Output Channels Research Articles

Routing Single Photons from a Trapped Ion Using a Photonic Integrated Circuit

Trapped ions are promising candidates for nodes of a scalable quantum network due to their long-lived qubit coherence times and high-fidelity single- and two-qubit gates. Future quantum networks based on trapped ions will require a scalable way to route photons between different nodes. Photonic integrated circuits from fabrication foundries provide a compact solution to this problem. However, these circuits typically operate at telecommunication wavelengths that are incompatible with the strong dipole emissions of trapped ions. In this work, we demonstrate the routing of single photons from a trapped ion using a photonic integrated circuit. We employ quantum frequency conversion to match the emission of the ion to the operating wavelength of a foundry-fabricated silicon nitride photonic integrated circuit, achieving a total transmission of 31.0% \ifmmode\pm\else\textpm\fi{} 0.9% through the device. Using programmable phase shifters, we switch the single photons between the output channels of the circuit and demonstrate a 50:50 beam splitting condition. These results constitute an important step towards programmable routing and entanglement distribution in large-scale quantum networks and distributed quantum computers.

Design and Development of Improved Coyote Optimization-based Adaptive Equalization Technique for ECG Signal Transmission

Biological signals such as an electrocardiogram (ECG) are broadcast directly through wireless devices and mobile networks during patients' real-time activities and over limitless distances. However, the transmission of signals through a limited band channel or over multi-propagation suffers from limitations such as inter-symbol interference (ISI). In channel output, the adjacent symbols smudge and integrate with each other by degenerating during error analysis. Equalization filters are used to improve such kinds of deformation. A new adaptive equalization approach is proposed in this paper to extract real broadcasted signals from Gaussian noise-distorted ECG signals using the enhanced meta-heuristic algorithm. The weight optimization strategy of the "adaptive equalization technique" is implemented using oppositional searched coyote optimization algorithm (OS-COA). The main purpose of adapting the improved meta-heuristic-based adaptive equalization technique is to decrease error in the section of receivers, thus ensuring the received ECG signal is error-free. The obtained outcomes are examined to evaluate the performance of error metrics like "mean square error (MSE), and convergence rate" of the introduced model and for comparison with other existing equalization approaches. According to the experimental evaluation, the recommended adaptive linear equalization technique has better extraction performance than other blind and nonlinear equalization approaches.

MSEDDI: Multi-Scale Embedding for Predicting Drug-Drug Interaction Events.

A norm in modern medicine is to prescribe polypharmacy to treat disease. The core concern with the co-administration of drugs is that it may produce adverse drug-drug interaction (DDI), which can cause unexpected bodily injury. Therefore, it is essential to identify potential DDI. Most existing methods in silico only judge whether two drugs interact, ignoring the importance of interaction events to study the mechanism implied in combination drugs. In this work, we propose a deep learning framework named MSEDDI that comprehensively considers multi-scale embedding representations of the drug for predicting drug-drug interaction events. In MSEDDI, we design three-channel networks to process biomedical network-based knowledge graph embedding, SMILES sequence-based notation embedding, and molecular graph-based chemical structure embedding, respectively. Finally, we fuse three heterogeneous features from channel outputs through a self-attention mechanism and feed them to the linear layer predictor. In the experimental section, we evaluate the performance of all methods on two different prediction tasks on two datasets. The results show that MSEDDI outperforms other state-of-the-art baselines. Moreover, we also reveal the stable performance of our model in a broader sample set via case studies.

Fully Integrated Line Array Angular Displacement Sensing Chip

The angular displacement sensor is a digital angular displacement measurement device that integrates optics, mechanics, and electronics. It has important applications in communication, servo control, aerospace, and other fields. Although conventional angular displacement sensors can achieve extremely high measurement accuracy and resolution, they cannot be integrated because complex signal processing circuitry is required at the photoelectric receiver, which limits their suitability for robotics and automotive applications. The design of a fully integrated line array angular displacement-sensing chip is presented for the first time using a combination of pseudo-random and incremental code channel designs. Based on the charge redistribution principle, a fully differential 12-bit, 1 MSPS sampling rate successive approximation analog-to-digital converter (SAR ADC) is designed for quantization and subdivision of the incremental code channel output signal. The design is verified with a 0.35 μm CMOS process and the area of the overall system is 3.5 × 1.8 mm2. The fully integrated design of the detector array and readout circuit is realized for the angular displacement sensing.

