Abstract: An electric ratchet wrench as one example of the ratchet wrench includes an electric motor, a holder that rotatably holds a socket via a one-way clutch mechanism, and a spindle configured to convert a driving force from the motor into a reciprocating rotation motion of the holder. A space is provided between the socket and the holder. The space includes large interval portions and small interval portions. The small interval portions are adjacent to the large interval portions in a rotation direction (lock direction) of the socket and have distances smaller than distances of the large interval portions. Additionally, the one-way clutch mechanism includes columnar locking pins disposed between the spaces and have a diameter with a size equal to or less than the distances of the large interval portions and exceeding the distances of the small interval portions.

Abstract: The present disclosure provides a method in a data processing system that includes at least one processor and at least one memory. The at least one memory includes instructions executed by the at least one processor to implement a driving encounter recognition system. The method includes receiving information, from one or more sensors coupled to a first vehicle, determining first trajectory information associated with the first vehicle and second trajectory information associated with a second vehicle, extracting a feature vector, providing the feature vector to a trained classifier, the classifier trained using unsupervised learning based on a plurality of feature vectors, and receiving, from the trained classifier, a classification of the current driving encounter in order to facilitate the first vehicle to perform a maneuver based on the current driving encounter.

Abstract: A method includes receiving audio data encoding an utterance, processing, using a speech recognition model, the audio data to generate speech recognition scores for speech elements, and determining context scores for the speech elements based on context data indicating a context for the utterance. The method also includes executing, using the speech recognition scores and the context scores, a beam search decoding process to determine one or more candidate transcriptions for the utterance. The method also includes selecting a transcription for the utterance from the one or more candidate transcriptions.

Abstract: The present invention relates to methods of electron beam lithography using ice resist to fabricate nanostructures on a substrate and, more particularly, to a method of fabricating desired three-dimensional nanostructures on a substrate. The method involves two main strategies: grayscale ice lithography and stacking layered structures. Moreover, these two strategies can be combined in one fabrication process to produce more complex 3D nanostructures.

Abstract: An electric ratchet wrench as one example of the ratchet wrench includes an electric motor, a holder that rotatably holds a socket via a one-way clutch mechanism, and a spindle configured to convert a driving force from the motor into a reciprocating rotation motion of the holder. A space is provided between the socket and the holder. The space includes large interval portions and small interval portions. The small interval portions are adjacent to the large interval portions in a rotation direction (lock direction) of the socket and have distances smaller than distances of the large interval portions. Additionally, the one-way clutch mechanism includes columnar locking pins disposed between the spaces and have a diameter with a size equal to or less than the distances of the large interval portions and exceeding the distances of the small interval portions.

Abstract: In one embodiment, example systems and methods related to a manner of optimizing LiDAR sensor placement on autonomous vehicles are provided. A range-of-interest is defined for the autonomous vehicle that includes the distances from which the autonomous vehicle is interested in collecting sensor data. The range-of-interest is segmented into multiple cubes of the same size. For each LiDAR sensor, a shape is determined based on information such as the number of lasers in each LiDAR sensor and the angle associated with each laser. An optimization problem is solved using the determined shape for each LiDAR sensor and the cubes of the range-of-interest to determine the locations to place each LiDAR sensor to maximize the number of cubes that are captured. The optimization problem may further determine the optimal pitch angle and roll angle to use for each LiDAR sensor to maximize the number of cubes that are captured.

Abstract: In one embodiment, example systems and methods related to a manner of unifying heterogeneous datasets are provided. Multiple heterogeneous datasets containing traffic or driving data are collected. The records of the datasets are combined, and the records in the combined dataset are ordered into a plurality of time series based on timestamps associated with each record. A Bayesian learning method, such as hidden Markov models, is used to identify traffic primitives in the datasets. Each traffic primitive may include several consecutive records in the combined dataset and may correspond to particular driving actions such as turning left or right, stopping, accelerating, etc. The traffic primitives are used to create a traffic primitive index that can be queried by users or researchers for specific records. These records can be used to train or test one or more learning-based algorithms.

Abstract: The present disclosure provides a method in a data processing system that includes at least one processor and at least one memory. The at least one memory includes instructions executed by the at least one processor to implement a driving encounter recognition system. The method includes receiving information, from one or more sensors coupled to a first vehicle, determining first trajectory information associated with the first vehicle and second trajectory information associated with a second vehicle, extracting a feature vector, providing the feature vector to a trained classifier, the classifier trained using unsupervised learning based on a plurality of feature vectors, and receiving, from the trained classifier, a classification of the current driving encounter in order to facilitate the first vehicle to perform a maneuver based on the current driving encounter.