Abstract: A styrene-isoprene-styrene triblock copolymer, a method for manufacturing the same, a composition, and an adhesive composition using the same are provided. The styrene-isoprene-styrene triblock copolymer includes a first polystyrene block, a polyisoprene block, and a second polystyrene block. Based on 100 wt % of the styrene-isoprene-styrene triblock copolymer, the content of the first polystyrene block and the second polystyrene block is 30-50 wt %, and the content of the polyisoprene block is 70-50 wt %. The weight average molecular weight (Mw) of the styrene-isoprene-styrene triblock copolymer is 40,000-75,000.

Abstract: A resistive random-access memory (RRAM) device includes a bottom electrode, a high work function layer, a resistive material layer, a top electrode and high work function spacers. The bottom electrode, the high work function layer, the resistive material layer and the top electrode are sequentially stacked on a substrate, wherein the resistive material layer includes a bottom part and a top part. The high work function spacers cover sidewalls of the bottom part, thereby constituting a RRAM cell. The present invention also provides a method of forming a RRAM device.

Abstract: Provided is a resistive random access memory (RRAM). The resistive random access memory includes a plurality of unit structures disposed on a substrate. Each of the unit structures includes a first electrode, and a first metal oxide layer. The first electrode is disposed on the substrate. The first metal oxide layer is disposed on the first electrode. In addition, the resistive random access memory includes a second electrode. The second electrode is disposed on the plurality of unit structures and connected to the plurality of unit structures.

Abstract: A layout pattern of a magnetoresistive random access memory (MRAM) includes a substrate having a first cell region, a second cell region, a third cell region, and a fourth cell region and a diffusion region on the substrate extending through the first cell region, the second cell region, the third cell region, and the fourth cell region. Preferably, the diffusion region includes a H-shape according to a top view.

Abstract: Electrostatic discharge (ESD) structures are provided. An ESD structure includes a semiconductor substrate, a first epitaxy region with a first type of conductivity over the semiconductor substrate, a second epitaxy region with a second type of conductivity over the semiconductor substrate, and a plurality of semiconductor layers. The semiconductor layers are stacked over the semiconductor substrate and between the first and second epitaxy regions. A first conductive feature is formed over the first epitaxy region and outside an oxide diffusion region. A second conductive feature is formed over the second epitaxy region and outside the oxide diffusion region. A third conductive feature is formed over the first epitaxy region and within the oxide diffusion region. A fourth conductive feature is formed over the second epitaxy region and within the oxide diffusion region. The oxide diffusion region is disposed between the first and second conductive features.

Abstract: A radar signal processing system with a self-interference cancelling function includes an analog front end (AFE) processor, an analog to digital converter (ADC), an adaptive interference canceller (AIC), and a digital to analog converter (DAC). The AFE processor receives an original input signal and generates an analog input signal. The ADC converts the analog input signal to a digital input signal. The AIC generates a digital interference signal digital interference signal by performing an adaptive interference cancellation process according to the digital input signal. The DAC converts the digital interference signal to an analog interference signal. Finally, the analog interference signal is fed back to the AFE and cancelled from the original input signal in the AFE processor while performing the front end process, reducing the interference of the static interference from the leaking of a close-by transmitter during the front end process.

Abstract: A surgical robot including at least one contact module, a control connection module, at least one first robotic arm, and at least one grip control device. A first transmission member of the control connection module drives the control module through a first transmission connecting member. A first shaft member of the first robotic arm is connected with the first transmission member while the grip control device is connected with the first robotic arm by a transmission interface. A force sensing member of the first robotic arm detects a first reaction force from the contact module so that the first robotic arm sends a feedback control signal to the grip control device to control a grip driving member to generate a force feedback for allowing a grip portion to move. Thereby, users can feel movement of the grip portion caused by the force feedback to avoid accidental iatrogenic injuries.

Abstract: A resistive random access memory (RRAM) structure includes a RRAM cell, spacers and a dielectric layer. The RRAM cell is disposed on a substrate. The spacers are disposed beside the RRAM cell, wherein widths of top surfaces of the spacers are larger than or equal to widths of bottom surfaces of the spacers. The dielectric layer blanketly covers the substrate and sandwiches the RRAM cell, wherein the spacers are located in the dielectric layer. A method for forming the resistive random access memory (RRAM) structure is also provided.

Abstract: A RRAM device includes a bottom electrode, a resistive material layer, atop electrode, a hard mask and high work function sidewall parts. The bottom electrode, the resistive material layer, the top electrode and the hard mask are sequentially stacked on a substrate. The high work function sidewall parts cover sidewalls of the top electrode and sidewalls of the hard mask, thereby constituting a RRAM cell. A method of forming the RRAM device is also provided.

Abstract: A mobile device includes a housing, a first radiation element, a second radiation element, a third radiation element, a first switch element, and a second switch element. The first radiation element has a first feeding point. The second radiation element has a second feeding point. The first radiation element, the second radiation element, and the third radiation element are distributed over the housing. The first switch element is closed or open, so as to selectively couple the first radiation element to the third radiation element. The second switch element is closed or open, so as to selectively couple the second radiation element to the third radiation element. An antenna structure is formed by the first radiation element, the second radiation element, and the third radiation element.