Highly accurate millimeter wave channel estimation in massive MIMO system

AbstractAccurate channel state information (CSI) is extremely crucial to realize high‐accurate hybrid precoding, in millimeter wave communication systems. In order to improve the CSI estimation performance, several traditional channel estimators have been developed by exploiting the sparse or low‐rank property, whilst they bring huge channel training overhead and computational complexity. In this work, based on jointly sparse and low‐rank property of massive multiple input multiple output (MIMO) channel, one non‐convex mmWave channel estimation problem is formulated and a novel scheme to acquire one accurate CSI estimation result with greatly reduced training overhead is proposed. Specifically, the non‐convex problem is reformulated as two simple sub‐problems, by exploiting the alternating direction method of multipliers (ADMM) technique. Based on the low‐rank characteristic, one fast gradient descent matrix completion algorithm is developed to accurately solve the first sub‐problem. On this basis, the compressed sensing (CS) technique to acquire the accurate CSI estimation matrix is further utilized. Numerical simulation validates that the method could achieve the much higher channel estimation accuracy, yet only incurs the lower overhead compared with the traditional scheme.

Multiqubit noise deconvolution and characterization

We present a noise deconvolution technique for obtaining noiseless expectation values of noisy observables at the output of multiqubit quantum channels. For any number of qubits or in the presence of correlations, our protocol applies to any mathematically invertible noise model, even when its inverse map is not physically implementable, i.e. when it is neither completely-positive nor trace-preserving. For a generic observable affected by Pauli noise it provides a quadratic speedup, always producing a rescaling of its Pauli basis components. We show that it is still possible to achieve the deconvolution while experimentally estimating the noise parameters, whenever these are unknown (bypassing resource-heavy techniques such as quantum process tomography). We provide a simulation, with examples for both Pauli and non-Pauli channels.

Maize root behavior as three-inputs-three-outputs logical gates due to positive gravitropism and nutritropism

Root growth and their interactions can provide valuable information for the development of asynchronous logic systems. Here, maize root behavior due to positive gravitropism and nutritropism is evaluated as three-inputs-three-outputs logical gates. Using plant roots as the element for unconventional computing, the Boolean functions of each root tropism were constructed through arithmetic-logical operations. One gravity gate (rGG) and two nutrient gates (rNG1 and rNG2) were fabricated using additive manufacturing. The rGG platform was oriented with roots directly pulled down by gravity which computes (x, y, z) = (xz + yz, x + y¯z+yz¯, xy + yz), whereas specific output channels in rNG1 and rNG2 were fertigated with high phosphorus concentration resulting in (x, y, z) = (x + y + z, xy + xz, xyz) for rNG1 and (x, y, z) = (xyz, xy¯z+xyz¯, x + y + z) for rNG2. For rGG, rNG1, and rNG2, the symbols x, y, and z pertain to “root presence” in the related channel, whereas top bar on the symbols indicates “root absence”. Anatomical traits of roots were evaluated to assess possible differences in vascular tissues due to gravitropic and nutritropic responses. Overall, maize primary roots showed prominent positive gravitropism and nutritropism, and the roots that were most attracted by gravitational or nutritional stimuli showed an increase in the diameter of phloem and xylem. The logic exhibited by roots was dependent on the gravitropic and nutritropic stimuli to which they were exposed in the different logic gates. The responsiveness of maize roots to environmental stimuli such as gravity and nutrients provided valuable information to be used in computational bioelectronics.

