Abstract: A novel design for conflict free address generation mechanism is provided for reading data from Block RAM (BRAM) into a Fast Fourier Transform (FFT) module and writing back the processed data back to the BRAM. Also, a novel way of reducing a memory footprint by reducing a twiddle factor table size by taking an advantage of the symmetry property of twiddle factors is presented. Further, additional architecture-specific optimizations are provided, which involve a design of deeply pipelined butterfly modules and the BRAM accesses, parallel butterfly modules for a single FFT block and parallel FFT lane implementation.

Abstract: A method and apparatus are provided for autonomously detecting and reporting anomalies in actions of an autonomous mobile node, or in behaviors of a swarm of autonomous mobile nodes to an operator. The autonomous mobile node may experience anomalies or unexpected situations due to various failures or external influence (e.g. natural weather phenomena, enemy threats). During a training phase a prediction model and a structured model may be established from measurement data received from one or more sensors of an autonomous mobile node or a swarm of nodes while executing an action or behavior under normal circumstances. A prediction model forecasts the expected outcome of an action or behavior, and structured model helps quantify the similarity of a learned action or behavior to the currently observed situation. Based on the measurement data applicable models can be used for an action or behavior for anomaly detection in the action or behavior.

Abstract: Techniques for autonomously establishing, maintaining, and repairing of a wireless communication network among multiple autonomous mobile nodes (AMN) are provided. The multiple AMNs are flown towards a first node. A tentacle is established with the first node and extended to cover a second node over a distance, thereby establishing a wireless communication network between the first node and the second node via the multiple AMNs. Any damage to the established wireless communication network or tentacle may be autonomously detected and repaired by using spare AMNs. Further, the communication network may be used to enable autonomous detection, tracking of the second node, as well as autonomous detection of a contamination area, based on data received from one or more sensors onboard the AMNs deployed in the air.

Abstract: A method and apparatus are provided for autonomously detecting and reporting anomalies in actions of an autonomous mobile node, or in behaviors of a swarm of autonomous mobile nodes to an operator. The autonomous mobile node may experience anomalies or unexpected situations due to various failures or external influence (e.g. natural weather phenomena, enemy threats). During a training phase a prediction model and a structured model may be established from measurement data received from one or more sensors of an autonomous mobile node or a swarm of nodes while executing an action or behavior under normal circumstances. A prediction model forecasts the expected outcome of an action or behavior, and structured model helps quantify the similarity of a learned action or behavior to the currently observed situation. Based on the measurement data applicable models can be used for an action or behavior for anomaly detection in the action or behavior.

Abstract: A method and apparatus are provided for autonomously detecting and reporting anomalies in actions of an autonomous mobile node, or in behaviors of a swarm of autonomous mobile nodes to an operator. The autonomous mobile node may experience anomalies or unexpected situations due to various failures or external influence (e.g. natural weather phenomena, enemy threats). During a training phase a prediction model and a structured model may be established from measurement data received from one or more sensors of an autonomous mobile node or a swarm of nodes while executing an action or behavior under normal circumstances. A prediction model forecasts the expected outcome of an action or behavior, and structured model helps quantify the similarity of a learned action or behavior to the currently observed situation. Based on the measurement data applicable models can be used for an action or behavior for anomaly detection in the action or behavior.

Abstract: Techniques for autonomously establishing, maintaining, and repairing of a wireless communication network among multiple autonomous mobile nodes (AMN) are provided. The multiple AMNs are flown towards a first node. A tentacle is established with the first node and extended to cover a second node over a distance, thereby establishing a wireless communication network between the first node and the second node via the multiple AMNs. Any damage to the established wireless communication network or tentacle may be autonomously detected and repaired by using spare AMNs. Further, the communication network may be used to enable autonomous detection, tracking of the second node, as well as autonomous detection of a contamination area, based on data received from one or more sensors onboard the AMNs deployed in the air.

Abstract: A method and apparatus are provided for autonomously detecting and reporting anomalies in actions of an autonomous mobile node, or in behaviors of a swarm of autonomous mobile nodes to an operator. The autonomous mobile node may experience anomalies or unexpected situations due to various failures or external influence (e.g. natural weather phenomena, enemy threats). During a training phase a prediction model and a structured model may be established from measurement data received from one or more sensors of an autonomous mobile node or a swarm of nodes while executing an action or behavior under normal circumstances. A prediction model forecasts the expected outcome of an action or behavior, and structured model helps quantify the similarity of a learned action or behavior to the currently observed situation. Based on the measurement data applicable models can be used for an action or behavior for anomaly detection in the action or behavior.

Abstract: A method and apparatus are provided for autonomously detecting and reporting anomalies in actions of an autonomous mobile node, or in behaviors of a swarm of autonomous mobile nodes to an operator. The autonomous mobile node may experience anomalies or unexpected situations due to various failures or external influence (e.g. natural weather phenomena, enemy threats). During a training phase a prediction model and a structured model may be established from measurement data received from one or more sensors of an autonomous mobile node or a swarm of nodes while executing an action or behavior under normal circumstances. A prediction model forecasts the expected outcome of an action or behavior, and structured model helps quantify the similarity of a learned action or behavior to the currently observed situation. Based on the measurement data applicable models can be used for an action or behavior for anomaly detection in the action or behavior.