Abstract: A method and system for activity classification. A pressure sensor receives input data resulting from physical activity of a subject performing an activity. The input data includes pressure data from at least one pressure sensor, and may include other data acquired through other types of sensors. A deep learning neural network is applied to the input data for identifying the activity. The neural network is trained with reference to training data from a training database. The training data may include empirical data from a database of previous data of corresponding activities, synthesized data prepared from the empirical data or simulated data. The training data may include data from physical activity of the subject being monitored by the system. Different aspects of the neural network may be trained with reference to the training data, and some aspects may be locked or opened depending on the application and the circumstances.

Abstract: A method and system for activity classification. A pressure sensor receives input data resulting from physical activity of a subject performing an activity. The input data includes pressure data from at least one pressure sensor, and may include other data acquired through other types of sensors. A deep learning neural network is applied to the input data for identifying the activity. The neural network is trained with reference to training data from a training database. The training data may include empirical data from a database of previous data of corresponding activities, synthesized data prepared from the empirical data or simulated data. The training data may include data from physical activity of the subject being monitored by the system. Different aspects of the neural network may be trained with reference to the training data, and some aspects may be locked or opened depending on the application and the circumstances.

Abstract: A method and system for heterogeneous event detection. Sensor data is obtained and divided into discrete data windows. Each data window is defined by and corresponds to a time period of the sensor data. A time-frequency representation over the time period is calculated for each data window. A filter mask is calculated based on the data window corresponding to the time-frequency representation. The filter mask is applied for reverting the time-frequency representation to a time representation, resulting in filtered data. Features, such as extrema or other inflection points, are identified in the filtered data. The features define events, and transforming the time-frequency representation back into the time domain emphasizes differences between more and less prominent frequencies, facilitating identification of heterogeneous events. The method and system may be applied to body movements of people or animals, automaton movement, audio signals, light intensity, or any suitable time-dependent variable.

Abstract: A method and system for activity classification. A pressure sensor receives input data resulting from physical activity of a subject performing an activity. The input data includes pressure data from at least one pressure sensor, and may include other data acquired through other types of sensors. A deep learning neural network is applied to the input data for identifying the activity. The neural network is trained with reference to training data from a training database. The training data may include empirical data from a database of previous data of corresponding activities, synthesized data prepared from the empirical data or simulated data. The training data may include data from physical activity of the subject being monitored by the system. Different aspects of the neural network may be trained with reference to the training data, and some aspects may be locked or opened depending on the application and the circumstances.

Abstract: A method and system for activity classification. A pressure sensor receives input data resulting from physical activity of a subject performing an activity. The input data includes pressure data from at least one pressure sensor, and may include other data acquired through other types of sensors. A deep learning neural network is applied to the input data for identifying the activity. The neural network is trained with reference to training data from a training database. The training data may include empirical data from a database of previous data of corresponding activities, synthesized data prepared from the empirical data or simulated data. The training data may include data from physical activity of the subject being monitored by the system. Different aspects of the neural network may be trained with reference to the training data, and some aspects may be locked or opened depending on the application and the circumstances.

Abstract: A method and system for heterogeneous event detection. Sensor data is obtained and divided into discrete data windows. Each data window is defined by and corresponds to a time period of the sensor data. A time-frequency representation over the time period is calculated for each data window. A filter mask is calculated based on the data window corresponding to the time-frequency representation. The filter mask is applied for reverting the time-frequency representation to a time representation, resulting in filtered data. Features, such as extrema or other inflection points, are identified in the filtered data. The features define events, and transforming the time-frequency representation back into the time domain emphasizes differences between more and less prominent frequencies, facilitating identification of heterogeneous events. The method and system may be applied to body movements of people or animals, automaton movement, audio signals, light intensity, or any suitable time-dependent variable.

Abstract: A flow stopper preventing the backflow of urine comprises a urinary catheter connector, a flow stopping valve, and a urinary bag connector. The flow stopping valve is a membrane material. A flow channel runs through the front and rear ends of the flow stopping valve. The flow channel is provided with a front-end inlet and a rear-end outlet. The front-end inlet is in communication with a valve port of the urinary catheter connector. The rear-end outlet is a flat slit. A channel of the urinary bag connector is provided with a front-end port and a rear-end port connected to a tube of a urinary bag. The flow stopping valve is arranged within the channel. Moreover, the rear-end outlet of the flow channel faces the rear-end port of the channel. The flow stopper can prevent the backflow of the urine and prevent a patient from being infected.

Abstract: A method and system for activity classification. A pressure sensor receives input data resulting from physical activity of a subject performing an activity. The input data includes pressure data from at least one pressure sensor, and may include other data acquired through other types of sensors. A deep learning neural network is applied to the input data for identifying the activity. The neural network is trained with reference to training data from a training database. The training data may include empirical data from a database of previous data of corresponding activities, synthesized data prepared from the empirical data or simulated data. The training data may include data from physical activity of the subject being monitored by the system. Different aspects of the neural network may be trained with reference to the training data, and some aspects may be locked or opened depending on the application and the circumstances.