Enhancing MIMO Spatial-Multiplexing and Parallel-Decoding under Interference by Computational Feedback

In this paper, we propose a new digital Hard-Successive-Interference-Cancellation (HSIC), the Alternating Projections-HSIC (AP-HSIC), an innovative fast computational feedback algorithm that deals with various destructive phenomena from different types of interferences. The correctness and convergence of the proposed algorithm are provided, and its complexity is given. The proposed algorithm possesses the functionality of canceling digital interference without the aid of physical feedback between the receiver and the transmitter or the loading of learning information about the state of the Multiple Input–Multiple Output (MIMO) channel to the transmitter. The proposed AP-HSIC algorithm enables a parallel decoding process from the parallel transmission of Orthogonal- Space–Time-Block-Coding (OSTBC) under a complex and challenging wireless environment to facilitate the Dynamic Spectrum Sharing (DSS) capability. We present a performance comparison of the proposed algorithm with the algorithm for Multi-Group-Space–Time-Coding (MGSTC) under MIMO fading channels and general interference or high-level Additive White Gaussian Noise (AWGN). Mathematical analysis and real-time simulations show the advantages of the proposed algorithm compared to the MGSTC decoding algorithm.

Optimization of two-dimensional generalized partial response target using multi-layer perceptron for bit-patterned media recording systems

In magnetic storage systems, the read signal that is the output of the magnetic recording channel is passed through the equalizer to convert it into the desired partial response (PR) target signal. However, mapping between the output of the recording channel and the PR target signal is a non-linear problem. Therefore, many multilayer perceptron (MLP) equalizers have been proposed. In this paper, we propose an optimization scheme of two-dimensional generalized PR target using MLP for improving the detection performance of the bit-patterned magnetic recording (BPMR) systems. The simulation results show that the proposed scheme outperforms the previous methods.

ВОЛОКОННО-ОПТИЧЕСКАЯ СИСТЕМА РАСПРЕДЕЛЕНИЯ СИГНАЛА СВЧ-ГЕТЕРОДИНА ДЛЯ АКТИВНЫХ ФАЗИРОВАННЫХ АНТЕННЫХ РЕШЕТОК

In the paper, different architectures of fiber-optic distribution systems of microwave signals for active phased array antennas are studied. The fiber-optic system with all-optical gain for microwave local oscillator signals distribution is presented. The fragment of proposed system is realized and characterized. It is experimentally shown that the developed distribution system provides noise-to-signal ratio of – 150 dBc/Hz, output channel power about 1 mW and phase fluctuation between channels less than 1°.

16-Channel Wavelength Division Multiplexers Based on Subwavelength Grating

Wavelength Division Multiplexing (WDM) plays an important role in optical interconnection. In this paper, a 16-channel WDM device is designed on a Silicon-On-Insulator (SOI) substrate by using a sub-wavelength grating (SWG) structure, which can cover O-band and C-band at the same time, and the output channel is reversely coupled from the main waveguide to realize wavelength demultiplexing. The simulation results show that the loss of our 16-channel wavelength demultiplexing device is less than 0.5 dB and the crosstalk is less than 17 dB. When WDM is performed in O-band, the transmission loss of the C-band in the bus waveguide is less than 0.05 dB and is insensitive to the grating duty cycle, with good process tolerance. The footprint of the device is 200 μm × 150 μm, and the size of the single-channel filter is 200 μm × 2 μm, which can realize WDM with large bandwidth in a compact structure.

A congestion-aware hybrid SRAM and STT-RAM buffer design for network-on-chip router

Network-on-chip (NoC) offers a scalable and flexible communication infrastructure for many-cores systems. Buffers in router is used for fine-grain flow control and Quality of Service (QoS), yet it is the major contributor of area and power consumption. In this paper, we propose a hybrid buffer design with SRAM and Spin-Torque Transfer Magnetic RAM (STT-RAM) for NoC router leveraging a novel architecture combined Virtual Channel (VC) and Virtual Output Queuing (VOQ) to store congested and uncongested flow separately. Experiments demonstrates that the proposed scheme can achieve 11.8% network performance improvement and 32.9% power saving with only 8.2% area overhead degradation compared to conventional SRAM based buffer design.

