Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously are employed in smart stadium or other venue applications, such applications including: parking lot security and management; intrusion detection; social sensing; air quality monitoring and early fire detection—among others.

Abstract: An IC package includes one or more microelectronic devices, a plurality of package bumps disposed at a first side, and a metal structure electrically connecting at least a first device contact pad of a first microelectronic device and at least a first package bump of the plurality of package bumps. The metal structure includes an RDL trace extending between a first region aligned with the first device contact pad and a second region aligned with the first package bump, wherein the first package bump is mechanically and electrically connected directly to the second region of the RDL trace. The metal structure further includes a first via extending between the first region of the RDL trace and the first device contact pad and further includes a set of one or more support studs extending from the second region to a support surface facing the first side.

Abstract: A device may receive, from a fiber sensor device, sensing data associated with a fiber optic cable, the sensing data being produced by an activity that poses a threat of damage to the fiber optic cable, and the sensing data identifying: amplitudes of vibration signals, frequencies of the vibration signals, patterns of the vibration signals, times associated with the vibration signals, and locations along the fiber optic cable associated with the vibration signals. The device may process, with a machine learning model, the sensing data to determine a threat level of the activity to the fiber optic cable, the machine learning model having been trained based on historical information regarding detected vibrations, historical information regarding sources of the detected vibrations, and historical information regarding threat levels to the fiber optic cable. The device may perform one or more actions based on the threat level to the fiber optic cable.

Abstract: A conductive pattern has been disclosed. The conductive pattern includes a pair of conductive traces. Each of the conductive traces comprises a linear portion and a terminal portion. The terminal portions are arranged adjacent to each other and comprises a pair of circular arc profile with a pair of complementary notches facing toward each other.

Abstract: A DFOS system and method providing detection and localization of vehicle emergency stops which employs DFOS and machine learning techniques as an integrated solution for automatic, real-time, detection and localization of vehicle emergency stop events. Real time-location data from a buried optical sensing fiber located along a roadway is used to derive continuous vehicle trajectories while providing a wide coverage area for more accurate assessments. AI techniques are employed which track vehicles' speed and acceleration, locate vehicle deceleration events, and localize emergency stop events. Danger assessment is determined by analyzing a vehicle's anticipated and reproducible trajectory after an emergency breaking event—while ignoring stop-and-go events as low-risk events—and discovering stop-no-go events exhibiting large deceleration as high-risk events.

Abstract: A machine learning (ML)/artificial intelligence (AI) based distributed fiber optic sensing (DFOS) system and method providing detection and localization of gunshot events. In addition to the ML/AI DFOS, a signal processing pipeline that compresses an audible distributed acoustic sensing (DAS) waveform data into a small set of features that protects privacy of individuals while preserving the utility of acoustic events to detect the gunshot events and discriminate same from other events. A data-driven deep learning approach automatically predicts acoustic event types with higher accuracy that realized by prior art methods.

Abstract: A neural network is used to place macros on a chip canvas in an integrated circuit (IC) design. The macros are first clustered into multiple macro clusters. Then the neural network generates a probability distribution over locations on a grid and aspect ratios of a macro cluster. The grid represents the chip canvas and is formed by rows and columns of grid cells. The macro cluster is described by at least an area size, aspect ratios, and wire connections. Action masks are generated for respective ones of the aspect ratios to block out a subset of unoccupied grid cells based on design rules that optimize macro placement. Then, by applying the action masks on the probability distribution, a masked probability distribution is generated. Based on the masked probability distribution, a location on the grid is selected for placing the macro cluster with a chosen aspect ratio.

Abstract: A distributed fiber optic sensing (DFOS) system including a smart-mat that: 1) identifies indoor locations of moving persons/objects; 2) provides a 2D visual mapping; and 3) covers any blind spots with supplemental technologies including LiDAR, RF radar, etc. The DFOS system with smart-mat may be deployed virtually anywhere indoors and may even be constructed to replace carpeting. When our inventive DFOS system and smart-mat is deployed throughout an entire building, building safety and security is greatly improved by providing up-to-date localization information of building occupants while eliminating blind zones and reducing maintenance costs.

Abstract: A neural network based method places flexible blocks on a chip canvas in an integrated circuit (IC) design. The neural network receives an input describing geometric features of a flexible block to be placed on the chip canvas. The geometric features includes an area size and multiple aspect ratios. The neural network generates a probability distribution over locations on the chip canvas and the aspect ratios of the flexible block. Based on the probability distribution, a location on the chip canvas is selected for placing the flexible block with a chosen aspect ratio.

