Input-output Channels Research Articles

Quantum reading of quantum information

We extend the notion of quantum reading to the case where the information to be retrieved, which is encoded into a set of quantum channels, is of quantum nature. We use two-qubit unitaries describing the system-environment interaction, with the initial environment state determining the system’s input-output channel and hence the encoded information. The performance of the most relevant two-qubit unitaries is determined with two different approaches: (i) one-shot quantum capacity of the channel arising between environment and system’s output; (ii) estimation of parameters characterizing the initial quantum state of the environment. The obtained results are mostly in (qualitative) agreement, with some distinguishing features that include the CNOT unitary.

Mitigation of Inter-Cell Interference by using MIMO System in Visible Light Communication System

In this paper, the multiple input and multiple output system (MIMO) is investigated to mitigate the inter-cell interference (ICI) in visible light communication (VLC). The interference due to light from neighbor cell deteriorates the channel capacity of the system. Therefore, a MIMO-based VLC system is employed to optimize the capacity especially in ICI areas of the room. In this paper, the capacity of [Formula: see text], [Formula: see text] and [Formula: see text] MIMO systems is investigated and compared with the single input and single output (SISO) channel capacity without interference. The channel capacity improves by increasing the order of MIMO array and it deteriorates due to ICI. However, the effects of ICI are more severe at higher signal-to-noise ratio (SNR) especially at high interference areas. Therefore, the order of MIMO array is investigated at different SNRs in interference regions to achieve the interference-free capacity system. Simulation and theoretical results show that ICI is considerably mitigated by using MIMO system. The high SNR 42, 47 and 51.7 dB is achieved in ICI for [Formula: see text], [Formula: see text] and [Formula: see text] MIMO system, respectively.

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.

Hardware Acceleration and Implementation of YOLOX-s for On-Orbit FPGA

The rapid development of remote sensing technology has brought about a sharp increase in the amount of remote sensing image data. However, due to the satellite’s limited hardware resources, space, and power consumption constraints, it is difficult to process massive remote sensing images efficiently and robustly using the traditional remote sensing image processing methods. Additionally, the task of satellite-to-ground target detection has higher requirements for speed and accuracy under the conditions of more and more remote sensing data. To solve these problems, this paper proposes an extremely efficient and reliable acceleration architecture for forward inference of the YOLOX-s detection network an on-orbit FPGA. Considering the limited onboard resources, the design strategy of the parallel loop unrolling of the input channels and output channels is adopted to build the largest DSP computing array to ensure a reliable and full utilization of the limited computing resources, thus reducing the inference delay of the entire network. Meanwhile, a three-path cache queue and a small-scale cascaded pooling array are designed, which maximize the reuse of on-chip cache data, effectively reduce the bandwidth bottleneck of the external memory, and ensure an efficient computing of the entire computing array. The experimental results show that at the 200 MHz operating frequency of the VC709, the overall inference performance of the FPGA acceleration can reach 399.62 GOPS, the peak performance can reach 408.4 GOPS, and the overall computing efficiency of the DSP array can reach 97.56%. Compared with the previous work, our architecture design further improves the computing efficiency under limited hardware resources.

Flying-qubit control via a three-level atom with tunable waveguide couplings

The control of flying qubits is at the core of quantum networks. When the flying qubits are carried by single-photon fields, the control involves not only their logical states but also their shapes. In this paper we explore a variety of flying-qubit control problems using a three-level atom with time-varying tunable couplings to two input-output channels. It is shown that one can tune the couplings of a $\mathrm{\ensuremath{\Lambda}}$-type atom to distribute a single photon into the two channels with arbitrary shapes, or use a $\mathrm{\ensuremath{\Lambda}}$-type atom or a $V$-type atom to catch an arbitrary-shape distributed single photon. The $\mathrm{\ensuremath{\Lambda}}$-type atom can also be designed to transfer a flying qubit from one channel to the other, with both the central frequency and the photon shape being converted. With a $\mathrm{\ensuremath{\Xi}}$-type atom, one can shape a pair of correlated photons via cascaded emission. In all cases, analytical formulas are derived for the coupling functions to fulfill these control tasks. Their correlation properties and physical limitations are discussed as well. These results provide useful control protocols for high-fidelity quantum information transmission over complex quantum networks.

