Abstract: The present application discloses a batch-type image distribution method based on an IPFS, which includes: obtaining a list of cluster nodes needed to download images; calculating a caching ratio of an image layer of images to be distributed of each node, comparing the caching ratio of each node with a threshold of a preset node, and obtaining a complete image from an image repository; adding the node with the complete image to a list of source nodes, and adding remaining nodes needed to download images to a list of demand nodes, and constructing an IPFS network, and exporting and sharing the image; calculating target demand nodes using an optimization algorithm, and inputting the target demand nodes into the IPFS network for image download, and importing the downloaded image into a container and starting the container.

Abstract: The present application discloses an external-cavity diode laser based on a Voigt anomalous dispersion atomic optical filter. The laser includes: a laser diode coated with an anti-reflective coating, where the laser diode is provided with a temperature control module thereon; a collimation module, configured to perform collimation and expanding for the laser diode; the Voigt anomalous dispersion atomic optical filter, configured to filter laser light emitted from the laser diode, where a spectrum of laser light output from the laser depends on a transmission spectrum of the atomic optical filter; a reflective cavity mirror, configured to reflect the laser light filtered by the atomic optical filter back into a resonant cavity to form oscillation and produce stable laser light; a piezoelectric ceramic, assembled with the reflective cavity mirror and configured to fine-tune a cavity length of the laser to achieve tuning of the laser light.

Abstract: A method of reconstruction of super-resolution of video frame includes inputting a first video frame with a first resolution and a plurality of consecutive frames thereof into a pre-trained super-resolution reconstruction network, and outputting, by the pre-trained super-resolution reconstruction network, a second video frame with a second resolution corresponding to the first video frame. The second resolution is higher than the first resolution. The super-resolution reconstruction network includes a feature extraction subnetwork, a spatial-temporal non-local alignment subnetwork, an attention progressive fusion subnetwork, and an up-sampling subnetwork which are connected in sequence.

Abstract: A multidimensional resource scheduling method in a Kubernetes cluster architecture system is provided. For a computing-intensive service, each server node in the cluster is scored according to CPU idleness and memory idleness; for an ordinary service, each server node in the cluster is scored according to resource requirements of a scheduling task, a resource priority of each server node and resource balance of each server node. The pod scheduling task is bound to a server node with a highest score for execution. This scheduling method meets diverse resource requests of various services, thereby enhancing the flexibility and expandability of the system.

Abstract: The present disclosure discloses an Internet-of-Things-oriented machine learning container image download system and a method. The Internet-of-Things-oriented machine learning container image download system includes a master node and a plurality of computing nodes; the master node is configured to store and convert a machine learning model, and build a machine learning container image from the format-converted machine learning model; and issue an image download instruction to each of the computing nodes after image information of the machine learning container image is completely built; and each of the computing nodes is configured to receive the image download instruction, download the machine learning container image, and start a machine learning container; and receive data collected by Internet-of-Things devices, and return a data processing result to the Internet-of-Things devices.

Abstract: A container image management system for distributed clusters, the system including at least one master node and at least one worker node. The at least one master node includes a container image database, a request input module and a container image management module. The container image management module is responsive when the container image management module establishes the connection to the container image database, then it is configured to perform a read/write operation on the container image database. The container image database is a distributed database configured to store node information of the at least one master node and the at least one worker node in the container image management system. The request input module is configured to receive request content including a request destination and command execution content. The command execution content includes an execution operation field and an executed container image list.

Abstract: Disclosed in the present invention are a distributed container image construction scheduling system and method. The system includes a construction node and a management node. The construction node includes an image constructor for executing a construction task issued by the management node. The management node includes a console and a scheduler. The console is responsible for acquiring the relevant parameters such as a development dependency library and system configuration required by a user, and generating tasks with these parameters and sending same to the scheduler. The scheduler is used for receiving the tasks sent by the console, detecting the legitimacy of the tasks, and sending the tasks to the corresponding construction node to be run.

Abstract: The present disclosure provides a deep neural network (DNN)-based reconstruction method and apparatus for compressive video sensing (CVS). The method divides a video signal into a key frame and a non-key frame. The key frame is reconstructed by using an existing image reconstruction method. The non-key frame is reconstructed by using a special DNN according to the present disclosure. The neural network includes an adaptive sampling module, a multi-hypothesis prediction module, and a residual reconstruction module. The neural network makes full use of a spatio-temporal correlation of the video signal to sample and reconstruct the video signal. This ensures low time complexity of an algorithm while improving reconstruction quality. Therefore, the method in the present disclosure is applicable to a video sensing system with limited resources on a sampling side and high requirements for reconstruction quality and real-time performance.

Abstract: A distributed container scheduling method includes: monitoring a container creation event in a Kubernetes API-Server in real time, and validating a container created once a new container creation event is detected; updating a container scheduling queue with containers passing the validation; when the container scheduling queue is empty, performing no operation until the containers passing the validation are added to the queue; when the container scheduling queue is not empty, reading the containers to be scheduled from the container scheduling queue in sequence, and selecting, from a Kubernetes cluster, an optimal node corresponding to the containers to be scheduled to generate a container scheduling two-tuple; and scheduling, based on the container scheduling two-tuple, the containers to be scheduled to the optimal node to finish the distributed container scheduling operation.

Abstract: A method of reconstruction of super-resolution of video frame includes inputting a first video frame with a first resolution and a plurality of consecutive frames thereof into a pre-trained super-resolution reconstruction network, and outputting, by the pre-trained super-resolution reconstruction network, a second video frame with a second resolution corresponding to the first video frame. The second resolution is higher than the first resolution. The super-resolution reconstruction network includes a feature extraction subnetwork, a spatial-temporal non-local alignment subnetwork, an attention progressive fusion subnetwork, and an up-sampling subnetwork which are connected in sequence.

