Boehmert & Boehmert

In some examples, a method of manufacturing a metal-oxide-semiconductor field-effect transistor (MOSFET) comprises forming multiple pillar trenches in a semiconductor substrate, wherein each pillar trench comprises a pillar trench source electrode and a field oxide material surrounding the source electrode, recessing the field oxide material in an upper portion of each pillar trench, whereby to form a recessed portion, and filling the recessed portion with a gate electrode, whereby to form a pillar trench gate electrode, forming multiple shallow trenches in a mesa region, wherein the mesa region comprises a region between two neighbouring pillar trenches, wherein each shallow trench comprises a shallow trench gate electrode, and wherein the shallow trench gate is formed by recessing the semiconductor substrate in the mesa region to define the shallow trench, and filling the shallow trench with a gate electrode material, whereby to form the shallow trench gate electrode, electrically connecting the pillar trench gate electrodes to the shallow trench gate electrodes via a gate pick-up, selectively converting at least one of the multiple pillar trenches, the shallow trenches, or both the multiple pillar trenches and the shallow trenches into a MOS diode structure by: replacing the pillar trench gate electrode, the shallow trench gate electrode, or both the pillar trench gate electrode and the shallow trench gate electrode, respectively, with a further source electrode, and forming a gate dielectric in the MOS diode structure.

A light wavefront measurement device includes a lens array, a light receiving sensor, and a wavefront shape calculation part. The lens array focuses measurement light for each region of the wavefront of the measurement light. The light receiving sensor receives multiple light spots focused by the lens array to output an electrical signal for pixels where the amount of received light has changed. The electrical signal indicates the direction of increase or decrease in the amount of received light and the time at which the amount of received light has changed. The wavefront shape calculation part calculates the wavefront shape of the measurement light, based on the electrical signal outputted from the light receiving sensor. The amount of information outputted from the light receiving sensor is smaller than that outputted for all pixels. The light receiving sensor is hence capable of outputting information about the displacement of the light spots with high time resolution. This allows the measurement of the wavefront shape of the measurement light with high time resolution, based on the information outputted from the light receiving sensor.

The disclosure relates generally to method and system to generate decision-fused classification map using different modalities in remote sensing applications. In remote sensing applications, the problem of missing data from satellite images occurs due to various environmental factors and spatial resolution provides inconsistent class labels during segmentation. The method receives from each satellite among a plurality of satellites a plurality of remote sensing images capturing a land use and land cover (LULC) geographical area on earth. Each pretrained classifier obtains the plurality of remote sensing images to generate a segmentation map. Further, Kolmogorov Arnold networks (KAN) decision fusion combines two or more closest segmentation maps using an ontological knowledge tree to determine a minimal common parent class based on at least one satisfying criteria. Finally, a fused classification map is generated to obtain finer grained LULC classes for each remote sensing image using the ontological knowledge tree.

Beschrieben wird eine Klemmvorrichtung für ein Sportgerät, die Klemmvorrichtung umfassend: mindestens ein Rotationselement; und ein Gehäuse, wobei das Gehäuse eine Aufnahme für ein Befestigungsobjekt aufweist, wobei das Rotationselement um eine Rotationsachse drehbar in dem Gehäuse gelagert ist, wobei, wenn das Befestigungsobjekt in der Aufnahme aufgenommen ist, das Rotationselement geeignet ist eine erste Klemmverbindung mit dem Befestigungsobjekt herzustellen, wenn das Rotationselement um die Rotationsachse in eine erste Rotationsrichtung rotiert wird und dadurch die Klemmvorrichtung von einem offenen Zustand, in dem das Rotationselement von dem Befestigungsobjekt beabstandet ist, in einen geschlossenen Zustand, in dem das Rotationselement die erste Klemmverbindung mit dem Befestigungsobjekt herstellt, wechselt. Beschrieben wird auch eine Verwendung einer solchen Vorrichtung zum Befestigen eines Sportgeräts an einem Befestigungsobjekt

