Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of configuration modes for baseband units (BBU) and remote radio heads (RRH). For example, a computing system including a BBU and a RRH may receive a configuration mode selection including information indicative of a configuration mode for respective processing units of the BBU and the RRH. The computing system allocates the respective processing units to perform wireless processing stages associated with a wireless protocol. The BBU and/or the RRH may generate an output data stream based on the mixing of coefficient data with input data at the BBU and/or the RRH. Examples of systems and methods described herein may facilitate the processing of data for 5G wireless communications in a power-efficient and time-efficient manner.

Abstract: Provided is a scroll compressor, comprising a compression mechanism and a valve assembly. The compression mechanism is provided with a vent, and the valve assembly is used for selectively opening and closing the vent. The valve assembly comprises a valve plate and at least one valve piece. The valve plate comprises at least one valve orifice; and the valve piece is configured to selectively open and close the valve orifice. The scroll compressor further comprises a guide passageway. The guide passageway is provided with a first port communicating with the valve orifice and a second port communicating with the exhaust opening. The valve assembly and the compressor can significantly prolong the service life of the valve piece, and can provide good guidance for a fluid discharged from the compression mechanism, thereby significantly reducing the resistance of venting and the fluid pressure drop, and improving the effect of venting.

Abstract: Examples described herein include an apparatus that includes a device having a first semiconductor chip and a second semiconductor chip. The first semiconductor chip has a configurable baseband processor that is configured to mix input data with a first set of weight values corresponding to a first wireless protocol to be utilized in a wireless, RF transmission to the second semiconductor chip when the input data is a first type of data and to mix the input data with a second set and to mix the input data with a second set of weight values corresponding to a second wireless protocol to be utilized in the wireless, RF transmission to the second semiconductor chip when the input data is a second type of data.

Abstract: A wireless device includes a wireless receiver configured to receive a respective plurality of receive signals from a respective receiving antenna of a plurality of receiving antennas and an interference mitigation circuit coupled to the respective receiving antenna and including a neural network. The interference mitigation circuit is configured to receive an interference mitigation mode signal indicating two or more interference types of a plurality of interference types to mitigate. In response to the interference mitigation mode signal, the interference mitigation circuit is configured to adjust weights applied by the neural network for adjusted signals to cause the neural network to mitigate the two or more interference types of the plurality of interference types while receiving the plurality of receive signals.

Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of full duplex compensation with a self-interference noise calculator. The self-interference noise calculator may be coupled to antennas of a wireless device and configured to generate adjusted signals that compensate self-interference. The self-interference noise calculator may include a network of processing elements configured to combine transmission signals into sets of intermediate results. Each set of intermediate results may be summed in the self-interference noise calculator to generate a corresponding adjusted signal. The adjusted signal is received by a corresponding wireless receiver to compensate for the self-interference noise generated by a wireless transmitter transmitting on the same frequency band as the wireless receiver is receiving.

Abstract: Systems, methods, and apparatuses related to cooperative learning neural networks are described. Cooperative learning neural networks may include neural networks which utilize sensor data received wirelessly from at least one other wireless communication device to train the neural network. For example, cooperative learning neural networks described herein may be used to develop weights which are associated with objects or conditions at one device and which may be transmitted to a second device, where they may be used to train the second device to react to such objects or conditions. The disclosed features may be used in various contexts, including machine-type communication, machine-to-machine communication, device-to-device communication, and the like. The disclosed techniques may be employed in a wireless (e.g., cellular) communication system, which may operate according to various standardized protocols.

Abstract: Disclosed are an online program update method and device for an optical amplifier. The method comprises: when a program update instruction is sent, a Microcontroller Unit MCU receiving update programs of MCU and a programmable logic device FPGA, storing them in a program memory device, and sending an update instruction to FPGA; FPGA terminating; operations of a digital-to-analog converter DAC according to the instruction and a current state remaining unchanged; MCU loading new codes of MCU and FPGA while DAC remains in state of halting refreshing; and after MCU and FPGA run the new codes, reading previously stored data, and starting switching from a previous operation state to enter normal operation state. On basis of conventional optical amplifier control, the invention combines characteristics of MCU and FPGA, and ensures uninterrupted service of optical amplifiers, achieving smooth transition of services, thereby improving stability and reliability of whole optical communications systems.