A two-layer transformation for zeros analysis of networked dynamical systems

Input–output properties are studied for a linear network model with homogeneous single-input single-output subsystems. Specifically, the finite- and infinite- zero structure of a channel in the network, defined by a single external actuation and measurement, is characterized. This is done by developing a two-layer transformation for the input–output model, which expresses the zero structure in terms of the subsystem model, global network interactions (graph), and the input and output locations. This algebraic characterization then provides a means to: (1) distinguish structural properties of the input–output channel (minimum and nonminimum behavior) in terms of the network’s graph, and (2) design of the graph to shape the input–output dynamics. Similar characterizations are also briefly explored for a multiple-input multiple-output (MIMO) network model with collocated measurements and actuations.

An improved platform for cultured neuronal network electrophysiology: multichannel optogenetics integrated with MEAs.

Cultured neuronal networks (CNNs) are powerful tools for studying how neuronal representation and adaptation emerge in networks of controlled populations of neurons. To ensure the interaction of a CNN and an artificial setting, reliable operation in both open and closed loops should be provided. In this study, we integrated optogenetic stimulation with microelectrode array (MEA) recordings using a digital micromirror device and developed an improved research tool with a 64-channel interface for neuronal network control and data acquisition. We determined the ideal stimulation parameters including light intensity, frequency, and duty cycle for our configuration. This resulted in robust and reproducible neuronal responses. We also demonstrated both open and closed loop configurations in the new platform involving multiple bidirectional channels. Unlike previous approaches that combined optogenetic stimulation and MEA recordings, we did not use binary grid patterns, but assigned an adjustable-size, non-binary optical spot to each electrode. This approach allowed simultaneous use of multiple input-output channels and facilitated adaptation of the stimulation parameters. Hence, we advanced a 64-channel interface in that each channel can be controlled individually in both directions simultaneously without any interference or interrupts. The presented setup meets the requirements of research in neuronal plasticity, network encoding and representation, closed-loop control of firing rate and synchronization. Researchers who develop closed-loop control techniques and adaptive stimulation strategies for network activity will benefit much from this novel setup.

Outage Performance Estimation of a MISO FSO System with OOK Signaling and Alamouti Type Space-Time Coding

In this work, multi input and single output optical wireless communication (OWC) links are analyzed, assuming the channel experiences fading due to atmospheric turbulence. OOK signaling and Alamouti-type space-time coding with intensity modulation and direct detection is assumed for weak to strong turbulence conditions modeled with the gamma–gamma distribution and closed form analytical expressions for their outage probability are derived for the first time, to the best of our knowledge. Finally, the corresponding numerical results are obtained, through the obtained mathematical expressions. Performance of a multi input and single output channel is analyzed for its outage probability as a function of power to noise ratio and normalized threshold. Three different cases for strong, medium, and weak turbulences are investigated.

An Operational Environment for Quantum Self-Testing

Observed quantum correlations are known to determine in certain cases the underlying quantum state and measurements. This phenomenon is known as (quantum) self-testing.Self-testing constitutes a significant research area with practical and theoretical ramifications for quantum information theory. But since its conception two decades ago by Mayers and Yao, the common way to rigorously formulate self-testing has been in terms of operator-algebraic identities, and this formulation lacks an operational interpretation. In particular, it is unclear how to formulate self-testing in other physical theories, in formulations of quantum theory not referring to operator-algebra, or in scenarios causally different from the standard one.In this paper, we explain how to understand quantum self-testing operationally, in terms of causally structured dilations of the input-output channel encoding the correlations. These dilations model side-information which leaks to an environment according to a specific schedule, and we show how self-testing concerns the relative strength between such scheduled leaks of information. As such, the title of our paper has double meaning: we recast conventional quantum self-testing in terms of information-leaks to an environment – and this realises quantum self-testing as a special case within the surroundings of a general operational framework.Our new approach to quantum self-testing not only supplies an operational understanding apt for various generalisations, but also resolves some unexplained aspects of the existing definition, naturally suggests a distance measure suitable for robust self-testing, and points towards self-testing as a modular concept in a larger, cryptographic perspective.

