Abstract: An anode active material comprising a transition metal oxide is provided. A method for preparing the anode active material may comprise the steps of preparing a first transition metal oxide source and a second transition metal oxide source; providing the first transition metal oxide source and the second transition metal oxide source for a secondary alcohol to prepare a base source; providing a hydrolysis catalyst for the base source and inducing a sol-gel reaction to prepare a transition metal oxide precursor; and subjecting the transition metal oxide precursor to heat treatment in a nitrogen environment to prepare an anode active material containing a transition metal oxide.

Abstract: A method of obtaining a plant cell having increased pathogen resistance and/or abiotic stress tolerance is provided, the method comprising the steps of a) providing a plant cell, and b) modifying the genome of the plant cell so as to obtain a decreased or inactivated expression of a protein having an amino acid sequence in which at least one part of the amino acid sequence has at least 78%, such as at least 85%, more preferably at least 90% sequence identity to SEQ ID NO: 5, and wherein the protein comprises less than 200 amino acids. A method of obtaining a plant, as well as a plant cell and a plant, are also provided.

Abstract: A memory access method used when training a deep learning network is illustrated in the present disclosure. When calculating the weightings of the current layer to the previous layer, the differential terms generated by the weighting updating calculation from the next layer to the current layer are used for reducing the access number of accessing the memory. Since the memory access method greatly reduces the access number of accessing the memory, the training time and power consumption can be reduced, and the lifetime of the battery and memory of the deep learning network device can be prolonged. Especially in the case of limited battery power, the deep learning network device can run longer.

Abstract: An integrated circuit chip and method for EEG monitoring. In one embodiment, the integrated circuit chip includes an Analog Front End cell in communication with an electrode and a Classification Processor wherein a signal received from the electrode is processed by the Classification Engine cell and designated as seizure or non-seizure. In another embodiment, the Analog Front End cell includes an amplifier cell in communication with an electrode; and an ASPU cell in communication with the amplifier cell. In yet another embodiment, the Classification Processor includes a DBE Channel Controller cell; a Feature Extraction Engine Processor cell, and a Classification Engine cell in communication with the Feature Extraction Engine Processor cells and the DBE Channel Controller cell.

Abstract: A method of generating a descriptor of at least part of an image includes receiving image data representing the at least part of the image. The image data is processed to identify at least one texture characteristic of the at least part of the image, thereby generating texture data indicative of a texture of the at least part of the image. The texture data is processed with the image data, thereby generating weighted texture data. A descriptor of the at least part of the image is generated using the weighted texture data.

Abstract: An integrated circuit chip and method for EEG monitoring. In one embodiment, the integrated circuit chip includes an Analog Front End cell in communication with an electrode and a Classification Processor wherein a signal received from the electrode is processed by the Classification Engine cell and designated as seizure or non-seizure. In another embodiment, the Analog Front End cell includes an amplifier cell in communication with an electrode; and an ASPU cell in communication with the amplifier cell. In yet another embodiment, the Classification Processor includes a DBE Channel Controller cell; a Feature Extraction Engine Processor cell, and a Classification Engine cell in communication with the Feature Extraction Engine Processor cells and the DBE Channel Controller cell.

Abstract: Disclosed are system and methods for prioritized authentication between a plurality of mobile objects. The system comprises: at least a safety application module capable of generating periodically or at specific time instants messages having at least current real-time mobility information of at least the mobile object; at least a mobility module capable of continuously tracking a real-time location information of at least the mobile object; at least a security module having at least one of a signature generation module and a signature verification module, wherein the signature generation module is capable of signing messages generated by the safety application module, wherein the signature verification module is capable of prioritizing the verification of exchanged messages between mobile objects; and at least a communication module capable of transmitting the messages signed by the security module through a network.

Abstract: This disclosure is directed to a machine-based patient-specific seizure classification system. In general, an example system may comprise a non-linear SVM seizure classification system-on-chip (SoC) with multichannel EEG data acquisition and storage for epileptic patients is presented. The SoC may integrate a hardware-efficient log-linear Gaussian Basis Function engine, floating point piecewise linear natural log, and low-noise, high dynamic range readout circuits. In at least one example implementation, the SoC may consume 1.83 ?J/classification while classifying 8 channel results with an average detection rate, average false alarm rate and latency of 95.1%, 0.94% and <2 s, respectively.

Abstract: A method of generating a descriptor of at least part of an image includes receiving image data representing the at least part of the image. The image data is processed to identify at least one texture characteristic of the at least part of the image, thereby generating texture data indicative of a texture of the at least part of the image. The texture data is processed with the image data, thereby generating weighted texture data. A descriptor of the at least part of the image is generated using the weighted texture data.

Abstract: A method of processing text having an associated source type to generate data indicative of a property associated with said text, said text comprising a plurality of tokens.

Abstract: A system and method of coordinating scheduling in a wireless communication system are described herein. In one aspect, user equipments determine one or more cells that interfere with communications received by the user equipments. The user equipment may determine particular information about the interfering cells, including a metric function based on the precoding matrix used by the interfering cell. The user equipment may transmit this information to the interfering cell in order to coordinate the use of precoding matrices between cells.

Abstract: A receiver circuit, including a multi-stage QAM de-mapper, for receiving a QAM data signal is disclosed. A first de-mapper circuit recovers a set of encoded data bits from the QAM data signal by calculating a plurality of distances between a received QAM symbol and each of a plurality of possible constellation points. A second de-mapper circuit then generates a set of unencoded data bits for the received QAM symbol based, at least in part, on the plurality of distances calculated by the first de-mapper circuit. The receiver circuit may further include a decoder circuit to decode the set of encoded data bits recovered by the first de-mapper circuit. The second de-mapper circuit may identify a subset of the plurality of possible constellation points based on a result of the decoding and select a constellation point that is associated with the shortest distance of the plurality of distances.

