Hon Hai Precision Industry Co., Ltd.

Hong-fu Jin Precision Industry (shenzhen) Co., Ltd.

Fu Tai Hua Industry (shenzhen) Co., Ltd.

Foxconn Technology Group Co., Ltd.

Jung-Yi Lin

Chin-Pin Kuo

Young-Way Liu

I-Hua Chen

Chih-Te Lu

Chung-Yu Wu

Tzu-Chen Lin

Chieh Lee

Chih-Yuan Chuang

Nien-Che Li

Chien-Min Chou

Po-Jen Hsu

Shu-Yuan Chang

A method for directionally warning as to a target object behind a vehicle acquires images of environment surrounding the vehicle. Objects in the environment images are identified and high-risk target object or objects from all the objects is labeled. A relative direction and a relative location of the target object with the transportation are confirmed. A warning device is adjusted based on the relative direction and the relative location. The warning device gives directional warning as to the target object. A collision risk warning system and a transportation applying the method are also disclosed.

Method for collision warnings specifying direction, system, and transportation applying method

A method for avoiding a collision between an autonomous mobile apparatus and a stationary or moving object initially deemed to be a collision threat includes acquiring a passage clearance of the object, from the point of view of the autonomous mobile apparatus, by establishing in two dimensions the nearest and the furthest physical parts of the object in the planned path of the autonomous mobile apparatus. Dimensions between the current position of the autonomous mobile apparatus and the at least two boundaries are computed as relative distances, and are considered as avoidance distances when the relative distances are greater than the passage clearance. For only one avoidance distance, a target boundary for clearance with safety is set based on the avoidance distance. The autonomous mobile apparatus moves along a specified direction to aim at the target boundary. An autonomous mobile apparatus applying the method is also disclosed.

Method for automatic avoidance of obstacles by mobile apparatus and autonomous mobile apparatus applying method

A system and method for controlling multi-party communication during video conferences communicatively connects with at least two participating terminals, each manned by one participant in the conference. A voice acquisition module acquires speech and video at each participating terminal. A recognition module acquires information as to an action state of a participant in the video and audio of what participant is saying, and determines whether the action of participant includes speaking movements and whether the audio includes what is being said by the participant. A control module can control the volume of other participating terminals and adjust the volumes of microphones of simultaneous speakers, to be louder, fainter, or muted, depending on assigned priority of each participant, for orderly control of conferences and as an overall recording.

System and method for controlling multi-party communication

A method for adjusting point cloud density, an electronic device, and a storage medium are provided. In the method an initial point cloud map and a distance determination threshold of a robot are obtained. A plurality of target regions in the initial point cloud map are determined, and an environmental complexity value of each target region is calculated. The initial point cloud map is divided into submaps, and a point cloud density coefficient of each submap is determined. The initial point cloud map is adjusted according to the point cloud density coefficient and the target point cloud map is obtained. By utilizing such method, adjustment efficiency and an accuracy of point cloud density can be improved.

Method for adjusting point cloud density, electronic device, and storage medium

A data generating method includes obtaining first sample data, determining a type of the first sample data and a corresponding data expansion method, expanding the first sample data according to the determined data expansion method to generate second sample data, and dividing the first sample data and the second sample data into a training set and a verification set according to a preset rule. A data model is trained according to the training set, and the data model is verified according to the verification set after training.

Data generating method, and computing device and non-transitory medium implementing same

An image marking method includes acquiring an image and at least one feature to be marked in the image, searching for at least one target area matching the at least one feature to be marked in the image, and marking the at least one target area with a preset graphic. The feature corresponds to the preset graphic.

Image marking method and computing device implementing image marking method

A method for assisting a driver to drive a vehicle in a safer manner includes capturing images of road in front of the vehicle, and identifying a traffic sign in the images. A first image frame is captured at a first time and a second image frame is captured at a later second time when the images do comprise the traffic sign. A change in size or other apparent change of the traffic sign from the first image frame to the second image frame is determined, and conformity or non-conformity with a predetermined rule is then determined. The traffic sign can be analyzed and recognized to trigger the vehicle to perform an action accordingly when conformity is found. A device providing assistance with driving is also provided.

