Systems and methods for detecting a waste receptacle, the system including a camera for capturing an image, a convolutional neural network, and processor. The convolutional neural network can be trained for identifying target waste receptacles. The processor can be mounted on the waste-collection vehicle and in communication with the camera and the convolutional neural network configured for using the convolutional neural network. The processor can be configured for using the convolutional neural network to generate an object candidate based on the image; using the convolutional neural network to determine whether the object candidate corresponds to a target waste receptacle; and selecting an action based on whether the object candidate is acceptable.

Systems and methods for detecting a waste receptacle, the system including a camera for capturing an image, a convolutional neural network, and processor. The convolutional neural network can be trained for identifying target waste receptacles. The processor can be mounted on the waste-collection vehicle and in communication with the camera and the convolutional neural network configured for using the convolutional neural network. The processor can be configured for using the convolutional neural network to generate an object candidate based on the image; using the convolutional neural network to determine whether the object candidate corresponds to a target waste receptacle; and selecting an action based on whether the object candidate is acceptable.

Provided are a latency prediction method and a computing device for the same. The latency prediction method includes receiving a deep learning model and predicting on-device latency of the received deep learning model using a latency predictor which is trained on the basis of a latency lookup table. The latency lookup table includes information on single neural network layers and latency information of the single neural network layers on an edge device.

Provided are a latency prediction method and a computing device for the same. The latency prediction method includes receiving a deep learning model and predicting on-device latency of the received deep learning model using a latency predictor which is trained on the basis of a latency lookup table. The latency lookup table includes information on single neural network layers and latency information of the single neural network layers on an edge device.

A method of optimizing an operating state of a vehicle includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, one or more effects of the classified social media data on the transportation system. The method further includes optimizing, using a third neural network of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

A generative system for the creation of digital images for printing on design surfaces comprises a training dataset comprising a plurality of sample images for printing on design surfaces, a generative adversarial network comprising a generator and a discriminator, wherein the generator receives noise at input and is trained to generate at output starting from the noise a new artificially generated image adapted to be used for printing on design surfaces, and wherein the discriminator receives at input the new artificially generated image and is trained to compare and distinguish the new image generated by the sample images of the training dataset.

Examples of methods for controlling machine learning model structures are described herein. In some examples, a method includes controlling a machine learning model structure. In some examples, the machine learning model structure may be controlled based on an environmental condition. In some examples, the machine learning model structure may be controlled to control apparatus power consumption associated with a processing load of the machine learning model structure.

A video recording method and a video recording device are provided. The method includes: obtaining video data to be recorded; dividing, based on the video data, each frame of the video data into a region of interest and a background region by using a preset neural network model; and encoding the region of interest of the video data based on a first encoding bit rate, and the background region based on a second bit rate, and storing the encoded video data into a storage device through a video buffer.

A video recording method and a video recording device are provided. The method includes: obtaining video data to be recorded; dividing, based on the video data, each frame of the video data into a region of interest and a background region by using a preset neural network model; and encoding the region of interest of the video data based on a first encoding bit rate, and the background region based on a second bit rate, and storing the encoded video data into a storage device through a video buffer.

The present invention relates to a method and system of cybersecurity; and particularly relates to an encryption method and system on the basis of cognitive computing for xenomorphic cryptography or unusual form of cryptography; said method comprises generating a Functional Neural Network or KeyNode (KN) of the system by programming a chain of multiple nodes also called Artificial Mirror Neurons (AMN) based on captured information of reaction time and emotional response to a simple task; racing the nodes in the Functional Neural Network or KeyNode (KN) as an encryption device or cipher for the time of use; generating a password at the time of use based on the sum of intrinsic values of the nodes in the racing network at this time and adopting the generated password for authentication. The present invention can be applied to secure online and mobile communication especially at the dawn of 5G with generalization of open API lifestyle platforms so as to allow real-time identification for digital cryptocurrency payments and other public distributed ledger technology (DLT) mechanisms.