Abstract: The present disclosure provides a method of training artificial intelligence to estimate a lifetime of a storage device, which includes steps of: determining whether an operating parameter of the storage device executing a processing program on bit value values is smaller than an operational threshold parameter or not, if yes, decoding the bit value values stored in the storage device by a decoder; determining whether the bit value values stored in the storage device are successfully decoded by the decoder or not, if yes, classifying the memory unit of the storage device into a strong correct region, a weak correct region, a strong error region or a weak error region; determining whether the number of the memory units falls within an allowable number range or not, if not, initiating an artificial intelligence neural network system to use machine learning to estimate the lifetime of the storage device.

Abstract: The present disclosure provides a method of training artificial intelligence to estimate a lifetime of a storage device, which includes steps of: determining whether an operating parameter of the storage device executing a processing program on bit value values is smaller than an operational threshold parameter or not, if yes, decoding the bit value values stored in the storage device by a decoder; determining whether the bit value values stored in the storage device are successfully decoded by the decoder or not, if yes, classifying the memory unit of the storage device into a strong correct region, a weak correct region, a strong error region or a weak error region; determining whether the number of the memory units falls within an allowable number range or not, if not, initiating an artificial intelligence neural network system to use machine learning to estimate the lifetime of the storage device.

Abstract: A method of training artificial intelligence to execute a decoding program of a low density parity check code, which includes steps of: providing check nodes and variable nodes; outputting accessed bit values stored in memory units to the variable nodes; providing initial log-likelihood ratios to the variable nodes; decoding the accessed bit values based on the initial log-likelihood ratios to output decoded bit values at the variable nodes; executing checking programs at the check nodes to determine whether or not the decoded bit values are equal to data bit values to be stored in the memory units, if yes, outputting a correct message, if not, outputting an error message and then executing the next step; initiating an artificial intelligence neural network system to use machine learning to analyze practical log-likelihood ratios; and decoding the accessed bit values based on the practical log-likelihood ratios at the variable nodes.

Abstract: A method of training artificial intelligence to estimate sensing voltages for a storage device is provided, which includes steps of: supplying initial sensing voltages to memory units; defining various storing states; comparing threshold voltages of the memory units with the initial sensing voltages to classify the memory units; calculating a ratio of the number of the memory units in a strong correct region to the number of in the strong correct region and a weak correct region; calculating a ratio of the number of the memory units in a strong error region to the number of in the strong error region and a weak error region; calculating the number of the memory units in the weak correct and error regions to obtain a histogram parameter; inputting the ratios and parameter to an artificial intelligence neural network; and using machine learning to analyze practical sensing voltages.

Abstract: A method of training artificial intelligence to estimate sensing voltages for a storage device is provided, which includes steps of: supplying initial sensing voltages to memory units; defining various storing states; comparing threshold voltages of the memory units with the initial sensing voltages to classify the memory units; calculating a ratio of the number of the memory units in a strong correct region to the number of in the strong correct region and a weak correct region; calculating a ratio of the number of the memory units in a strong error region to the number of in the strong error region and a weak error region; calculating the number of the memory units in the weak correct and error regions to obtain a histogram parameter; inputting the ratios and parameter to an artificial intelligence neural network; and using machine learning to analyze practical sensing voltages.

Abstract: A method of training artificial intelligence to correct a log-likelihood ratio for a storage device is provided, which includes steps of: defining various storing states; classifying memory units; calculating a strong correct ratio of the number of the memory units classified in a strong correct region to the number of the memory units classified in the strong correct region and a weak correct region; calculating an strong error ratio of the number of the memory units classified in an strong error region to the number of the memory units classified in the strong error region and a weak error region; calculating the number of the memory units classified in the weak correct and error regions to obtain a histogram parameter; inputting the ratios and parameter to an artificial intelligence neural network system; and using machine learning to analyze a log-likelihood ratio.

Abstract: A method for detecting storing states of a solid state storage device is provided, including steps of: applying sensing voltages to memory units; comparing threshold voltages of the memory units with the sensing voltages and accordingly to define the storing states including a strong correct region, a weak correct region, a strong error region and a weak error region, in which the memory units are classified; calculating the number of the memory units in the storing states; calculating a strong correct ratio of the number of the memory units in the strong correct region to the number of the memory units in the strong and weak correct regions; calculating a strong error ratio of the number of the memory units in the strong error region to the number of the memory units in the strong and weak error regions; and generating a log-likelihood ratio based on said ratios.