Embodiments herein provide a method and system for splitting an artificial neural network (ANN) between edge devices (edge) and a user equipment (UE) under supervision of an application cloud (AC) through a unified splitting AI framework (SAF). In AI operation splitting, a few layers of the artificial neural network (ANN) are computed at the endpoint itself. The intermediate result is transferred to the Edge/ Cloud for further computation. Besides distributing the computation load, this has another important advantage. If the first few layers of the ANN are computed at the endpoint itself, then that saves the resources required for transmission of the entire input signal because the intermediate result needs much less bandwidth than the actual input. Also, the ANN could start in the offloaded node only after receiving the entire input. So, splitting the ANN computation is beneficial in terms of the total computation latency as well.

A method for operating a runtime system for an industrial control environment comprises steps of defining a parent process in the runtime system; starting at least a first child process and a second child process from the parent process in the runtime system; establishing a first private bidirectional communication channel between the parent process and the first child process, and a second private bidirectional communication channel between the parent process and the second child process; receiving, at the second child process, a first handshake request from the first child process via a communication channel; and verifying, at the second child process, the authenticity of the first handshake request. Verifying the authenticity of the first handshake request comprises sending a first attestation request to the parent process via the second private bidirectional communication channel, and receiving a first attestation confirmation from the parent process via the second private bidirectional communication channel.

This disclosure relates generally to a method and system for predicting cardiologist level clinical explanations for electrocardiogram signal classification using deep learning. Conventional methods for explainability of artificial intelligence models are influenced by data distribution and do not provide clinical level explanations in cardiac healthcare. The method disclosed provides clinical level explanations for rhythm diagnosis from ECG signals. The method validates that clinical concepts in ECG signals can be extracted by generating a set of synthetic ECG signals. The set of synthetic ECG signals are generated using trained generative adversarial networks and statistical parameterization techniques. Further the set of synthetic ECG signals are validated by classifying and comparing them with a set of real ECG signals. Then a clinical concept identifier model is trained for predicting the clinical concepts present in an ECG signal classified into a set of signal classes. These clinical concepts predicted are cardiologist level explanations.

The disclosure relates generally to methods and systems for generating design component knowledge graph and design component retrieval from the same. Conventional techniques that utilize customizable knowledge graphs for retrieval of the design components are very limited in the digital design domain. The methods and systems of the present disclosure generate the design component knowledge graph using filtered captions generated for the design component objects. The design component objects are of the design components and are selected using an instruction triplet generated based on a user requirement query of the digital design that the user is interested in. Then, a plurality of captions is generated and filtered using a semantic filtering to obtain the filtered captions. Then the design component knowledge graph is queried with the user input to retrieve the design components that best suit the design requirement of the user.

Embodiments disclosed herein may include an apparatus with a substrate. In an embodiment, an opening is formed through the substrate. In an embodiment, a layer is provided over a sidewall of the opening, and the layer comprises a first magnetic material, and a plug is in the opening. In an embodiment, the plug comprises a second magnetic material that is different than the first magnetic material. In an embodiment, a first via is through the plug, and a second via may be formed through the plug.

This observation apparatus 1 observes a particle trapped by a plurality of electrodes 72 extending in a direction crossing a channel 80 formed in a transparent substrate 9, and includes an imaging unit 31, a camera moving mechanism 32 that moves an imaged area of the imaging unit 31, and a control unit 10. The control unit 10 includes an end-electrode detection unit 511 that detects an end electrode 720a positioned at an end of the channel 80, an adjacent-electrode detection unit 512 that detects an adjacent electrode 720b adjacent to the end electrode 720a, a channel wall detection unit 513 that detects a sidewall 800w of the channel 80, and an observation position adjustment unit 514 that adjusts an observation position where the particle is observed, on the basis of the positions of the end electrode 720a, the adjacent electrode 720b, and the sidewall 800w. This enables proper alignment in observation of particles trapped in the vicinity of the electrodes 72 in the substrate 9 such as a micro-channel chip.