Abstract: The AI-Based Real-Time Prediction Engine Apparatuses, Methods and Systems (“AIRTPE”) transforms machine learning training input, order placement input inputs via AIRTPE components into machine learning training output, order placement output, information leakage alert outputs. An order placement datastructure associated with a security identifier is obtained. An order placement allocation for the security identifier is determined. An order placement request datastructure for a first order is sent to a server associated with a first venue. A set of trade tick data messages associated with the first venue is obtained. A set of inferred labels is determined for each obtained trade tick data message using a real-time prediction logic generated using a machine learning technique. The inferred labels of a selected inferred label type are grouped into buckets. When it is determined that the grouped inferred labels correspond to execution data generated by the first order, an information leakage alert is generated.

Abstract: The AI-Based Real-Time Prediction Engine Apparatuses, Methods and Systems (“AIRTPE”) transforms machine learning training input, order placement input inputs via AIRTPE components into machine learning training output, order placement output, information leakage alert outputs. An order placement datastructure associated with a security identifier is obtained. An order placement allocation for the security identifier is determined. An order placement request datastructure for a first order is sent to a server associated with a first venue. A set of trade tick data messages associated with the first venue is obtained. A set of inferred labels is determined for each obtained trade tick data message using a real-time prediction logic generated using a machine learning technique. The inferred labels of a selected inferred label type are grouped into buckets. When it is determined that the grouped inferred labels correspond to execution data generated by the first order, an information leakage alert is generated.

Abstract: The AI-Based Neighbor Discovery Search Engine Apparatuses, Methods and Systems (“ANDSE”) transforms embedding neural network training request, object search request inputs via ANDSE components into embedding neural network response, object search response outputs. An embedding neural network training request associated with a set of context objects is obtained. Sample similarity evaluation metrics are determined. For each context object, a set of positive target samples that satisfy the sample similarity evaluation metrics for the respective context object is determined. For each context object and each positive target sample in the respective set of positive target samples, a training example comprising the respective context object and a positive target sample is added to a training set. Configuration parameters for an embedding neural network are determined. The embedding neural network is trained using training examples in the training set.

Abstract: The Secret Key-Based Counterparty Matching Engine Apparatuses, Methods and Systems (“SKCME”) transforms client key message, order placement input inputs via SKCME components into order placement output outputs. A matched key is determined and used to generate a set of cross times and a set of cross venues for a security identifier. Orders associated with the security identifier are obtained from counterparties and are assigned a scheduled cross time from the generated set of cross times and a scheduled cross venue from the generated set of cross venues. The orders are sent for execution at the scheduled cross venue at the scheduled cross time.

Abstract: The AI-Based Real-Time Prediction Engine Apparatuses, Methods and Systems (“AIRTPE”) transforms machine learning training input, order placement input inputs via AIRTPE components into machine learning training output, order placement output, information leakage alert outputs. An order placement datastructure associated with a security identifier is obtained. An order placement allocation for the security identifier is determined. An order placement request datastructure for a first order is sent to a server associated with a first venue. A set of trade tick data messages associated with the first venue is obtained. A set of inferred labels is determined for each obtained trade tick data message using a real-time prediction logic generated using a machine learning technique. The inferred labels of a selected inferred label type are grouped into buckets. When it is determined that the grouped inferred labels correspond to execution data generated by the first order, an information leakage alert is generated.

Abstract: The AI-Based Context Evaluation Engine Apparatuses, Methods and Systems (“ANDSE”) transforms embedding neural network training request, object search request, object evaluation request inputs via ANDSE components into embedding neural network response, object search response, object evaluation response outputs. Comparable context objects for a context object are determined. Relative values of the comparable context objects are calculated with regard to a benchmark object and used to calculate a relative value of the context object. The relative value is converted to a predicted price for the context object. Bid ask spreads for bid request objects are calculated. A spread win decision tree is constructed based on the calculated bid ask spreads and used to generate a spread win probability map for the context object. A spread is selected from the spread win probability map based on a desired winning bid confidence level and a bid price for the context object is calculated.

Abstract: The AI-Based Neighbor Discovery Search Engine Apparatuses, Methods and Systems (“ANDSE”) transforms embedding neural network training request, object search request inputs via ANDSE components into embedding neural network response, object search response outputs. An embedding neural network training request associated with a set of context objects is obtained. Sample similarity evaluation metrics are determined. For each context object, a set of positive target samples that satisfy the sample similarity evaluation metrics for the respective context object is determined. For each context object and each positive target sample in the respective set of positive target samples, a training example comprising the respective context object and a positive target sample is added to a training set. Configuration parameters for an embedding neural network are determined. The embedding neural network is trained using training examples in the training set.

Abstract: A network includes a transmitter and a receiver, wherein the transmitter includes a set of transmit antennas and the receiver includes a set of receive antennas. The transmitter duplicates a packet as copies of the packet, and selects subsets of the set of transmit antennas independent of channel characteristics between the subsets of transmit antennas and the set of receive antennas, wherein combinations of the antennas in the subsets of the transmit antennas are different. The receiver selects subsets of the set of receive antennas independent of channel characteristics between the subsets of receive antennas and the set of transmit antennas, wherein combinations of the antennas in the subsets of the receive antennas are different. The selected subsets are used to transmit the packet, and retransmit the packet in case of a failure in a previous transmission.

