclosed that generates route plans based on user intents. The system includes a search engine for retrieving location data. The location data includes geolocations and location constraints associated with the geolocations. The system also includes an input device to receive selections of a plurality of geolocations and user intents corresponding to the plurality of geolocations. The system further includes a route generator to generate a route plan including a sequence of the plurality of geolocations. The sequence is based at least in part on comparing the user intents to location constraints associated with the plurality of geolocations. The system additionally includes a display device to present the route plan.

Abstract: Systems, apparatuses and methods may receive, at a local Internet of Things (IOT) device, a request to deploy an IOT application. Additionally, the IOT application may be partitioned into a plurality of atomic nodes, wherein configuration information for the plurality of atomic nodes may be sent, at runtime, to a plurality of remote IOT devices having abstracted resources that support operation of the first plurality of atomic nodes. In one example, the configuration information is sent via a device independent message protocol having a universal namespace.

Abstract: Systems, apparatuses and methods may identify a capability abstraction in a request to configure a first Internet of Things (IOT) application in a physical environment including a plurality of IOT devices and select a resource abstraction from a plurality of resource abstractions based on the capability abstraction. The selected resource abstraction may correspond to a first IOT device in the plurality of IOT devices. Additionally, the first IOT application may be bound with the first IOT device. In one example, first data originating from the first IOT device is received, a first runtime abstraction is selected from a plurality of runtime abstractions, wherein the first runtime abstraction corresponds to the first IOT application, and the first data is sent to the first IOT application via the first runtime abstraction.

Abstract: A set of data is identified that includes a plurality of observed values generated by a plurality of sensor devices located in a plurality of different locations. For each of the plurality of observed values, a modality of the value, a spatial location of the value, and a timestamp of the value is determined. Values for one or more missing values in the set of data are determined from the modalities, spatial locations, and timestamps of the plurality of observed values.

Abstract: An embodiment includes at least one computer readable storage medium comprising instructions that when executed enable a system to: receive (a)(i) first radio signal location data for a first object from a radio sensor; and (a)(ii) first visual signal location data for the first object from a camera sensor; perform feature extraction on (b)(i) the first radio signal location data to determine first extracted radio signal features; and (b)(ii) the first visual signal location data to determine first extracted visual signal features; solve a first association problem between the first extracted radio signal features and the first extracted visual signal features to determine first fused location data; and store the first fused location data in the at least one computer readable storage medium. Other embodiments are described herein.

Abstract: A weighting value is determined for each of a plurality of decision trees in a random forest model hosted on a particular device, where the weighting is based on entropy of the respective decision tree. A new decision tree is received at the particular device and a weighting value is determined for the new decision tree based on entropy of the new decision tree. Based on the determined weighting value, it is determined whether to add the new the decision tree to the random forest model. A classification for data generated at the particular device is predicted using the random forest model.

Abstract: Example task assignment methods disclosed herein for video analytics processing in a cloud computing environment include determining a graph, such as a directed acyclic graph, including nodes and edges to represent a plurality of video sources, a cloud computing platform, and a plurality of intermediate network devices in the cloud computing environment. Disclosed example task assignment methods also include specifying task orderings for respective sequences of video analytics processing tasks to be executed in the cloud computing environment on respective video source data generated by respective ones of the video sources.

Abstract: An electron-beam lithography method includes, computing and outputting a development time of a positive-tone electron-sensitive layer and a parameter recipe of an electron-beam device by using a pattern dimension simulation system, performing a low-temperature treatment to chill a developer solution, utilizing an electron-beam to irradiate an exposure region of the positive-tone electron-sensitive layer based on the parameter recipe, and utilizing the chilled developer solution to develop a development region of the positive-tone electron-sensitive layer based on the development time. The development region is present within the exposure region, and an area of the exposure region is smaller than that of the first portion. As a result, the electron-beam lithography method may control a dimension of a development pattern of the positive-tone electron-sensitive layer more accurately, and may also shrink a minimum dimension of the development pattern of the positive-tone electron-sensitive layer.

Abstract: An anomaly detection model generator accesses sensor data generated by a plurality of sensors, determines a plurality of feature vectors from the sensor data, and executes a plurality of unsupervised anomaly detection machine learning algorithms in an ensemble using the plurality of feature vectors to generate a set of predictions. Respective entropy-based weightings are determined for each of the plurality of unsupervised anomaly detection machine learning algorithms from the set of predictions. A set of pseudo labels is generated based on the predictions and weightings, and a supervised machine learning algorithm uses the set of pseudo labels as training data to generate an anomaly detection model corresponding to the plurality of sensors.

Abstract: An adaptive video end-to-end network is described that uses local abstraction. One example includes an image sensor to generate a sequence of images, a processor coupled to the image sensor to analyze the sequence of images to detect an event, to select images related to the event and to generate metadata regarding the event, and a communications interface coupled to the processor to send the metadata information through a network connection to a central node.

Abstract: A system for locating a mobile device includes an input that accesses a plurality of scans of wireless network access signaling, where the scans indicate received signal measurement results. A similarity measure module executes comparisons between the data of different scans in order to assess the similarity between those scans. The comparisons produce multi-dimensional comparison results. A dimension reduction module reduces dimensionality of the multi-dimensional comparison results to produce a dimension-reduced set of comparison results. A clustering module identifies groupings of similar scans based on the dimension-reduced set of comparison results.

Abstract: A display device includes a substrate, display units, and a plurality of integrated circuits (ICs). The substrate includes an active area and a non-active area. The non-active area is located around the active area. The display units are disposed in the active area of the substrate, and arranged in a matrix. The ICs are disposed in the active area of the substrate, arranged in a matrix, and are electrically coupled to the display units. Each of the ICs includes a shift register unit. Each of the shift register units of the ICs is configured to receive a previous-stage scan signal, and generate a current-stage scan signal according to the previous-stage scan signal. The ICs drive the display units according to the current-stage scan signals.

Abstract: A system for locating a mobile device includes an input that accesses a plurality of scans of wireless network access signaling, where the scans indicate received signal measurement results. A similarity measure module executes comparisons between the data of different scans in order to assess the similarity between those scans. The comparisons produce multi-dimensional comparison results. A dimension reduction module reduces dimensionality of the multi-dimensional comparison results to produce a dimension-reduced set of comparison results. A clustering module identifies groupings of similar scans based on the dimension-reduced set of comparison results.