Output Channels Research Articles

OTFS—A Mathematical Foundation for Communication and Radar Sensing in the Delay-Doppler Domain

Orthogonal time frequency space (OTFS) is a framework for communication and active sensing that processes signals in the delay-Doppler (DD) domain. This paper explores three key features of the OTFS framework, and explains their value to applications. The first feature is a compact and sparse DD domain parameterization of the wireless channel, where the parameters map directly to physical attributes of the reflectors that comprise the scattering environment, and as a consequence these parameters evolve predictably. The second feature is a novel waveform / modulation technique, matched to the DD channel model, that embeds information symbols in the DD domain. The relation between channel inputs and outputs is localized, non-fading and predictable, even in the presence of significant delay and Doppler spread, and as a consequence the channel can be efficiently acquired and equalized. By avoiding fading, the post equalization SNR remains constant across all information symbols in a packet, so that bit error performance is superior to contemporary multi-carrier waveforms. Further, the OTFS carrier waveform is a localized pulse in the DD domain, making it possible to separate reflectors along both delay and Doppler simultaneously, and to achieve a high-resolution delay-Doppler radar image of the environment. In other words, the DD parameterization provides a common mathematical framework for communication and radar. This is the third feature of the OTFS framework, and it is ideally suited to intelligent transportation systems involving self-driving cars and unmanned ground/aerial vehicles which are self/network controlled. The OTFS waveform is able to support stable and superior performance over a wide range of user speeds.

Improving signal fidelity for deep learning‐based seismic interference noise attenuation

AbstractDeep learning has shown a considerable potential to significantly improve processing efficiency but has not yet been widely deployed to production projects of seismic signal separation such as seismic interference attenuation. The main reasons are: First, the industry has high standards for signal fidelity, which are critical for the success of subsequent seismic imaging, and deep neural network methods have not yet matched the required level; second, the network's interpretability issue has affected many geophysicists and sponsors’ trust in the deep learning technique. To develop deep neural network methods towards the end of benefiting real‐world production, we first attempt to better understand their performance, especially in how they make use of local and global features of the data. A novel quantitative research of the overall network model behaviour on synthetic data is conducted. We simulate three types of coherent seismic data components in the shot domain, blend them together and then train a network to separate them. In this process, random noise, a component having only learnable local features, is selectively injected into the network's training pairs. Three network models sharing the same architecture are trained individually, and they show distinctive behaviours when applied to the same test data. Step‐by‐step analysis of each of them reveals that training the network with additional random noise injected into both the input and the output channel of the desired signal can lead to a decent prediction of the coherent noise based on good learning of the global features and, in the meantime, preserve almost all the data information from being lost. We propose this key lesson we learnt as a new method to improve the network's signal fidelity for shot‐domain seismic interference attenuation, which is essentially a signal separation task. Its effectiveness is demonstrated on field data from Africa with a comparison to a conventional physics‐based seismic interference attenuation method used in production.

Low-Complexity Near-Optimum Symbol Detection Based on Neural Enhancement of Factor Graphs

We consider the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Based on the Ungerboeck observation model, a detection algorithm with appealing complexity properties can be derived. However, since the underlying factor graph contains cycles, the sum-product algorithm (SPA) yields a suboptimal algorithm. In this paper, we develop and evaluate efficient strategies to improve the performance of the factor graph-based symbol detection by means of neural enhancement. In particular, we consider neural belief propagation and generalizations of the factor nodes as an effective way to mitigate the effect of cycles within the factor graph. By applying a generic preprocessor to the channel output, we propose a simple technique to vary the underlying factor graph in every SPA iteration. Using this dynamic factor graph transition, we intend to preserve the extrinsic nature of the SPA messages which is otherwise impaired due to cycles. Simulation results show that the proposed methods can massively improve the detection performance, even approaching the maximum a posteriori performance for various transmission scenarios, while preserving a complexity which is linear in both the block length and the channel memory.

Fundamental limits on concentrating and preserving tensorized quantum resources

Quantum technology offers great advantages in many applications by exploiting quantum resources like nonclassicality, coherence, and entanglement. In practice, an environmental noise unavoidably affects a quantum system and it is thus an important issue to protect quantum resources from noise. In this paper, we investigate the manipulation of quantum resources possessing the so-called tensorization property and identify the fundamental limitations on concentrating and preserving those quantum resources. We show that if a resource measure satisfies the tensorization property as well as the monotonicity, it is impossible to concentrate multiple noisy copies into a single better resource by free operations. Furthermore, we show that quantum resources cannot be better protected from channel noises by employing correlated input states on joint channels if the channel output resource exhibits the tensorization property. We address several practical resource measures where our theorems apply and manifest their physical meanings in quantum resource manipulation.