Abstract: An optical network includes multiple source, cross connect, and destination nodes. A traffic demand matrix is constructed for each possible pair of combinations of the source nodes and the destination nodes. A first energy reduction metric is determined for creating the bypass between the source node and any XC node based on the traffic demand matrix, and a second energy reduction metric is determined for creating the bypass between any XC node and the destination node using the traffic demand matrix. Then, a bypass that terminates at one of the XC nodes that has a largest energy reduction metric is created.

Abstract: A hybrid communication network for a transportation safety system includes a fixed wired nodes and mobile wireless nodes. Because the wired nodes operate independently packets transmitted by the wired nodes to the wireless nodes need to be synchronized. A downlink travel time for downlink packets traveling from a controller to the wireless nodes is determined. Then, the controller schedules downlink data intervals (DDI) based on the downlink travel time; and transmits downlink packets to the wireless nodes during the DDI, such that a latency requirement of the transportation safety system is satisfied.

Abstract: A wireless star network includes including a master node (master) and a set of N slave nodes (slaves), wherein the network uses orthogonal frequency division multiple access (OFDMA). The master partitions the set of slaves in a first subset A(i) and second subset B(j), wherein the first and second subsets are disjoint. Packets are transmitted by the first subset of slaves only while the master and second subset of slaves operate in receive mode, and packets are transmitted by the second subset of slaves only while the master and first of slaves operate in receive mode.

Abstract: An encoder in a transmitter uses space-time trellis coding. An input bitstream is multiplexed to produce in parallel a set of output bitstreams. A multiplier applies a code generating weight to each output bitstream, which are combined, mapped and transmitted by a single antenna.

Abstract: A method estimates a wireless channel at a receiver. The signal is transmitted using narrowband orthogonal frequency division demultiplexing (OFDM) and frequency subcarriers, and the signal includes a set of data tones and a set of pilot tones. The channel and pilot tone interference are estimated based on all the pilot tones extracted from the signal and a channel model. The set of data are equalized based on the channel estimate. Data interference is detected according to the pilot interference and the equalized data tones. Subcarrier interference-to-noise ratios are determined based on the data interference. Signal strengths of the data tones are determined based on the equalized data tones, log-likelihood ratios of bits represented by the data tones are determined based on the subcarrier interference-to-noise ratios and the signal strength of the data tones.

Abstract: In a network for a safety system in a transportation system, the transportation system includes a shaft and a car arranged in the shaft. A first wall node is at a first end of the shaft and a second wall node is at a second end of the shaft to communicate safety messages with the car. Each wall node includes at least one wireless transceiver connected to one or more antennas. Each car in the shaft includes at least two wireless transceiver connected to one or more antennas, wherein the first transceiver of the car uses a first frequency and the second transceiver of the car uses a second frequency to communicate each safety messages in duplicate. A wired backbone connects the set of wall nodes to a controller of the safety system of the transportation system.

Abstract: Symbols in information are encoded as a codeword using a differential orthogonal code. The codeword is stored in a substrate. A moving sensor acquires an image of the codeword in the substrate and decodes the codeword using a balanced differential decoder. The codeword can be painted as lane markings on a road surface.

Abstract: A set of symbols is encoded using a continuous mapping function to produce corresponding encoded functions, wherein the mapping function is continuous and values of the encoded functions are real and vary continuously. The encoded functions are summed in a spatial domain to generate a summed function, which is biased so that values of a biased function are all positive and real. Then, a physical property of the object is altered according the biased function to arrange a mark on the object.

Abstract: A method transmits an L bit packet in a relay network including a source node, a relay node and a destination node. The source node partitions the packet into first fragment of ?L bits and a second fragment of (1??) bits. The first fragment is transmitted from the source node to the relay node at a first data rate during a first phase. The second fragment is transmitted from the source node to the destination node at a second data rate during a second phase while the first fragment is retransmitted from the relay node to the destination node at a third data rate.

Abstract: Soft decision decoding of a codeword of a Reed-Muller (RM) code by selecting an optimal decomposition variable i using a likelihood calculation. A code RM(r, m) is expressed as {(u, uv)|u?RM(r, m?1) and v?RM(r?1, m?1)}, where uv denotes a component-wise multiplication of u and v, and (u, uv)=(r1, r2). A receive codeword is separated into r1=u and r2=uv based on the optimal decomposition variable, and r2 is decoded according to the optimal decomposition variable, using a RM(r?1, m?1) decoder to obtain a decoded v and a first set of decoded bits. The decoded v is combined with r1 using (r1+r2v)/2, and (r1+r2v)/2 is decoded using a RM(r, m?1) decoder to obtain a decoded u and a second set of decoded bits.

Abstract: In a vehicle ad-hoc network (VANET), a transceiver node is arranged in a vehicle. The node includes a protocol stack, which includes an application layer, a link layer; and a congestion control layer arranged between the application layer and the link layer. The congestion layer transfers short messages between the application layer and the link layer. The short messages are defined according to a standard for the VANET, and the congestion layer optimizes a network-wide rate allocation for the short messages.

Abstract: A wireless network master node periodically broadcasts beacons that specify a structure of a following fixed length superframe. Slave nodes determine a channel condition between each slave and the master. Then, the set of slaves is partitioned into subsets of slaves according to the channel conditions. The master assigns, to each slave, a transmission rate in a low to high order according to the channel conditions, and the slaves transmit data to the master in the low to high order between two consecutive beacons, wherein the subsets of slaves with a higher transmission rate also receive the data from the subsets of slaves with a lower transmission rate, and wherein a slave with a higher transmission rate includes a part of or all the data from a slave with a lower transmission rate.