Modeling and robust decentralized control system design for plasma current and position in Damavand tokamak

This paper presents a linear dynamic model for the current and position of plasma in Damavand tokamak (DT) based on the RZIP methodology. Experimental data shots of DT are employed to validate the model and to show its accuracy. Since there are uncertainties in the model parameters, a robust decentralized control system is designed by considering the interaction between the input-output channels. Robust proportional and proportional-integral controllers are designed to obtain robust closed-loop stability and performance based on the Kharitonov theorem. The simulation results of the closed-loop system in two scenarios are presented to show the effectiveness of the introduced control system.

A router architecture with dual input and dual output channels for Networks-on-Chip

Networks-on-Chip (NoCs) are the most viable and scalable solution for connecting thousands of processing cores, and are emerging as the interconnect infrastructure for future Chip-Multiprocessors. Latency and throughput are the primary metrics for on-chip network performance, while chip area and power budgets are increasingly dominated by interconnect networks. Hence, it is critical to achieve higher performance gains at a lower cost. Previous research has proposed a router architecture that forwards packets through idle bidirectional channels to improve network performance, but it introduces significant area and power overheads. In this paper, we propose DIDO: a router architecture with dual input and dual output channels. First, the dual output channels provide extra flexibility for forwarding packets and reduce packet contention. Then, the redesigned input ports and crossbar shorten the pipeline and reduce the crossbar overhead. Finally, the removal of the virtual channel allocator allows DIDO to operate at higher frequencies. Simulation results indicate that DIDO reduces average packet latency by up to 44.8% and improves network throughput by up to 37.8% compared to the baseline router at the same frequency. Synthesis results indicate that the DIDO’s area and power overhead are comparable to the baseline router, while the maximum frequency is boosted by 18%.

Observer-Based PD Controller for a Class of High Order Linear Unstable Delayed Systems

In this paper it is considered the stabilization and control of a class of high order linear systems that are subject to constant time delay at the input-output channel. In particular, systems with two unstable poles and any number of real or complex conjugate stable poles are studied. To improve the solutions existing in the literature, a new proposal for the solution of the considered problem is taken into account consisting in the design of an observer and a ProportionalDerivative PD controller to assure stable closedloop performance, establishing by means of a frequency analysis necessary and sufficient conditions for the proposed control strategy. The proposal novelty is the inclusion of the PD control action in the observer that allows to estimate internal signals used in the solution. This configuration offers advantages in the time delay size that can be handled and on the control performance of the closed loop system. A practical design procedure is presented to determine the value of the controller gains. Numerical simulations examples are presented to show the operation of the proposal.

AdaPrune: An Accelerator-Aware Pruning Technique for Sustainable CNN Accelerators

Convolutional neural network (CNN) accelerators have achieved great success from cloud to edge scenarios. However, given the trend towards even larger and deeper neural network models, it remains a challenging problem to efficiently process these CNNs especially on edge devices with limited energy budget. Accordingly, reducing the energy consumption is of paramount importance for sustainable CNN accelerators. In this paper, we propose AdaPrune, a novel pruning technique that reduces model size and computation to achieve performance improvement and energy savings for CNN accelerators. Unlike previous pruning techniques that sacrifice either computational regularity or accuracy, AdaPrune maintains both by customizing CNN pruning for the underlying accelerators to maximally leverage the sparsity benefits. AdaPrune consists of two techniques: input channel group pruning and output channel group pruning. By analyzing the weight fetching patterns of sparse CNN accelerators, AdaPrune adaptively switches between the two techniques to guarantee that the zeros are evenly distributed in each fetching group. In doing so, the pruned network structure preserves customized computational regularity for the underlying accelerators, thereby boosting the performance and energy efficiency. We evaluate AdaPrune on three sparse CNN accelerators with different spatial tiling strategies. The experimental results show that AdaPrune achieves up to 1.6× performance speedup, and 1.5× energy savings compared to unstructured pruning.

