Samsung Electronics Co., Ltd.

Qualcomm Incorporated

Eran Hof

Jun Jin Kong

Amichai Sanderovich

Evgeny Levitan

Evyatar Hemo

Sharad Deepak Sambhwani

Ning Bi

Yingyong Qi

Michel Adib Sarkis

Moshe Twitto

Michael Erlihson

Mohammad Faroq Salama

Jun-Jin Kong

Jong-Won Lee

Avner Dor

Ariel Yaakov Sagi

Elona Erez

Ran Eliyahu Hay

Shmuel Dashevsky

Liat Rahmani Kochavi

Dashevsky Shmuel

Techniques for performing gesture recognition with an electronic device are disclosed where the electronic device has a wireless communications capability using beamforming techniques and includes a plurality of millimeter wave antenna modules, each antenna module including at least one transmit antenna and at least one receive antenna, the antennas being operable in one or more frequency ranges greater than 20 GHz. Performing gesture recognition includes: simultaneous operation of the at least one transmit antenna and the at least one receive antenna so as to provide a radar capability; and detecting a presence and motion of a reflective object by analyzing magnitude and phase of signals received by the at least one receive antenna and resulting from reflection of signals transmitted by the transmit antenna and reflected by the reflective object.

Gesture detection in interspersed radar and network traffic signals

Embodiments described herein can address these and other issues by using radar machine learning to address the radio frequency (RF) to perform object identification, including facial recognition. In particular, embodiments may obtain IQ samples by transmitting and receiving a plurality of data packets with a respective plurality of transmitter antenna elements and receiver antenna elements, where each data packet of the plurality of data packets comprises one or more complementary pairs of Golay sequences. I/Q samples indicative of a channel impulse responses of an identification region obtained from the transmission and reception of the plurality of data packets may then be used to identify, with a random forest model, a physical object in the identification region.

Radio frequency (RF) object detection using radar and machine learning

Embodiments described herein can address these and other issues by using radar machine learning to address the radio frequency (RF) to perform object identification, including facial recognition. In particular, embodiments may obtain IQ samples by transmitting and receiving a plurality of data packets with a respective plurality of transmitter antenna elements and receiver antenna elements. I/Q samples indicative of a channel impulse responses of an identification region obtained from the transmission and reception of the plurality of data packets may then be used to identify, with an autoencoder, a physical object in the identification region.

A two-stage technique for compressing radar data is disclosed in which, in the first stage, Constant False Alarm Rate (CFAR) compression is conducted using a median calculated from at least one dimension of radar data, which is then used to determine filtering threshold values for all dimensions of the radar data. The radar data is then compressed by filtering the radar data based on the filtering threshold values. In the second stage, peak detection is then performed on the compressed data to identify detected objects (targets). These and other embodiments are described herein.

Efficient compression of radar data

A data journaling method in a solid state storage device, the method including: receiving a read request; determining whether updated data mapping for the read request is located in at least one of a volatile journal and a nonvolatile journal using a Bloom filter; searching the volatile journal if the updated data mapping for the request is located in the Bloom filter, and then, searching the nonvolatile journal if the updated data mapping for the request is not found in the volatile journal; and stopping the search when the updated data mapping is found.

Data journaling for large solid state storage devices with low DRAM/SRAM

A method of operating a storage device including a nonvolatile memory can be provided by receiving, from a host, address change information including changing logical addresses for data to be stored in the nonvolatile memory. Physical addresses can be sequentially allocated to the changing logical addresses included in the address change information to provide a first journal. A portion of at least one physical address allocated to the changing logical addresses can be removed to provide a second journal and the second journal can be stored in the nonvolatile memory.

Storage devices including logical to physical address mapping and methods of operating

A method of storing survivor data generated while decoding channel polarization codes in a memory module includes setting a list size that corresponds to a number of decoder units used to decode the channel polarization codes, inputting a stream of input bits to the decoder units, and sequentially decoding the input bits. Each input bit is decoded using all previous input bits decoded before the each input bit. The method further includes selecting a plurality of survivor bits from among the decoded input bits, and storing the selected survivor bits in the memory module in a binary tree configuration. The number of edges in each level of the binary tree configuration does not exceed the list size.

Efficient survivor memory architecture for successive cancellation list decoding of channel polarization codes

An apparatus for polar coding includes an encoder circuit that implements a transformation c=uB{tilde over (M)}, where u, B, {tilde over (M)}, and C are defined over a Galois field GF(2), k&#x3e;1, N=2, s&#x3c;N, u=(u, . . . , u) is an input vector of N-s symbols over GF(), Bis a permutation matrix, {tilde over (M)}=((N&#x2212;s) rows of M=), the matrix Mis a pre-defined matrix of size q&#xd7;q, 2&#x3c;q, N=qand n&#x2265;1, and C is a codeword vector of N-s symbols. A decoding complexity of C is proportional to a number of symbols in C. The apparatus further includes a transmitter circuit that transmits codeword C over a transmission channel.

Sliced polar codes

A decoder unit is configured to perform a decoding on encoded data. The decoder unit includes a data bus comprising a number N of data lines, a local memory configured to store messages for a message-passing decoding and communicate the messages across the data bus, a plurality of first decoder processing units, wherein each first decoder processing unit is configured to perform the message-passing decoding by communicating with the local memory using a number A of the data channels, and a plurality of second decoder processing units, where each second decoder processing unit is configured to perform the message-passing decoding by communicating with the local memory using a number B of the data lines. N is at least two, A and B are less than or equal to N, and A is different from B.

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Haifa, Israel

Eran Hof