Method for driving assistance and mobile device using the method

A computing device includes a processor and a memory. The processor is configured to acquire a voice instruction through at least two voice receiving devices, analyze the voice instruction to determine at least one display device controlled by the voice instruction, generate a control instruction according to the voice instruction, and send the control instruction to the at least one display device to cause the at least one display device to display corresponding contents according to the voice instruction.

Display content control method, computing device, and non-transitory storage medium

A method for detecting defects in manufactured objects includes acquiring a high-resolution image of an object for test, dividing the image into a plurality of smaller sub-images and determining, by a first model, whether each of the small sub-images is similar to a preset template image. The test object is determined to be flawless, when each of the sub-images is found to be similar to a template image. When sub-images are not found sufficiently similar to template images, determining, by a second model, whether a defect is shown to exist within each sub-image, and if so the test object is declared defective. The longer application of the second model is only applied if testing for defects is not resolved by the application of the first model. An electronic device and a computer readable storage medium are also provided.

Method for detecting defects, electronic device, and computer readable medium

A voice recognition system includes a computing device and at least one mobile terminal communicatively coupled to the computing device through a network. The computing device obtains an original sound from the at least one mobile terminal and converts the original sound into a digitized time-frequency map, performs compression segmentation on the time-frequency map to obtain a sound image corresponding to the time-frequency map, and uses an image recognition method to recognize the sound image, obtain an enhanced sound image, and search a preset database for sound information corresponding to the enhanced sound image.

Sound recognition system and method

A method for classifying images by applying probabilities in steps includes selecting image category corresponding to an image classifier with the first probability value top ranked from the first layer image classifier set when the first layer image classifier set cannot classify an image to be identified. A second layer image classifier set is established according to the selected image category. The method selects output of the image classifier with the largest second probability value from the second layer image classifier set as result of classification when the second probability value of at least one image classifier is greater than or equal to a second predetermined value.

Method for classifying images and electronic device employing the method

A method for detecting an obstacle applicable in an electronic device includes detecting whether at least one object is within a line of sight of an image capturing device. The image capturing device is controlled to capture a first image of the object and the image capturing device is caused to move until a capturing angle for capturing another image of the object is changed. The image capturing device is controlled to capture a second image of the object and a determination is made as to whether the object in the first image is the same as the object in the second image. For such recognized objects, the object is determined to be a non-planar obstacle when the object in the first image is not the same as the object in the second image.

Electronic device and method for detecting obstacle

An identity authentication device includes a communication unit configured to establish communication with a plurality of online servers, a processor, and a storage configured to store a user profile database including a number of user profiles, wherein each of the user profiles corresponds to an online browsing habit. The processor receives an online browsing record of a user, compares the received online browsing record to the online browsing habits of the user profiles stored in the user profile database to determine whether the received online browsing record matches one of the online browsing habits, and determines whether the received online browsing record belongs to a same user of a matched one of the user profiles according to a result of comparison.

Identity authentication device and method

A cloud device, a terminal device, and an image classification system and method includes the terminal reading an image to be classified; transmitting an instruction of image classification to the cloud device; determining whether the instruction of image classification comprises an intermediate operation result of the first partial operation; performing the second partial operation to obtain an image classification result, when the instruction of image classification comprises intermediate operation result of the first partial operation; and transmitting the image classification result to the terminal device; performing the first partial operation to obtain the intermediate operation result, when the intermediate operation result is not contained in the instruction of image classification; transmitting the intermediate operation result to the terminal device; and performing the second partial operation to obtain an image classification result.

Cloud device, terminal device, and method for classifying images

A speech recognition device includes a speech acquiring unit, a speech outputting unit, a camera unit, and a processor. The processor obtains speech input acquired by the speech acquiring unit, obtains images acquired by the camera unit, chronologically links the obtained speech and the obtained images together, compares the obtained speech to a speech database to confirm matching speech and a confidence level of the matching speech, determines whether the confidence level of the matching speech exceeds a predetermined confidence level, and outputs the matching speech when the confidence level of the matching speech exceeds the predetermined confidence level.

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Taipei, Taiwan

Jung-Yi Lin