Control device synthesis by polynomial matrix fractional descriptions method with limitation on controller structure

Modification of the algorithm for the polynomial synthesis of a multi-channel controller was proposed to preserve all control channels in this article. In order to test the functionality of the proposed modification, an example of a linear model of an unstable multi-channel plant is considered. The choice of the plant was determined by the possibility of a visual algorithm demonstration for polynomial synthesis of the controller, taking into account the proposed modifications.The plant was represented as three series-connected standard links: an aperiodic link of the first order, an unstable link, and an integrator, and has three input and two output channels. The control in the system is carried out in the feedback of the system and is summed up with the input impact. The feature of the plant is to limit the task to the second output, since it is essentially a derivative of the first output. In addition, the plant has a direct input–output channel. That is, the traversal matrix of the system is nonzero (when described through the state space). The synthesis task was set as follows: it is necessary to achieve certain quality indicators of the output vector value while maintaining all three control channels of the plant.

Progressive kernel pruning with saliency mapping of input-output channels

As the smallest structural unit of feature mapping, the convolution kernel in a deep convolution neural networks (DCNN) convolutional layer is responsible for the input channel features to output channel features. A specific convolution kernel belongs to a specific group from the perspective of the input channel, and it belongs to a specific filter from the perspective of the output channel. If the input and output channels are simultaneously considered in the pruning process, the performance of the pruning model can be further improved. This paper proposes progressive kernel pruning with salient mapping of input-output channels, introduces the concept of input-output channel saliency and defines single-port salient mapping channels and dual-port salient mapping channels. This study demonstrates that single-port salient mapping channels can ensure that each input channel signal has a relatively strong convolution kernel mapped to the output channel, and vice versa. The dual-port salient mapping channel is a channel with high feature mapping abilities from both the input and output directions. Additionally, the average mapping ability measure index is defined, which is used to control the kernel pruning process of the single-port salient mapping channel to switch to the kernel pruning process of the dual-port salient mapping channel. The experimental results and analysis show that the method proposed in this paper can be used to effectively prune a heavyweight model and a lightweight model and can obtain a better accuracy under the conditions of higher compression ratio and acceleration ratios. For example, when VGG-16 is pruned on CIFAR-10, the compression ratio and acceleration ration are 91.00× and 15.24×, respectively, and the classification accuracy of the model decreased slightly by 0.22%. When ResNet-101 is pruned on ImageNet, the compression ratio and acceleration ratio are 3.90× and 3.38×, respectively, and the classification accuracy of the model decreased slightly by 0.48%. The proposed method is significantly better than state-of-the-art methods.

The Design of MIMO Precoders Based on Decomposition Algorithms

The Wireless Communication over Multiple Input and Multiple Output (MIMO) channel increases transmission rate by splitting the input data stream into a plethora of parallel data streams that are transmitted in simultaneously. The goal of precoding at the transmitter is to divide the channel into numerous unconnected subchannels so that many data streams may be sent out at the same time. This article examines a MIMO precoder's performance utilising different channel decomposition method, as well as its computational complexity in terms of the number of floating-point operations (FLOPs). Singular Value Decomposition (SVD), Geometric Mean Decomposition (GMD), LDLH, LU, Schur, QR, and Jordan decomposition are among the techniques discussed. According to simulation findings, precoding for MIMO based on QR decomposition beats all other precoding techniques based on channel decomposition in terms of BER performance and requires less FLOPs.