Abstract: A distributed fiber optic sensing (DFOS) system and method employing a fiber optic sensor cable that autonomously collects DFOS data and employs artificial intelligence/machine learning (AI/ML) to distinguish sections of the fiber optic sensor cable that are above ground (aerial), below ground (buried), and buried but occasionally above ground, in addition to any change(s) that occur with respect to the fiber optic sensor cable at such sections.

Abstract: An integrated circuit includes a semiconductor substrate, first tap regions, second tap regions, and first gate structures. The semiconductor substrate includes a first active region. The first and second tap regions in the semiconductor substrate and on opposite sides of the first active region. The first gate structures are over the first active region. A distance between the first tap region and a first one of the first gate structures adjacent the first tap region is greater than a distance between the second tap region and a second one of the first gate structures adjacent the second tap region.

Abstract: A car wheel rim decoration cover fastening structure includes a wheel rim, a locking ring, a wheel rim cover, and a central cap. The wheel rim has a rim body and a central hole. The wheel rim has an inner flange formed in the central hole. The locking ring is mounted in the central hole of the wheel rim and locked on the inner flange of the wheel rim. The wheel rim cover has multiple locking blocks secured to the rim body of the wheel rim. The wheel rim cover corresponds to the locking ring. The central cap extends through the center of the wheel rim cover and is locked on the inside of the locking ring. The central cap presses the wheel rim cover. Thus, the wheel rim cover is fixed and will not be detached.

Abstract: An integrated circuit includes a semiconductor substrate, devices, first tap regions, and second tap regions. The devices are over the semiconductor substrate. The first tap regions are over the semiconductor substrate along a first direction. The second tap regions are over the semiconductor substrate along the first direction. A first pitch between adjacent two of the first tap regions in the first direction is greater than a second pitch between adjacent two of the second tap regions in the first direction.

Abstract: A device includes an electrostatic discharge (ESD) protection switch and an ESD driver. The ESD driver is configured to receive a first voltage at a first terminal and receive a second voltage at a second terminal and includes a first trigger circuit and a first resistor. The first trigger circuit includes a first input terminal and a first output terminal. The first input terminal is configured to receive the first voltage. The first resistor is coupled between the first output terminal and the second terminal. When the first voltage received at the first terminal is a first overvoltage and a voltage difference between the first voltage and the second voltage is higher than a first voltage threshold, the ESD driver outputs a first trigger signal to turn on the ESD protection switch.

Abstract: This disclosure provides a perovskite light-emitting diode with an adjustable light field, including a glass layer, an anode, a hole transport layer, an emission layer, an electron transport layer and a cathode in sequence from top to bottom. The electron transport layer is provided with a periodic nano-grating structure.

Abstract: Aspects of the present disclosure describe an unsupervised context encoder-based fiber sensing method that detects anomalous vibrations proximate to a sensor fiber that is part of a distributed fiber optic sensing system (DFOS) such that damage to the sensor fiber by activities producing and anomalous vibrations are preventable. Advantageously, our method requires only normal data streams and a machine learning based operation is utilized to analyze the sensing data and report abnormal events related to construction or other fiber-threatening activities in real-time. Our machine learning algorithm is based on waterfall image inpainting by context encoder and is self-trained in an end-to-end manner and extended every time the DFOS sensor fiber is optically connected to a new route. Accordingly, our inventive method and system it is much easier to deploy as compared to supervised methods of the prior art.

Abstract: An electrostatic discharge protection circuit includes an electrostatic discharge clamp between a first rail and a second rail, a trigger device configured to activate the electrostatic discharge clamp in response to an electrostatic discharge event, and a charge dissipation element between the first rail and the second rail to dissipate a residual charge at an input of the trigger device.

Abstract: The present disclosure provides a light metal structure-function dual-gradient composite brake disc or brake drum. The light metal structure-function dual-gradient composite brake disc or brake drum is made of a light metal/graded ceramic skeleton composite friction layer bearing friction and wear functions and having function (performance) gradient characteristics and a light metal bearing connection and structure functions and having structure gradient characteristics with the light metal/graded ceramic skeleton composite friction layer by integrated composite casting. Such a dual-gradient brake disc or brake drum can exert the advantages of large thermal capacity, rapid thermal dissipation, and insensitivity to cracks of the light metal, and high hardness, high shear resistance, high elastic modulus, and excellent thermal shock resistance of the graded ceramic skeleton.

Abstract: Methods and systems for training a neural network include generate an image of a mask. A copy of an image is generated from an original set of training data. The copy is altered to add the image of a mask to a face detected within the copy. An augmented set of training data is generated that includes the original set of training data and the altered copy. A neural network model is trained to recognize masked faces using the augmented set of training data.