Abstract: Positions of non-reference wireless transceivers to be added to a wireless communication network are determined as follows. Each non-reference wireless transceiver performs measurements of times of arrival (TOA) of signals transmitted by other non-reference wireless transceivers, as well as reference wireless transceivers. Thereafter, time difference of arrival (TDOA) values are computed from at least two types of pairs of measurements as follows: (a) unknown-unknown TDOA values are obtained as differences between TOA measurements of signals transmitted by non-reference wireless transceivers and (b) unknown-known TDOA values are obtained as differences between a TOA measurement of a signal transmitted by a non-reference wireless transceiver and another TOA measurement of another signal transmitted by a reference wireless transceiver.

Abstract: Techniques for determining time of arrivals (TOAs) of signals in a wireless communication network are described. Each cell may transmit (i) synchronization signals on a set of contiguous subcarriers in the center portion of the system bandwidth and (ii) reference signals on different sets of non-contiguous subcarriers distributed across the system bandwidth. A UE may determine TOA for a cell based on multiple signals transmitted on different sets of subcarriers. The UE may perform correlation for a first signal (e.g., a synchronization signal) from the cell to obtain first correlation results for different time offsets. The UE may perform correlation for a second signal (e.g., a reference signal) from the cell to obtain second correlation results for different time offsets. The UE may combine the first and second correlation results and may determine the TOA for the cell based on the combined correlation results.

Abstract: An integrated circuit chip and method for EEG monitoring. In one embodiment, the integrated circuit chip includes an Analog Front End cell in communication with an electrode and a Classification Processor wherein a signal received from the electrode is processed by the Classification Engine cell and designated as seizure or non-seizure. In another embodiment, the Analog Front End cell includes an amplifier cell in communication with an electrode; and an ASPU cell in communication with the amplifier cell. In yet another embodiment, the Classification Processor includes a DBE Channel Controller cell; a Feature Extraction Engine Processor cell, and a Classification Engine cell in communication with the Feature Extraction Engine Processor cells and the DBE Channel Controller cell.

Abstract: A system and method of coordinating scheduling in a wireless communication system are described herein. In one aspect, cells receive information regarding one or more precoding matrices from user equipments. The information may include a request from an user equipment that the cell not use a particular precoding matrix for communications. The cell may determine whether restricting usage of the precoding matrix for communication improves overall communication in the wireless communication system by performing pair-wise comparisons of utility based on usage and restriction of the precoding matrix. The cell may further resolve conflicts that arise between several cells based on the granting of restriction of the precoding matrix.

Abstract: This disclosure is directed to a machine-based patient-specific seizure classification system. In general, an example system may comprise a non-linear SVM seizure classification system-on-chip (SoC) with multichannel EEG data acquisition and storage for epileptic patients is presented. The SoC may integrate a hardware-efficient log-linear Gaussian Basis Function engine, floating point piecewise linear natural log, and low-noise, high dynamic range readout circuits. In at least one example implementation, the SoC may consume 1.83W/classification while classifying 8 channel results with an average detection rate, average false alarm rate and latency of 95.1%, 0.94% and <2 s, respectively.

Abstract: A method of signal generation includes selecting a subset of contiguous OFDM symbols from a set of contiguous OFDM symbols, selecting a subset of contiguous subcarriers from a set of subcarriers, and generating a preamble that occupies the subset of contiguous subcarriers in the subset of contiguous OFDM symbols. The preamble includes portions in respective OFDM symbols of the subset of contiguous OFDM symbols. In the time domain each preamble portion corresponds to a repeating sequence of samples when subcarriers outside of the subset of contiguous subcarriers are filtered out. Generating the preamble may include flipping the sign of one or more occurrences of the repeating sequence for a final preamble portion and may include placing modulation symbols on regularly spaced subcarriers in the subset of contiguous subcarriers and phase-shifting the modulation symbols for a respective preamble portion with respect to a previous preamble portion.

Abstract: A receiver circuit, including a multi-stage QAM de-mapper, for receiving a QAM data signal is disclosed. A first de-mapper circuit recovers a set of encoded data bits from the QAM data signal by calculating a plurality of distances between a received QAM symbol and each of a plurality of possible constellation points. A second de-mapper circuit then generates a set of unencoded data bits for the received QAM symbol based, at least in part, on the plurality of distances calculated by the first de-mapper circuit. The receiver circuit may further include a decoder circuit to decode the set of encoded data bits recovered by the first de-mapper circuit. The second de-mapper circuit may identify a subset of the plurality of possible constellation points based on a result of the decoding and select a constellation point that is associated with the shortest distance of the plurality of distances.

Abstract: Positions of non-reference wireless transceivers to be added to a wireless communication network are determined as follows. Each non-reference wireless transceiver performs measurements of times of arrival (TOA) of signals transmitted by other non-reference wireless transceivers, as well as reference wireless transceivers. Thereafter, time difference of arrival (TDOA) values are computed from at least two types of pairs of measurements as follows: (a) unknown-unknown TDOA values are obtained as differences between TOA measurements of signals transmitted by non-reference wireless transceivers and (b) unknown-known TDOA values are obtained as differences between a TOA measurement of a signal transmitted by a non-reference wireless transceiver and another TOA measurement of another signal transmitted by a reference wireless transceiver.