The Design of MIMO Precoders Based on Decomposition Algorithms

The Wireless Communication over Multiple Input and Multiple Output (MIMO) channel increases transmission rate by splitting the input data stream into a plethora of parallel data streams that are transmitted in simultaneously. The goal of precoding at the transmitter is to divide the channel into numerous unconnected subchannels so that many data streams may be sent out at the same time. This article examines a MIMO precoder's performance utilising different channel decomposition method, as well as its computational complexity in terms of the number of floating-point operations (FLOPs). Singular Value Decomposition (SVD), Geometric Mean Decomposition (GMD), LDLH, LU, Schur, QR, and Jordan decomposition are among the techniques discussed. According to simulation findings, precoding for MIMO based on QR decomposition beats all other precoding techniques based on channel decomposition in terms of BER performance and requires less FLOPs.

Single-site indoor fingerprint localization based on MIMO-CSI

In order to achieve higher accuracy and lower cost of indoor localization, we propose a positioning method using multiple input and multiple output (MIMO) channel state information (CSI) as a fingerprint. The method can be divided into three stages, feature extraction, offline training and online localization. In the feature extraction, the segmented average and principal component analysis (PCA) are used to reduce the data dimension and decrease system complexity. In the offline training, the deep neural network (NN) model is trained to implement the position classification. In the online localization, the data are input into the trained NN model first, and then its output is further processed by weighted k-nearest neighbor (WKNN) technology to estimate the position. Experimental results show that the proposed method can significantly reduce the positioning error compared to other methods and the average error is 1.39m in a complex indoor environment.

Linear multi-photon storage based on dark modes with frequency tuning

We propose a quantum memory applicable to the optical regime based on a linear system. The system is in a symmetrical star configuration: one central mode is both connected to the input–output channel and uniformly coupled to a number of modes with tunable frequencies. We show that, as long as the number of these tunable modes is double an odd number, such a configuration is just flexible enough to perform the storage and on-demand recall of a number of individual photons. Tuning of the mode frequencies is feasible experimentally by means of adiabatic frequency tuning, and we show the system is scalable and robust against various type of imperfections. Moreover, the linearity of the system is compatible with the use of single-mode continuous-variable error correction code. Our results therefore provide a promising approach to the storage of many photons carrying protected quantum information.

Modular Data Acquisition System for Recording Activity and Electrical Stimulation of Brain Tissue Using Dedicated Electronics.

In this paper, we present a modular Data Acquisition (DAQ) system for simultaneous electrical stimulation and recording of brain activity. The DAQ system is designed to work with custom-designed Application Specific Integrated Circuit (ASIC) called Neurostim-3 and a variety of commercially available Multi-Electrode Arrays (MEAs). The system can control simultaneously up to 512 independent bidirectional i.e., input-output channels. We present in-depth insight into both hardware and software architectures and discuss relationships between cooperating parts of that system. The particular focus of this study was the exploration of efficient software design so that it could perform all its tasks in real-time using a standard Personal Computer (PC) without the need for data precomputation even for the most demanding experiment scenarios. Not only do we show bare performance metrics, but we also used this software to characterise signal processing capabilities of Neurostim-3 (e.g., gain linearity, transmission band) so that to obtain information on how well it can handle neural signals in real-world applications. The results indicate that each Neurostim-3 channel exhibits signal gain linearity in a wide range of input signal amplitudes. Moreover, their high-pass cut-off frequency gets close to making it suitable for recording both Local Field Potential (LFP) and spiking brain activity signals. Additionally, the current stimulation circuitry was checked in terms of the ability to reproduce complex patterns. Finally, we present data acquired using our system from the experiments on a living rat’s brain, which proved we obtained physiological data from non-stimulated and stimulated tissue. The presented results lead us to conclude that our hardware and software can work efficiently and effectively in tandem giving valuable insights into how information is being processed by the brain.