Abstract: Aspects of the present disclosure describe systems, methods. and structures in which a hybrid neural network combining a CNN and several ANNs are shown useful for predicting G-ONSR for Ps-256QAM raw data in deployed SSMF metro networks with 0.27 dB RMSE. As demonstrated, the CNN classifier is trained with 80.96% testing accuracy to identify channel shaping factor. Several ANN regression models are trained to estimate G-OSNR with 0.2 dB for channels with various constellation shaping. Further aspects include the tuning of existing optical networks and the characterization of retrofit/upgraded optical networks to estimate capacity—both aspects employing our inventive hybrid neural network methodology.

Abstract: Aspects of the present disclosure describe improving neural network robustness through neighborhood preserving layers and learning weighted-average neighbor embeddings.

Abstract: Aspects of the present disclosure describe systems, methods and structures that perform linear-grammetry and calibration by simultaneous multilateration using only edge distance estimates via two-way ranging.

Abstract: Aspects of the present disclosure describe systems, methods, and structures that provide action recognition with high-order interaction with spatio-temporal object tracking. Image and object features are organized into into tracks, which advantageously facilitates many possible learnable embeddings and intra/inter-track interaction(s). Operationally, our systems, method, and structures according to the present disclosure employ an efficient high-order interaction model to learn embeddings and intra/inter object track interaction across the space and time for AR. Each frame is detected by an object detector to locate visual objects. Those objects are linked through time to form object tracks. The object tracks are then organized and combined with the embeddings as the input to our model. The model is trained to generate representative embeddings and discriminative video features through high-order interaction which is formulated as an efficient matrix operation without iterative processing delay.

Abstract: Aspects of the present disclosure describe systems, methods and structures including a network that recognizes action(s) from learned relationship(s) between various objects in video(s). Interaction(s) of objects over space and time is learned from a series of frames of the video. Object-like representations are learned directly from various 2D CNN layers by capturing the 2D CNN channels, resizing them to an appropriate dimension and then providing them to a transformer network that learns higher-order relationship(s) between them. To effectively learn object-like representations, we 1) combine channels from a first and last convolutional layer in the 2D CNN, and 2) optionally cluster the channel (feature map) representations so that channels representing the same object type are grouped together.

Abstract: Aspects of the present disclosure describe systems, methods and structures providing contextual grounding—a higher-order interaction technique to capture corresponding context between text entities and visual objects.

Abstract: Aspects of the present disclosure describe systems, methods and structures for an efficient multi-person posetracking method that advantageously achieves state-of-the-art performance on PoseTrack datasets by only using keypoint information in a tracking step without optical flow or convolution routines. As a consequence, our method has fewer parameters and FLOPs and achieves faster FPS. Our method benefits from our parameter-free tracking method that outperforms commonly used bounding box propagation in top-down methods. Finally, we disclose tokenization and embedding multi-person pose keypoint information in the transformer architecture that can be re-used for other pose tasks such as pose-based action recognition.

Abstract: Aspects of the present disclosure describe systems, methods and structures providing bidirectional optical fiber communication and sensing using the same fiber transmission band and bidirectional WDM fiber sharing such that communications channels and optical fiber sensing channel(s) coexist on the same fiber. As a result, nonlinear interaction between communications channels and interrogating pulse(s) of sensing are much reduced or eliminated.

Abstract: Aspects of the present disclosure describe systems, methods and structures providing wide-area traffic monitoring based on distributed fiber-optic sensing (DFOS) that employs deep neural network(s) for denoising noisy waterfall traces measured by the DFOS. Such systems, methods, and structures according to aspects of the present disclosure may advantageously monitor multiple highways/roadways using a single interrogator and optical fiber switch(es) which provides traffic information along every sensing point of existing, deployed, in-service optical telecommunications facilities.

Abstract: Aspects of the present disclosure describe systems, methods and structures for providing semiconductor amplifiers exhibiting a low polarization-dependent gain.

Abstract: Aspects of the present disclosure describe multi-frequency coherent distributed acoustic sensing with a single transmitter/receiver pair using an offset Tx/Rx framing scheme and an additional optical IQ modulator to generate the multiple frequency channels for DAS interrogation.

Abstract: Aspects of the present disclosure describe amplifier dynamics compensation through feedback control for distributed fiber sensing systems, methods, and structures employing Brillouin optical time-domain reflectometry.

Abstract: Aspects of the present disclosure describe systems, methods and apparatus for improving the performance of Rayleigh-based phase-OTDR with correlation-based diversity combining and bias removal.

Abstract: Aspects of the present disclosure describe monitoring of optical fiber by distributed temperature sensing (DTS) and determining optical fiber degradation and/or abnormal environmental events including landslides, fires, etc., from DTS data.

Abstract: Aspects of the present disclosure describe Rayleigh fading mitigation via short pulse coherent distributed acoustic sensing with multi-location beating-term combination. In illustrative configurations, systems, methods, and structures according to the present disclosure employ a two stage modulation arrangement providing short interrogator pulses resulting in a greater number of sensing data points and reduced effective sectional length. The increased number of data points are used to mitigate Rayleigh fading via a spatial combining process, multi-location-beating combining (MLBC) which uses weighted complex-valued DAS beating results from neighboring locations and aligns phase signals of each of the locations, before combining them to produce a final DAS phase measurement. Since Rayleigh scattering is a random statistic, the MLBC process allows capture of different statics from neighboring locations with correlated vibration/acoustic signal.

Abstract: Aspects of the present disclosure describe distributed optical fiber sensing for smart city applications in which distributed optical fiber sensing is integrated with a surveillance system into a single system such that the distributed sensing system may detect an event of interest and the surveillance system including cameras may be reoriented in response to verify and/or examine and/or acquire video of the event. Of particular advantage such distributed fiber sensing may include distributed acoustic sensing (DAS) for vibrational sensing and distributed temperature sensing (DTS) for temperature sensing. The integrated system employs shared optical fiber transport for the distributed sensing and the surveillance.

Abstract: Aspects of the present disclosure describe aerial fiber optical cable localization using distributed acoustic sensing (DAS) that advantageously may determine the locality of electrical transformers affixed to utility poles along with the aerial fiber optical cable as well as any length(s) of fiber optical cable between the poles. Further aspects employ survey manned or unmanned, aerial or terrestrial survey vehicles that acoustically excite locations along the fiber optical cable and associate those DAS excitations with global positioning location (GPS).

Abstract: Aspects of the present disclosure describe rapid temperature measurement by wavelength modulation spectroscopy (WMS) that determines gas temperature from 2ƒ signals from two absorption lines by WMS methodologies even when the gas concentration is sufficiently high to saturate optical absorptions. In sharp contrast to the prior art, rapid temperature measurement by WMS according to aspects of the present disclosure employs both a 2ƒ signal ratio and gas concentration determined from the 2ƒ signal.

Abstract: Aspects of the present disclosure describe optical fiber sensing systems, methods and structures disclosing a distributed fiber sensor network constructed on an existing, live network, data carrying, optical fiber telecommunications infrastructure to detect temperatures, acoustic effects, and vehicle traffic—among others. Of particular significance, sensing systems, methods, and structures according to aspects of the present disclosure may advantageously identify specific network locations relative to manholes/handholes and environmental conditions within those manholes/handholes namely, normal, flooded, frozen/iced, etc.

Abstract: Disclosed are improved distributed optical fiber sensing systems, methods, and structures employing disparate point sensors that utilize uni-directional signal transmission via the distributed optical fiber such that a separate communications network for the disparate point sensors is not required.