Abstract: A method and system for heterogeneous event detection. Sensor data is obtained and divided into discrete data windows. Each data window is defined by and corresponds to a time period of the sensor data. A time-frequency representation over the time period is calculated for each data window. A filter mask is calculated based on the data window corresponding to the time-frequency representation. The filter mask is applied for reverting the time-frequency representation to a time representation, resulting in filtered data. Features, such as extrema or other inflection points, are identified in the filtered data. The features define events, and transforming the time-frequency representation back into the time domain emphasizes differences between more and less prominent frequencies, facilitating identification of heterogeneous events. The method and system may be applied to body movements of people or animals, automaton movement, audio signals, light intensity, or any suitable time-dependent variable.

Abstract: An image processing device and methods for performing Rotationally Invariant S-transform (RIST) for an image are provided herein. An example method of determining the RIST magnitude at a pixel is provided herein. Further, an example method of determining RIST magnitudes and statistics in a region of interest is provided herein.

Abstract: An image processing device and methods for performing an S-transform (ST) are provided herein. An example method of generating a compressed form of values of a one-dimensional ST for a time series and generating an approximate form of ST is provided herein. Additionally, an example method of determining local spectrum at a pixel is provided herein. Further, an example method of determining ST magnitudes and statistics in a region of interest (ROI) is provided herein.

Abstract: An image processing device and methods for performing Rotationally Invariant S-transform (RIST) for an image are provided herein. An example method of determining the RIST magnitude at a pixel is provided herein. Further, an example method of determining RIST magnitudes and statistics in a region of interest is provided herein.

Abstract: The present invention relates methods of preparing quinoline derivative compounds, and administering such compounds in the treatment of solid and non-solid tumors, notably on liver cancer.

Abstract: An image processing device and methods for performing Rotationally Invariant Stransform (RIST) for an image are provided herein. An example method of determining the RIST magnitude at a pixel is provided herein. Further, an example method of determining RIST magnitudes and statistics in a region of interest is provided herein.

Abstract: An image processing device and methods for performing an S-transform (ST) are provided herein. An example method of generating a compressed form of values of a one-dimensional ST for a time series and generating an approximate form of ST is provided herein. Additionally, an example method of determining local spectrum at a pixel is provided herein. Further, an example method of determining ST magnitudes and statistics in a region of interest (ROI) is provided herein.

Abstract: A blower assembly for a leaf blower has a case, a motor, an impeller driven by the motor, and an axial fan driven by the motor. The axial fan and the impeller are connected to opposite ends of the motor. The case has an air inlet, an air outlet and an air channel communicating the air inlet with the air outlet. The impeller is disposed inside the air channel to move air through the air channel from the air inlet to the air outlet. The axial fan is arranged to generate an axial flow of air towards the motor to cool the motor.

Abstract: A cantharimide compound may include the backbone of formula (1). R1, R2, R3, and R4 may be independently selected from the group consisting of H, C(O)OR5, C(O)R6, C(O)NR7R8, NR9C(O)R10, N—R11R12, O—R13, S—R14, P(O)(OR15)(OR16), As(O)(OR17)(OR18), SO2R19, SO3R20, and B(OR21). X1 to X4 may be independently selected from the group consisting of nitrogen and carbon, such that X1 to X4 are not all hydrogen. Y1, Y2 and R5 to R21 may be independently selected from the group consisting of hydrogen, C1-12-alkyl, -aryl, heteroaryl, and a bioactive polymer.

Abstract: A blower assembly for a leaf blower has a case, a motor, an impeller driven by the motor, and an axial fan driven by the motor. The axial fan and the impeller are connected to opposite ends of the motor. The case has an air inlet, an air outlet and an air channel communicating the air inlet with the air outlet. The impeller is disposed inside the air channel to move air through the air channel from the air inlet to the air outlet. The axial fan is arranged to generate an axial flow of air towards the motor to cool the motor.

Abstract: A cantharimide compound may include the backbone of formula (1). R1, R2, R3, and R4 may be independently selected from the group consisting of H, C(O)OR5, C(O)R6, C(O)NR7R8, NR9C(O)R10, N—R11R12, O—R13, S—R14, P(O)(OR15)(OR16), As(O)(OR17)(OR18), SO2R19, SO3R20, and B(OR21). X1 to X4 may be independently selected from the group consisting of nitrogen and carbon, such that X1 to X4 are not all hydrogen. Y1, Y2 and R5 to R21 may be independently selected from the group consisting of hydrogen, C1-12-alkyl, -aryl, heteroaryl, and a bioactive polymer.

Abstract: The present invention relates methods of preparing quinoline derivative compounds, and administering such compounds in the treatment of solid and non-solid tumors, notably on liver cancer.