Abstract: The present disclosure provides an electric heating device including: a protective cover provided with an inner protrusions and an outer protrusions of the protective cover; a heating cavity, where the bottom of the heating cavity adopts a closing structure that is gradually narrowed, and includes an outer wall and an inner wall of the heating cavity, where the heating cavity outer wall is provided with a protrusions on the heating cavity outer wall, where the heating cavity inner wall is provided with a protrusions on the heating cavity inner wall; and a heating means located in the heating cavity, where the inner protrusions on the heating cavity outer wall are snap-fitted with the outer protrusions of the protective cover, and where the outer protrusions on the heating cavity inner wall are snap-fitted with the inner protrusions of the protective cover.

Abstract: Examples described herein include methods, devices, and systems which compensates input data for I/Q imbalance or noise related thereto to generate compensated input data. In doing such the above compensation, during an uplink transmission time interval (TTI), a switch path is activated to provide converted input data to a receiver stage including a recurrent neural network (RNN). The RNN calculates an error representative of the noise based partly on the input signal to be transmitted and a feedback signal to generate filter coefficient data associated with the I/Q imbalance. The feedback signal is provided, after processing through the receiver, to the RNN. During an uplink TTI, the converted input data is transmitted as the RF wireless transmission via an RF antenna. During a downlink TTI, the switch path is deactivated and the receiver stage receives an additional RF wireless transmission to be processed in the receiver stage.

Abstract: Video processing matrix operations within a memory fabric, including converting a memory array into a matrix fabric for discrete cosine transform (DCT) matrix transformations and performing DCT matrix operations therein. For example, DCT matrix-matrix multiplication operations are performed within a memory device that includes a matrix fabric and matrix multiplication unit (MMU). Matrix-matrix multiplication operations may be obtained using separate matrix-vector products. The matrix fabric may use a crossbar construction of resistive elements. Each resistive element stores a level of impedance that represents the corresponding matrix coefficient value. The crossbar connectivity can be driven with an electrical signal representing the input vector as an analog voltage. The resulting signals can be converted from analog voltages to a digital values by an MMU to yield a vector-matrix product. In some cases, the MMU may additionally perform various other logical operations within the digital domain.

Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of cross correlation including symbols indicative of radio frequency (RF) energy. An electronic device including a statistic calculator may be configured to calculate a statistic including the cross-correlation of the symbols. The electronic device may include a comparator configured to provide a signal indicative of a presence or absence of a wireless communication signal in the particular portion of the wireless spectrum based on a comparison of the statistic with a threshold. A decoder/precoder may be configured to receive the signal indicative of the presence or absence of the wireless communication signal and to decode the symbols responsive to a signal indicative of the presence of the wireless communication signal. Examples of systems and methods described herein may facilitate the processing of data for wireless communications in a power-efficient and time-efficient manner.

Abstract: Apparatuses, systems, and methods related to an image processor formed in an array of memory cells are described. An image processor as described herein is configured to reduce complexity and power consumption and/or increase data access bandwidth by performing image processing in the array of memory cells relative to image processing by a host processor external to the memory array. For instance, one apparatus described herein includes sensor circuitry configured to provide an input vector, as a plurality of bits that corresponds to a plurality of color components for an image pixel, and an image processor formed in an array of memory cells. The image processor is coupled to the sensor circuitry to receive the plurality of bits of the input vector. The image processor is configured to perform a color correction operation in the array by performing matrix multiplication on the input vector and a parameter matrix to determine an output vector that is color corrected.

Abstract: Systems, apparatuses, and methods related to organizing data to correspond to a matrix at a memory device are described. Data can be organized by circuitry coupled to an array of memory cells prior to the processing resources executing instructions on the data. The organization of data may thus occur on a memory device, rather than at an external processor. A controller coupled to the array of memory cells may direct the circuitry to organize the data in a matrix configuration to prepare the data for processing by the processing resources. The circuitry may be or include a column decode circuitry that organizes the data based on a command from the host associated with the processing resource. For example, data read in a prefetch operation may be selected to correspond to rows or columns of a matrix configuration.