Majorization ladder in bosonic Gaussian channels

We show the existence of a majorization ladder in bosonic Gaussian channels, that is, we prove that the channel output resulting from the nth energy eigenstate (Fock state) majorizes the channel output resulting from the (n+1)th energy eigenstate (Fock state). This reflects a remarkable link between the energy at the input of the channel and a disorder relation at its output as captured by majorization theory. This result was previously known in the special cases of a pure-loss channel and quantum-limited amplifier, and we achieve here its non-trivial generalization to any single-mode phase-covariant (or -contravariant) bosonic Gaussian channel. The key to our proof is the explicit construction of a column-stochastic matrix that relates the outputs of the channel for any two subsequent Fock states at its input. This is made possible by exploiting a recently found recurrence relation on multiphoton transition probabilities for Gaussian unitaries [Jabbour and Cerf, Phys. Rev. Res. 3, 043065 (2021)]. Possible generalizations and implications of these results are then discussed.

Fully-Integrated SPAD-Based Receiver With Nanosecond Dead Time for Optical Wireless Communication

This paper presents the design and characterization of a fully-integrated receiver based on single-photon avalanche diodes (SPADs) with nanosecond dead time for high-speed high-sensitivity optical wireless communication (OWC). The receiver consists of a 4x4 SPAD array that is based on a p-well/deep n-well (DNW) structure, and each SPAD is integrated with a tunable front-end circuit to perform quench and reset. In addition, an OR tree is designed to combine the 16 channels of output from the front-end circuits to generate a single data stream for signal processing, and an output buffer is implemented as the interface to drive 50-Ω loading for testing purpose. Fabricated in a standard 180 nm CMOS process, the receiver achieves a minimum dead time of about 2.5 ns. Bit error rate (BER) measurement of the implemented receiver indicates a sensitivity of −31.6 dBm at 100 Mb/s for a BER of 2 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> and a wavelength of 520 nm, where on-off keying (OOK) modulation and a 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">15</sup> -1 pseudorandom binary sequence (PRBS-15) are employed. To recover the transmitted data stream from the received signal, a signal processing flow specific for SPAD-based receivers is proposed and implemented.

Topology design of reconfigurable power splitter with pixelated Sb-based phase change materials

The waveguide-integrated reconfigurable power splitting is of great importance for on-chip all-optical connection. Based on the combination of silicon and Sb-based phase change materials (PCMs), a series of ultra-compact reconfigurable power splitters is topologically designed. The footprints of the functional regions are kept less than 1.0λ2. The power splitting ratio between the output channels can be reconfigured through the amorphous and crystalline switching of Sb-based PCMs. We demonstrate Sb2Se3-based and Sb2S3-based digital metamaterials for the reconfiguration between power splitting ratio of 5:5 and power splitting ratios of 5:5, 6:4, 7:3, 8:2 and 9:1. The three-dimensional FDTD simulations show that the targeted power splitting ratio can be achieved with acceptable precision. Moreover, the robustness of design is validated by introducing pixel errors.

Hand pose estimation based on improved NSRM network

Hand pose estimation is the basis of dynamic gesture recognition. In vision-based hand pose estimation, the performance of hand pose estimation is affected due to the high flexibility of hand joints, local similarity and severe occlusion among hand joints. In this paper, the structural relations between hand joints are established, and the improved nonparametric structure regularization machine (NSRM) is used to achieve more accurate estimation of hand pose. Based on the NSRM network, the backbone network is replaced by the new high-resolution net proposed in this paper to improve the network performance, and then the number of parameters is decreased by reducing the input and output channels of some convolutional layers. The experiment of hand pose estimation is carried out by using public dataset, the experimental results show that the improved NSRM network has higher accuracy and faster inference speed for hand pose estimation.