A robust integrated control design for weight-on-bit fluctuation suppression in drilling process subject to fractured formations

Fractured formations lead to insufficient contact or loss of contact between the drill bit and rocks, which subsequently causes notable fluctuation in weight-on-bit due to flexible drill strings. This paper presents a robust integrated control design to suppress weight-on-bit fluctuation. First, a finite element drill-string longitudinal model is employed as the basis of control design, which has been verified using actual run data. The integrated controller contains a proportional–integral (PI) controller and a dynamic output feedback controller, dealing with the system’s first-order and high-order dynamics. The dynamic output feedback controller only needs to focus on the system’s high-order dynamics since the controller synthesis is based on the pre-designed PI controller. The controller decreases the flexible mode amplitude from disturbance to the output channel through H-infinity loop shaping, making the closed-loop system less flexible. The numerical results demonstrate that the developed approach can effectively suppress weight-on-bit fluctuation due to drill-string flexibility when drilling in fractured formations.

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.

Fully controllable multichannel waveguides induced by counterpropagating Bessel beams

We theoretically analyze the waveguiding structures photo-induced by two incoherent counter-propagating Bessel beams (BBs) in a biased photorefractive crystal. We demonstrate that the cross-coupling of two BBs enables adressable channels and tunability of the forming guiding structures. The truncation parameter of the BBs, their Bessel orders and the misalignment between the two beams are all key parameters for tailoring the characteristics of the photo-induced waveguides such as the number of outputs, the output intensity levels and the distance between each output channel. Accordingly, we optimized the different parameters for designing not only a fully tunable Y-coupler but also optical splitters with up to five outputs and even more complex star couplers for all-optical interconnect applications. Finally, we report on the stability behavior of the photo-induced platform. The stability threshold depends on the nonlinearity parameter beyond which the beams display time-periodic, quasi-periodic and turbulent dynamics where spatially localized instabilities can be observed. All these results suggest more opportunities for fully controllable complex waveguiding structures and new all-optical solutions for active components in optical telecommunication and innovative ways of performing optical computing based on spatiotemporal chaos.

Evolutionary gravitational Neocognitron neural network based differential spatial modulation detection scheme for uplink multiple user huge MIMO systems

Interfering manipulation is a growing number of bright aspects for 6th generation (6G) infrastructure, particularly to detect huge sequence quadrature amplitude modulation (QAM) signals. In this manuscript, an evolutionary gravitational Neocognitron neural network based differential spatial modulation detection scheme (EGNNN-DSMDS) is proposed for uplink multiple user huge multiple input multiple output (MIMO) systems. To prevent the wastage of antenna resources, massive MIMO uplink systems use the differential spatial modulation (DSM), here the transmitter is equipped with a small count of antennas, whereas the receiver is equipped with a huge count of antennas. By utilizing the asymptotic characteristics of massive multiple input multiple output channels, the transmitting signal can be uniquely decoded without instantaneous channel state information in the receiver. When analyzed with conventional coherent zero-forcing detector (ZFD), the proposed approach has less complication. The proposed EGNNN-DSMDS detection scheme achieves excellent bit error ratio performance and estimating channel errors. EGNNN-DSM attains higher spectral efficiency 99.56%, 96.33%, 98.11%, lower BER 2.5%, 4.13%, 1.19% and lower computational complexity 1.19%, 2.16%, 1.15% compared with existing methods, like MLPNN-MUD-UMM-MIMO, CNN-LASD-UMM-MIMO, and DNN-SRD-UMM-MIMO. Finally, the proposed EGNNN-DSM detection scheme attains the better outcomes.

Functional interval observer design for linear time-varying systems with additive disturbances

This paper investigates the problem of the functional interval observer design for linear time-varying (LTV) systems with additive disturbances in both input and output channels. First, sufficient conditions for the existence of a functional interval observer for LTV systems are proposed. Based on the solution of a type of Sylvester matrix equations (generalized Sylvester equations, GSEs), completely parameterized expressions of functional interval observer coefficient matrices are established, yielding a simple and effective design approach for solving the interval estimation issue of the LTV system, while the free parameters in the expressions provide the design degrees of freedom that can be utilized to achieve additional system specifications. Furthermore, the developed observer may execute state estimation at the desired convergence rate, considerably improving estimate performance. Finally, a numerical example and a back-to-turn (BTT) aircraft control example are presented, with the results showing that the upper and lower bounds provided by the designed observer provide a better performance of interval estimation of the functional state variables, demonstrating the effectiveness of the proposed method.

