Abstract: A fuzz testing apparatus transmits a plurality of packets to the device under test (DUT), and calculates a probability of abnormal state, a normal response time and an abnormal-state-processing time of the DUT for the packets. Then, the fuzz testing apparatus estimates a maximum quantity of packets that can be sent to the DUT according to the probability of abnormal state the normal response time and the abnormal-state-processing time corresponding to the standby time of the DUT. Finally, the fuzz testing apparatus determines a plurality of test packets from a plurality of candidate test packets according to the maximum quantity of packets.

Abstract: A user equipment (UE) and a base station (BS) for a mobile communication system are provided. The UE obtains a first timing advance (TA) value of a source BS, and receives a radio resource control (RRC) connection reconfiguration message for a handover from the source BS to a target BS. The UE calculates a receiving time difference between a receiving time of two synchronization signals respectively transmitted by the source BS and the target BS, and calculates a reference time difference according to first reference time information of the source BS and second reference time information of the target BS. The UE calculates a second TA value of the target BS based on the first TA value, the receiving time difference and the reference time difference.

Abstract: A model building system, a quality prediction system, and a quality management system using the same are provided. The quality management system includes the quality prediction system, a measurement device, and a quality inspection device. The quality prediction system includes the model building system and a model application device. The model building system includes a data transformation device and a model training device. The data transformation device transforms raw measured data, including measurement properties of products, to sets of cluster data and multi-dimensional metadata corresponding to the measurement properties. The model training device creates a prediction model according to the cluster data, the quality inspection results, and the multi-dimensional metadata corresponding to a portion of the products, and then modifies structural parameters of the prediction model according to the raw measured data and the quality inspection results corresponding to the other portion of the products.

Abstract: A relay node and method for encapsulating a packet based on a tunneling protocol. The relay node includes a communication device, a storage device, and a processor. The communication device communicates with a receiving node and a transmitting node; the storage device stores multiple instructions; and the processor is coupled to the communication device and the storage device for loading and executing the multiple instructions stored in the storage device to: control the communication device to receive a packet transmitted by the transmitting node; generate a protocol header related to the packet based on the packet, and calculate a checksum as a checksum block in the multiple sections using multiple sections in the protocol header at least; generate an encapsulated packet including the protocol header and the packet; and transmit the encapsulated packet to the receiving node through the communication device for verifying the checksum block.

Abstract: A measurement system for positioning accuracy of a robotic arm includes the robotic arm, a computing device, a robotic arm controlling device, a first magnetic element and a second magnetic element. The robotic arm controlling device is electrically connected to the robotic arm and the computing device. The first magnetic element is disposed on a robotic arm. The second magnetic element is disposed on a fixed platform. One of the first magnetic element and the second magnetic element is electrically connected to the computing device. The robotic arm controlling device controls the robotic arm to move the first magnetic element above the second magnetic element to generate a magnetic field. The computing device is configured to calculate a plurality of movement error information of the first magnetic element in the magnetic field, and count the plurality of movement error information to obtain a positioning accuracy of the robot arm.

Abstract: A virtual and real image fusion method is disclosed. The method comprises the following operations: obtaining a picture of a three dimensional space by a first camera, in which the picture comprises a screen picture and a tag picture of an entity tag and the screen picture is projected on the entity tag; obtaining a corresponding point data of the entity tag on the screen picture according to the picture by a processor; obtaining a spatial correction parameter according to the corresponding point data by the processor; and displaying an image on the screen picture according to the spatial correction parameter by the processor.

Abstract: An abnormal packet detection apparatus and method are provided. The abnormal packet detection apparatus stores a whitelist corresponding to a protocol port, wherein the whitelist includes at least one legal packet record. Each legal packet record includes a legal packet length, a legal source address, and a legal variation position set, and corresponds to a reference packet. The abnormal packet detection apparatus determines that a current packet length and a current source address of a to-be-analyzed packet are respectively the same as the legal packet length and the legal source address of a reference packet record among the at least one legal packet record, determines a current variation position of the to-be-analyzed packet by comparing the to-be-analyzed packet with the reference packet corresponding to the reference packet record, and generates a detection result by comparing the current variation position with the legal variation position set of the reference packet record.

Abstract: A classification model building apparatus and a classification model building method thereof are provided. The classification model building apparatus introduces a clustering algorithm to assist in training a deep learning model for classification and takes a sum of a clustering loss function, a center concentration loss function and a classification loss function multiplied by different weights, respectively, as a total loss function for training the deep learning model. Based on the total loss function, the classification model building apparatus adjusts parameters of the deep learning model through a backpropagation algorithm to build a classification model.

Abstract: A traffic condition prediction system and a traffic condition prediction method are disclosed. The method includes: determining a center of circle in a surveillance image of a target traffic scene; determining a first circle based on the center of circle and a first radius; extracting a plurality of first feature points along the circumference of the first circle according to a first preset sampling frequency; generating a scene feature of the target traffic scene at least based on the first feature points; determining whether the scene feature and a scene feature of another traffic scene are similar; and when determining that they are similar, predicting traffic condition of the target traffic scene through a prediction model used for predicting traffic condition of the other traffic scene. The scene feature of the target traffic scene and that of the other traffic scene are generated in a same way.

Abstract: The present disclosure relates to an imaging system and a detection method. The detection method includes the following steps. Receiving, by a processing unit of the image system, multiple recognition label data sets transmitted from multiple terminal devices. Determining a matching degree value between the recognition label data sets and an image data, and obtaining multiple weight values from a storage unit corresponding to the terminal devices. Setting the weight values and the corresponding matching degree values as multiple label points, and classifying the marker points into multiple cluster groups by a clustering algorithm. Calculating a centroid of the largest cluster group. The centroid of the largest cluster group corresponds to a clustering weight value and a clustering matching value. When the clustering weight value or the clustering matching value meets an adjustment condition, adjusting a neural network unit according to the largest cluster group.