Brain Computer Interface System, Performance, Challenges and Applications

Using electrodes placed on the scalp, a Brain-Computer Interface (BCI) may read electric activity in the brain and interpret it into orders to be sent to output devices. Artificial neuromuscular output channels are not used in BCIs. People with neuromuscular illnesses such as cerebral palsy, amyotrophic lateral sclerosis, spinal cord or stroke might greatly benefit from BCI since it can help them regain or maintain the abilities they once had. Standardized technological platforms have been developed as a result of massive multinational research efforts; and these platforms have the potential to be utilized to tackle intractable issues such as feature selection and segmentation, as well as the brain's incredibly complex dynamics. Researchers working on BCIs face additional challenges from the impact of time-variable psycho-neurophysiological fluctuations on brain signals, which must be overcome before the technology can be used in a plug-and-play fashion in daily life. This article provides a concise summary of the decades of research and development that have gone into BCIs so far, as well as a discussion of the most pressing issues yet to be solved.

Spoofing’s Selection and Compensation in the Global Navigation Satellite System Consumer Navigation Equipment with Multi-Channel Antenna System

A method for selecting spoofing signals in consumer equipment with a multichannel antenna system is proposed. The method provides for consistent filtering of the received signals at the outputs of all antenna system elements and by ranging codes of all navigation satellites, detection and measurement of the time delay of the true and false navigation signals in each code channel by the output signal of one of the antenna system elements, measurement of the phase difference of the signals at the outputs of the antenna system elements and the selected reference element for the estimated time delay in each channel by code. Compensation of spoofing signals is carried out in the spatial domain by estimating the correlation matrix of processes at the outputs of the antenna system channels by the corresponding countdown of code channel signals after matched filtering and forming of a weight vector while minimizing the output power of spoofing signals by directly inverting the estimate of the correlation matrix and carrying out weight processing of the adopted implementation. The simulation results are presented, confirming the efficiency of the method.

From 3D to 2D and back again

Abstract The prospect of massive parallelism of optics enabling fast and low energy cost operations is attracting interest for novel photonic circuits where 3-dimensional (3D) implementations have a high potential for scalability. Since the technology for data input–output channels is 2-dimensional (2D), there is an unavoidable need to take 2D-nD transformations into account. Similarly, the 3D-2D and its reverse transformations are also tackled in a variety of fields such as optical tomography, additive manufacturing, and 3D optical memories. Here, we review how these 3D-2D transformations are tackled using iterative techniques and neural networks. This high-level comparison across different, yet related fields could yield a useful perspective for 3D optical design.

Segment-Less Continuous Speech Separation of Meetings: Training and Evaluation Criteria

Continuous Speech Separation (CSS) has been proposed to address speech overlaps during the analysis of realistic meeting-like conversations by eliminating any overlaps before further processing. CSS separates a recording of arbitrarily many speakers into a small number of overlap-free output channels, where each output channel may contain speech of multiple speakers. Often, a separation model is trained with Utterance-level Permutation Invariant Training (uPIT), which exclusively maps a speaker to an output channel, and applied in a sliding window approach called stitching. Recently, we introduced an alternative training scheme called Graph-PIT that teaches the separator to produce a speaker-shared output channel format without stitching. It can handle an arbitrary number of speakers as long as the number of overlapping speakers is never larger than the number of output channels. Models trained in this way are able to perform segment-less CSS, i.e., without stitching, and achieve comparable and often better separation quality than the conventional CSS with uPIT and stitching. In this contribution, we further investigate the Graph-PIT training scheme. We show in extended experiments that Graph-PIT also works in challenging reverberant conditions. We simplify the training schedule for Graph-PIT with the recently proposed Source Aggregated Signal-to-Distortion Ratio (SA-SDR) loss, which eliminates unfavorable properties of the previously used A-SDR loss to enable training with Graph-PIT from scratch. Furthermore, we introduce novel signal-level evaluation metrics for meeting scenarios, namely the source-aggregated scale- and convolution-invariant Signal-to-Distortion Ratio (SA-SI-SDR and SA-CI-SDR), which are generalizations of the commonly used SDR-based metrics for the CSS case.