Abstract: An artificial intelligence psychological stress detection device of the present invention includes a mental sensor chip, a communication module, and a microcontroller unit. The mental sensor chip is configured to sense a user's physiological characteristic(s) and/or behavior characteristic(s) and/or environmental characteristic(s). The communication module is configured to communicate with a mobile device. The microcontroller unit is connected to the mental sensor chip and the communication module, and configured to send the user's physiological characteristic(s) and/or the environmental characteristic(s) and/or the behavior characteristic(s) obtained from the mental sensor chip to the mobile device via the communication module.

Abstract: A static random-access memory (SRAM) circuit and associated read operation method and write operation method are provided. The SRAM circuit includes memory units arranged in M columns and N rows, M bit lines, N row-voltage selection lines, N word lines, and a control circuit. The control circuit includes a controller, a voltage source, a voltage selection module, a word-line driving module, and a bit-line driving module. The voltage source provides a first voltage and a second voltage. When the control circuit performs access to the memory unit located in the mth column and the nth row, the voltage selection module transmits one of the first voltage and the second voltage to an nth row-voltage selection line. The voltage selection module transmits the second voltage to the other (N-1) row-voltage selection lines. The variables M, N, m, and n are positive integers.

Abstract: A computing method for performing a matrix multiplying-and-accumulating computation by a flash memory array which includes word lines, bit lines and flash memory cells. The computing method includes the following steps: respectively storing a weight value in each of the flash memory cells, receiving a plurality of input voltages via the word lines, performing an computation on one of the input voltages and the weight value by each of the flash memory cells to obtain an output current, outputting the output currents of the flash memory cells via the bit lines, and accumulating the output currents of the flash memory cells connected to the same bit line of the bit lines to obtain a total output current. Each of the flash memory cells is an analog device, and each of the input voltages, each of the output currents and each of the weight values are analog values.

Abstract: An error detection and correction method is provided. The method includes: when a pipeline stage error is detected, correcting the pipeline stage error; when it is determined that a plurality of cascaded pipeline stage circuits have continuous pipeline stage errors, stopping all operations of all pipeline stage circuits; flushing the data of the pipeline stage circuits; and re-processing the data of the pipeline stage circuits at a downclocked frequency.

Abstract: A system for determining a colonoscopy image is disclosed. The system includes a bowel preparation evaluation unit, an image processing unit electrically or communicatively connected to the bowel evaluation unit, and a convolutional neural network unit electrically or communicatively connected to the image processing unit. The bowel preparation evaluation unit determines whether a bowel in the colonoscopy image is clean. The image processing unit receives the colonoscopy image determined to have a clean bowel and outputted by the bowel preparation evaluation unit, and performs an image process on the colonoscopy image with clean bowel to adjust a contrast thereof. The convolutional neural network unit comprises a convolutional neural network and in a working mode, receives and determines whether the processed colonoscopy image is a cecal image, a non-cecal image, or an indeterminable colonoscopy image.

Abstract: A system for determining a colonoscopy image is disclosed. The system includes a bowel preparation evaluation unit, an image processing unit electrically or communicatively connected to the bowel evaluation unit, and a convolutional neural network unit electrically or communicatively connected to the image processing unit. The bowel preparation evaluation unit determines whether a bowel in the colonoscopy image is clean. The image processing unit receives the colonoscopy image determined to have a clean bowel and outputted by the bowel preparation evaluation unit, and performs an image process on the colonoscopy image with clean bowel to adjust a contrast thereof. The convolutional neural network unit comprises a convolutional neural network and in a working mode, receives and determines whether the processed colonoscopy image is a cecal image, a non-cecal image, or an indeterminable colonoscopy image.