Abstract: An action evaluation model building apparatus and an action evaluation model building method thereof are provided. The action evaluation model building apparatus stores a plurality of raw data sets and a plurality of standard action labels corresponding thereto. Based on machine learning algorithms, the action evaluation model building apparatus computes the raw data sets and performs a supervised learning to build a feature vector creation model and a classifier model. The action evaluation model building apparatus determines a representation action feature vector of each standard action label by randomly generating a plurality of action feature vectors and inputting them into the classifier model. The action evaluation model building apparatus builds an action evaluation model based on the feature vector creation model, the classifier model and the representation action feature vectors.

Abstract: A system for producing test data produces fuzzing data at least according to first test data which is used for testing at least one first device and second test data which is used for testing a second device. Then, the system transmits the fuzzing data to the second device so as to test the second device.

Abstract: A fitness equipment control system is provided. The system includes a receiver and a mobile apparatus, and performs the operations: generating a first control signal based on a training course of a user; transmitting the first control signal to the receiver to make the receiver generates a signal which is readable by the fitness equipment according to the first control signal and transmit the readable signal to the fitness equipment; receiving at least one physiological signal from at least one of the wearable devices worn by the user; based on the training course and the at least one physiological signal, generating a second control signal; and when the second control signal is generated, transmitting the second control signal to the receiver, wherein the receiver generates a signal which is readable by the fitness equipment according to the second control signal and transmits it to the fitness equipment.

Abstract: A data filtering device stores a piece of standard track data and a piece of target track data. The piece of standard track data corresponds to a standard track in a six degree-of-freedom (DoF) virtual space constructed by a plurality of virtual space units, and the piece of target track data corresponds to a target track in the six-DoF space. The data filtering device determines whether a degree of similarity between the standard track and the target track is lower than a threshold of similarity. When determining that the degree of similarity is lower than the threshold of similarity, the data filtering device classifies the piece of target track data as an invalid data.

Abstract: A security monitoring apparatus and method for a vehicle network are provided. The apparatus transmits an indicator and an encryption key to a plurality of electronic control units via the controller area network interface. The apparatus receives a response code from each electronic control unit via the controller area network interface, wherein each of the response codes is generated by a serial number of each electronic control unit and the encryption key via a hash algorithm. The apparatus compares the response code returned by each electronic control unit according to a list, the encryption key and the hash algorithm to determine whether each electronic control unit correctly returns the response code. The apparatus determines to generate an alert signal when one of the electronic control units does not correctly return the response code.

Abstract: An image recognition system includes a cloud server that stores a cloud recognition model and a local server that stores a local recognition model. The local server recognizes an image through the local recognition model to generate a local recognition result. The image corresponds to a field, the image includes at least one object, and the local recognition result includes local category confidence scores of each object corresponding to each of a plurality of categories. For each object, the local server calculates a local entropy indicator according to the field and the corresponding local category confidence scores. The local server determines to transmit the image to the cloud server according to the local entropy indicators of the image. The cloud server recognizes the image through the cloud recognition model to generate a cloud recognition result, and updates the local recognition model according to the cloud recognition result.

Abstract: A device and a method for detecting a purpose of an article are provided. The device is configured to divide the article into a plurality of sentences and input the sentences to a feature identification model to generate a contextualized word vector corresponding to each of the sentences. The device further inputs the representation to a specific purpose detecting model to generate a distributed representation similarity of the article. When the distributed representation similarity of the article is greater than a threshold, the device determines that the article conforms to a specific purpose.

Abstract: A motion recognition apparatus extracts a plurality of first motion feature vectors within a period of time, and extracts a plurality of second motion feature vectors within another period of time closely next to the period of time. The apparatus calculates and sequences the distances between each of the plurality of first motion feature vectors and the plurality of cluster centroids to generate a corresponding first cluster centroid sequence respectively, and calculates and sequences the distances between each of the plurality of second motion feature vectors and the plurality of cluster centroids to generate a corresponding second cluster centroid sequence respectively. The apparatus recognizes a first segment motion, a second segment motion, and a whole motion based on the number of appearances of the cluster centroid in the first cluster centroid sequences, the second cluster centroid sequences and both of them respectively.

Abstract: A base station (BS) and a user equipment (UE) for a mobile communication system are provided. The BS transmits a downlink reception indication message, including a serving source configuration and a downlink parameter setting for signal combination, to the UE. The serving source configuration indicates a plurality of serving sources. The BS generates a first downlink signal and a second downlink signal based on a piece of downlink information. The first downlink signal carries a first part of the piece of downlink information and the second downlink signal carries a second part of the piece of downlink information. The BS further transmits the first downlink signal to the UE through the first serving source and transmits the second downlink signal to the UE through the second serving source.

Abstract: A base station and user equipment with prioritized transmission considerations are provided. The base station generates an RRC configuration message for a user equipment. The RRC configuration message includes a first information element regarding a PHY layer, wherein the first information element is related to a DCI format for a specific indication, and the specific indication is related to a prioritized transmission mode. The RRC configuration message further includes a second information element regarding a MAC layer, wherein the second information element defines a transmission restriction of each of a plurality of logical channels, and at least one of the transmission restrictions corresponds to the prioritized transmission mode. The base station further transmits the RRC configuration message to the user equipment. The user equipment receives the RRC configuration message from the base station and reads the first information element and the second information element from the RRC configuration message.