Abstract: A transport apparatus is for conveying a shoe sole, and includes a rail unit divided into process and bridge regions, a delivery device and a transfer device. The delivery device is movable in the process and the bridge regions, and includes a conveyor belt for carrying the shoe sole and to move the shoe sole on the rail unit. The transfer device is located adjacent to the bridge region, and includes a transfer roller assembly and a transfer belt, which surrounds the transfer roller assembly and is drivable by the same for carrying the shoe sole. When the delivery device is in the bridge region, the conveyor belt is adjacent to the transfer belt, and is operable to convey the shoe sole onto the transfer belt.

Abstract: A method and a system for determining bit values in a non-volatile memory having a number of cells each for storing a bit value are disclosed. The method includes the steps of: a) providing a first test sensing voltage to the cells and calculating a cell count; b) providing another test sensing voltage to the cells and calculating a difference of the cell counts between this step and previous step; c) providing still another test sensing voltage and calculating another difference of the cell counts between this step and previous step; d) processing step c) for N times; e) calculating differential amounts of cell counts and assigning an index number to each group of cells; f) choosing a voltage as an updated sensing voltage.

Abstract: A transport apparatus is for conveying a shoe sole, and includes a rail unit divided into process and bridge regions, a delivery device and a transfer device. The delivery device is movable in the process and the bridge regions, and includes a conveyor belt for carrying the shoe sole and to move the shoe sole on the rail unit. The transfer device is located adjacent to the bridge region, and includes a transfer roller assembly and a transfer belt, which surrounds the transfer roller assembly and is drivable by the same for carrying the shoe sole. When the delivery device is in the bridge region, the conveyor belt is adjacent to the transfer belt, and is operable to convey the shoe sole onto the transfer belt.

Abstract: A portable electronic device is capable of switching between a locked state and an unlocked state. The portable electronic device includes a touch screen, a storage device, a control module, an identifying module, and a comparison module. The touch screen is black while the portable electronic device in the locked state. The storage device stores unlocking information. The control module controls a display region of the touch screen to form an active region for sensing a touch operation and a non-active region. The identifying module identifies a touch operation on the active region as input information. The comparison module compares the input information and the unlocking information. When the input information is identical to the unlocking information, the comparison module switches the portable electronic device to be unlocked.

Abstract: An electronic device includes an imaging unit configured for capturing an image of a subject, a memory unit configured for storing data and images; a processing unit configured for processing data and images; a display unit configured for displaying data and images; and a central processing unit configured for processing various signals and images. The processing unit includes a replay module, a selection module, a function module, a menu module and an input/output unit. The input/output unit is configured for activating anyone of the replay module, the selection module, the function module and the menu module. The application system for the electronic device through activating each module via the input/output interface unit can allow the use of the electronic device to be more convenient and simplified.

Abstract: An electronic device includes an imaging unit configured for capturing an image of a subject, a memory unit configured for storing data and images; a processing unit configured for processing data and images; a display unit configured for displaying data and images; and a central processing unit configured for processing various signals and images. The processing unit includes a replay module, a selection module, a function module, a menu module and an input/output unit. The input/output unit is configured for activating anyone of the replay module, the selection module, the function module and the menu module. The application system for the electronic device through activating each module via the input/output interface unit can allow the use of the electronic device to be more convenient and simplified.

Abstract: A digital camera includes a rotatable wheel, a screen, and a menu display system for displaying menu on the screen. The menu display system includes a primary menu having a number of menu options and may have one or more hierarchical submenu options. The submenu is disk-shaped with a number of sub-submenu options displayed around the disk, each of the sub-submenu options can be selected by rotating the rotatable wheel clockwise or anticlockwise. The digital camera is capable of being operated quickly and efficiently.

Abstract: An exemplary backlight module (120) includes a plurality of optical films (140, 150); a light guide plate (160) having a light output surface (161), the light output surface defines a recessed (165) receiving the optical films; a light source (170) disposed adjacent to a side of the light guide plate; and a frame (190) containing the light source and the light guide plate. With this configuration, the recessed can firmly fix the optical films in the light guide plate. Therefore the backlight module having the optical films and the light guide plate can be mechanically stable and operate properly.