Abstract: Disclosed in the present invention are a distributed container image construction scheduling system and method. The system includes a construction node and a management node. The construction node includes an image constructor for executing a construction task issued by the management node. The management node include, a console and a scheduler. The console is responsible for acquiring the relevant parameters such as a development dependency library and system configuration required by a user, and generating tasks with these parameters and sending same to the scheduler. The scheduler is used for receiving the tasks sent by the console, detecting the legitimacy of the tasks, and sending the tasks to the corresponding construction node to be run.

Abstract: The present disclosure provides a deep neural network (DNN)-based reconstruction method and apparatus for compressive video sensing (CVS). The method divides a video signal into a key frame and a non-key frame. The key frame is reconstructed by using an existing image reconstruction method. The non-key frame is reconstructed by using a special DNN according to the present disclosure. The neural network includes an adaptive sampling module, a multi-hypothesis prediction module, and a residual reconstruction module. The neural network makes full use of a spatio-temporal correlation of the video signal to sample and reconstruct the video signal. This ensures low time complexity of an algorithm while improving reconstruction quality. Therefore, the method in the present disclosure is applicable to a video sensing system with limited resources on a sampling side and high requirements for reconstruction quality and real-time performance.

Abstract: A multidimensional resource scheduling method in a Kubernetes cluster architecture system is provided. For a computing-intensive service, each server node in the cluster is scored according to CPU idleness and memory idleness; for an ordinary service, each server node in the cluster is scored according to resource requirements of a scheduling task, a resource priority of each server node and resource balance of each server node. The pod scheduling task is bound to a server node with a highest score for execution. This scheduling method meets diverse resource requests of various services, thereby enhancing the flexibility and expandability of the system.

Abstract: A container image management system for distributed clusters, the system including at least one master node and at least one worker node. The at least one master node includes a container image database, a request input module and a container image management module. The container image management module is responsive when the container image management module establishes the connection to the container image database, then it is configured to perform a read/write operation on the container image database. The container image database is a distributed database configured to store node information of the at least one master node and the at least one worker node in the container image management system. The request input module is configured to receive request content including a request destination and command execution content. The command execution content includes an execution operation field and an executed container image list.

Abstract: A system for testing thermal response time of an uncooled infrared focal plane detector array and a method therefor is provided. The system comprises: a blackbody, a chopper, a detector unit under test and a testing system. The method comprises: emitting radiation by the blackbody, chopping by the chopper, then radiating the radiation to the uncooled infrared focal plane detector array under test; generating different responses on the radiation at different chopping frequencies by the uncooled infrared focal plane detector array under test; collecting different response values of the uncooled infrared focal plane detector array under test at different chopping frequencies; obtaining response amplitude at a corresponding frequency in a frequency domain by FFT; fitting according to a formula Rv ? ( f ) = Rv ? ( 0 ) 1 + ( 2 ? ? ? ? f ? ? ? ) 2 to obtain the thermal response time.

Abstract: A system for testing thermal response time of an uncooled infrared focal plane detector array and a method therefor is provided. The system comprises: a blackbody, a chopper, a detector unit under test and a testing system. The method comprises: emitting radiation by the blackbody, chopping by the chopper, then radiating the radiation to the uncooled infrared focal plane detector array under test; generating different responses on the radiation at different chopping frequencies by the uncooled infrared focal plane detector array under test; collecting different response values of the uncooled infrared focal plane detector array under test at different chopping frequencies; obtaining response amplitude at a corresponding frequency in a frequency domain by FFT; fitting according to a formula Rv ? ( f ) = Rv ? ( 0 ) 1 + ( 2 ? ? ? ? f ? ? ? ) 2 to obtain the thermal response time.

Abstract: A light splitting module for obtaining spectrums of an object to be tested is disclosed, which sequentially includes a light entrance window, a diffuser and a filter array along a light entrance direction, wherein the filter array is an angle modulated filter array which has multiple subareas and includes multiple filters with different center wavelengths respectively corresponding to the subareas. Also, a dual-mode multiplexing optical device is disclosed, which includes the light splitting module, an illumination module and a light field imaging module, can realize the integration of spectral detection and light field imaging, so it can be applied to material spectral detection, digital image detection and digital focusing for obtaining high-resolution imaging results; and simultaneously, the modules of the device are detachable, so that users can use the device as required.

Abstract: A light splitting module for obtaining spectrums of an object to be tested is disclosed, which sequentially includes a light entrance window, a diffuser and a filter array along a light entrance direction, wherein the filter array is an angle modulated filter array which has multiple subareas and includes multiple filters with different center wavelengths respectively corresponding to the subareas. Also, a dual-mode multiplexing optical device is disclosed, which includes the light splitting module, an illumination module and a light field imaging module, can realize the integration of spectral detection and light field imaging, so it can be applied to material spectral detection, digital image detection and digital focusing for obtaining high-resolution imaging results; and simultaneously, the modules of the device are detachable, so that users can use the device as required.

Abstract: A first image, associated with a first tag, and/or other images may be presented to a user. A user behavior of the user in regards to the first image may reduce or increase a quality score of the first image. A quality metric of the first image may be determined, and may be used to decrease or increase the quality score of the first image. A rank may be assigned to the first image based upon the modified quality score. The first image may be provided to users based upon the rank.