Abstract: A performing device of an impulse-like gesture recognition system executes an impulse-like gesture recognition method. A performing procedure of the impulse-like gesture recognition method includes steps of: receiving a sensing signal from a sensing unit; determining a prediction with at least one impulse-like label according to the sensing frames by a deep learning-based model; and classifying at least one gesture event according to the prediction. The gesture event is classified to determine the motion of the user. Since the at least one impulse-like label is used to label at least one detection score of the deep learning-based model, the detection score is non-decreasing, reaction time of the at least one gesture event for an incoming gesture is fast, rapid consecutive gestures are easily decomposed, and an expensive post-processing is not needed.

Abstract: A loop antenna which has a signal input/output wire, a first conductor, an upper conductor, an upper conductor, a second conductor, a first lower conductor, and a second lower conductor sequentially connected. The loop antenna further has a first grounding via, and a lower end of the first grounding via is connected to a first grounding layer, and an upper end of the first grounding via is disposed between and connecting the first lower conductor and the second lower conductor, wherein a first end of the second lower conductor is connected to the upper end of the first grounding via, and a second end of the second lower conductor is connected to the first conductor. A second grounding layer and the combination of the signal input/output wire, the first lower conductor, and the second lower conductor are disposed on the same layer disconnectedly.

Abstract: A loop antenna which has a signal input/output wire, a first conductor, an upper conductor, an upper conductor, a second conductor, a first lower conductor, and a second lower conductor sequentially connected. The loop antenna further has a first grounding via, and a lower end of the first grounding via is connected to a first grounding layer, and an upper end of the first grounding via is disposed between and connecting the first lower conductor and the second lower conductor, wherein a first end of the second lower conductor is connected to the upper end of the first grounding via, and a second end of the second lower conductor is connected to the first conductor. A second grounding layer and the combination of the signal input/output wire, the first lower conductor, and the second lower conductor are disposed on the same layer disconnectedly.

Abstract: A method of customizing a hand gesture provides a touch screen, a computing unit connected with the touch screen, and a hand gesture database connected with the computing unit, and the method includes the following steps: recording a hand gesture trajectory data input of an input hand gesture on the touch screen; converting the hand gesture trajectory data input into a 2D trajectory graph of the input hand gesture; the computing unit sequentially reads a 2D hand gesture reference graph from the hand gesture database, and correspondingly generates a 2D hand gesture reference graph set for the 2D hand gesture reference graph read, and then the computing unit compares the similarity between the 2D trajectory graph of the input hand gesture and each reference graph in the 2D hand gesture reference graph set to determine whether the input hand gesture is already in the hand gesture database.

Abstract: A generic gesture detecting method executed by a generic gesture detecting device includes steps of: receiving a current sensing signal from a sensing unit; generating a current image according to the current sensing signal; determining whether the current image is similar with a stored image stored in a memory unit; when the current image is similar with the stored image, detecting the current image and the stored image to be a gesture signal; when the current image is different from the stored image, storing the current image into the memory unit, and returning to the step of receiving a current sensing signal. Since the generic gesture detecting device can automatically detect the gesture signal, the user may not need to enable a detecting time period before implementing a command motion. Therefore, the user can make the command motion without enabling the detecting time period, and the convenience can be increased.

Abstract: A temporal sequence alignment method includes the following steps: receiving gesture training data and gesture sample data; wherein the gesture training data includes multiple training frames and multiple training soft labels, and the gesture sample data includes multiple sample frames and multiple sample soft labels; compressing the training frames to generate a compressed training frame; compressing the sample frames to generate a compressed sample frame; calculating an alignment model of the compressed training frame and the compressed sample frame; aligning the sample soft labels to multiple aligned soft labels according to the alignment model; generating an aligned training data according to the gesture sample data and the aligned soft labels. The present invention uses the aforementioned steps to calibrate the sample soft labels of the gesture sample data, allowing a gesture recognition system to minimize time discrepancy for recognizing a gesture.

Abstract: A pressure sensing element has an elastic porous substrate, an electrode, an upper protective layer, and a lower protective layer. The elastic porous substrate is provided with a piezoelectric layer on a surface of the elastic porous substrate. The electrode is formed on at least one of a top and a bottom of the elastic porous substrate. The upper protective layer and a lower protective layer are provided respectively above and below the elastic porous substrate. The elastic porous substrate has multiple holes arranged in regular and repetitive patterns including gyroidal structures, lattice structures or schwarz structures.

Abstract: The present invention provides a method of angiography for cerebrovascular obliteration includes: using a classifier to obtain a 2D medical image using from a plurality of Multiphase CTA images; using a gray-scale conversion to obtain a N*M pixels grayscale image; filtering the grayscale image not being meet a condition of grayscale threshold and performing an image binarization to obtain a binarized image; confirming at least one vascular region and performing an image skeletonization; filtering according to a vascular image features of a vascular region to obtain a vascular-enhanced image; using a fracture analysis to obtain an analysis report related to a plurality of quantifying parameters of vascular characteristics; wherein the quantifying parameters of vascular characteristics comprises a quantitative value of fractal dimension (FD), vessel density (VD), skeleton density (SD) and vascular diameter index (VDI).

Abstract: A custom gesture collection and recognition system having a machine learning accelerator includes a transmission unit, a first reception chain, a second reception chain, a customized gesture collection engine and a machine learning accelerator. The transmission unit transmits a transmission signal to detect a gesture. The first reception chain receives a first signal and generates first feature map data corresponding to the first signal. The second reception chain receives a second signal and generates second feature map data corresponding to the second signal. The first signal and the second signal are generated by the gesture reflecting the transmission signal. The customized gesture collection engine generates gesture data according to at least the first feature map data and the second feature map data. The machine learning accelerator performs machine learning with the gesture data. The accuracy and correctness of gesture recognition may be improved by means of machine learning.