Abstract: Examples described herein utilize multi-layer neural networks, such as multi-layer recurrent neural networks to estimate message probability compute data based on encoded data (e.g., data encoded using one or more encoding techniques). The neural networks and/or recurrent neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous in many systems employing a neural network or recurrent neural network to estimate message probability compute data for a message probability compute (MPC) decoder. In this manner, neural networks or recurrent neural networks described herein may be used to implement aspects of error correction coding (ECC) decoders, e.g., an MPC decoder that iteratively decodes encoded data.

Abstract: Examples described herein include systems and methods which include wireless devices and systems with examples of full duplex compensation with a self-interference noise calculator that compensates for the self-interference noise generated by power amplifiers at harmonic frequencies of a respective wireless receiver. The self-interference noise calculator may be coupled to antennas of a wireless device and configured to generate the adjusted signals that compensate self-interference. The self-interference noise calculator may include a network of processing elements configured to combine transmission signals into sets of intermediate results. Each set of intermediate results may be summed in the self-interference noise calculator to generate a corresponding adjusted signal.

Abstract: Apparatuses, systems, and methods related to memory pooling between selected memory resources are described. A system using a memory pool formed as such may enable performance of functions, including automated functions critical for prevention of damage to a product, personnel safety, and/or reliable operation, based on increased access to data that may improve performance of a mission profile. For instance, one apparatus described herein includes a memory resource, a processing resource coupled to the memory resource, and a transceiver resource coupled to the processing resource. The memory resource, the processing resource, and the transceiver resource are configured to enable formation of a memory pool between the memory resource and another memory resource at another apparatus responsive to a request to access the other memory resource transmitted from the processing resource via the transceiver.

Abstract: Systems, methods, and apparatuses related to cooperative learning neural networks are described. Cooperative learning neural networks may include neural networks which utilize sensor data received wirelessly from at least one other wireless communication device to train the neural network. For example, cooperative learning neural networks described herein may be used to develop weights which are associated with objects or conditions at one device and which may be transmitted to a second device, where they may be used to train the second device to react to such objects or conditions. The disclosed features may be used in various contexts, including machine-type communication, machine-to-machine communication, device-to-device communication, and the like. The disclosed techniques may be employed in a wireless (e.g., cellular) communication system, which may operate according to various standardized protocols.

Abstract: Systems, methods, and apparatuses related to cooperative learning neural networks are described. Cooperative learning neural networks may include neural networks which utilize sensor data received wirelessly from at least one other wireless communication device to train the neural network. For example, cooperative learning neural networks described herein may be used to develop weights which are associated with objects or conditions at one device and which may be transmitted to a second device, where they may be used to train the second device to react to such objects or conditions. The disclosed features may be used in various contexts, including machine-type communication, machine-to-machine communication, device-to-device communication, and the like. The disclosed techniques may be employed in a wireless (e.g., cellular) communication system, which may operate according to various standardized protocols.

Abstract: Methods and apparatus for performing video processing matrix operations within a memory fabric. Various embodiments of the present disclosure are directed to converting a memory array into a matrix fabric for discrete cosine transform (DCT) matrix transformations and performing DCT matrix operations therein. Exemplary embodiments described herein perform DCT matrix-matrix multiplication operations within a memory device that includes a matrix fabric and matrix multiplication unit (MMU). In one embodiment, matrix-matrix multiplication operations are obtained using separate matrix-vector products. In one exemplary embodiment, the matrix fabric uses a “crossbar” construction of resistive elements. Each resistive element stores a level of impedance that represents the corresponding matrix coefficient value. The crossbar connectivity can be driven with an electrical signal representing the input vector as an analog voltage.

Abstract: Examples described herein include systems and methods which include a multiple input, multiple output transceiver including a plurality of receive antenna configured to receive a plurality of receive signals, and a wireless receiver coupled to the plurality of antenna and configured to receive and decode the plurality of receive signals. The transceiver includes a memory array and a memory controller. The memory controller includes a data address generator configured to, during the decode of the plurality of receive signals, generate at least one memory address according to an access mode of a memory command associated with a memory access operation. The at least one memory address corresponds to a specific sequence of memory access instructions to access a memory cell of the memory array.