Reliable nonfragile tracking control for switched systems with external disturbances

AbstractWith the assistance of reliable control technique, the nonfragile tracking problem has been proposed in this paper for a class of switched systems with external disturbances under the aegis of modified repetitive controller. Notably, the designed repetitive controller is used to improve the tracking performance of the addressed switched systems. Preciously, the influence of external disturbances are estimated through the improved equivalent‐input‐disturbance strategy, wherein the effect caused by the external disturbances to the output channel are reduced by the aid of modified repetitive control strategy. The fundamental intention of this control synthesis is that the output of the system precisely tracks the reference signal even in the presence of external disturbances and gain fluctuations. For that cause, Lyapunov stability technique in conjunction with average dwell time approach is implemented to obtain adequate conditions in the shape of linear matrix inequalities which insists the exponential stability for the addressed system. Ultimately, the efficiency and supremacy of the proposed control schemes are justified via two numerical examples, wherein it is exposed to view that the developed control strategy is capable of good tracking performance and also estimates the external disturbances efficiently.

Discrete Multiple Second-Order Generalized Integrator With Low-Pass Filters and Frequency-Locked Loop for DC Rejection

The multiple second-order generalized integrator (MSOGI), which is composed of parallel second-order generalized integrators (SOGI), is widely used in grid synchronization and signal extraction, and it usually suffers from dc offset issue. In continuous domain, low-pass filter (LPF) can be added to SOGI to eliminate the dc offset in its quadrature channel. However, in digital implementation, directly discretized LPF-based methods may not be able to eliminate dc offset and ac discretization error simultaneously, where the steady-state error will also be introduced by the conventional frequency-locked loop (FLL). In this article, a modified impulse invariant method is adopted to discretize SOGIs, which presents zero ac errors and good dynamics, but shows dc offset in both output channels. Then, an LPF is introduced to eliminate the dc offset of the discrete SOGI, forming a discrete SOGI-LPF. And, a discrete MSOGI-LPF (DMSOGI-LPF) is formed by paralleling multiple DSOGI-LPFs, which can extract different frequency components. Furthermore, a discrete FLL is proposed to estimate the frequency of the input signal, which suppresses the influence of the dc offset and achieves good dynamics. Finally, the stability analysis of the proposed method based on discrete linear-time periodic (LTP) model is provided. The effectiveness of the proposed method and its discrete LTP model are validated through experimental results.

An Efficient Implementation of Convolutional Neural Network With CLIP-Q Quantization on FPGA

Convolutional neural networks (CNNs) have achieved tremendous success in the computer vision domain recently. The pursue for better model accuracy drives the model size and the storage requirements of CNNs as well as the computational complexity. Therefore, Compression Learning by InParallel Pruning-Quantization (CLIP-Q) was proposed to reduce a vast amount of weight storage requirements by using a few quantized segments to represent all weights in a CNN layer. Among various quantization strategies, CLIP-Q is suitable for hardware accelerators because it reduces model size significantly while maintaining the full-precision model accuracy. However, the current CLIP-Q approach did not consider the hardware characteristics and it is not straightforward when mapped to a CNN hardware accelerator. In this work, we propose a software-hardware codesign platform that includes a modified version of CLIP-Q algorithm and a hardware accelerator, which consists of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times 5$ </tex-math></inline-formula> reconfigurable convolutional arrays with input and output channel parallelization. Additionally, the proposed CNN accelerator maintains the same accuracy of a full-precision CNN in Cifar-10 and Cifar-100 datasets.

Calcium-permeable AMPA receptors on AII amacrine cells mediate sustained signaling in the On-pathway of the primate retina.

Midget and parasol ganglion cells (GCs) represent the major output channels from the primate eye to the brain. On-type midget and parasol GCs exhibit a higher background spike rate and thus can respond more linearly to contrast changes than their Off-type counterparts. Here, we show that a calcium-permeable AMPA receptor (CP-AMPAR) antagonist blocks background spiking and sustained light-evoked firing in On-type GCs while preserving transient light responses. These effects are selective for On-GCs and are occluded by a gap-junction blocker suggesting involvement of AII amacrine cells (AII-ACs). Direct recordings from AII-ACs, cobalt uptake experiments, and analyses of transcriptomic data confirm that CP-AMPARs are expressed by primate AII-ACs. Overall, our data demonstrate that under some background light levels, CP-AMPARs at the rod bipolar to AII-AC synapse drive sustained signaling in On-type GCs and thus contribute to the more linear contrast signaling of the primate On- versus Off-pathway.

Об управляемости и наблюдаемости многоканальных САУ при синтезе модальным методом с использованием полиномиального матричного разложения объекта и регулятора

When considering the issue of synthesis of an automatic control system by a modular method using a polynomial decomposition of the transfer functions of an object and a controller, synthesis algorithms for fully controlled systems are proposed. However, the question arises about the possibilities of using this algorithm if this condition is not met. The consideration of this is-sue turned out to be especially relevant for multichannel models of objects with a non-square transfer function (having an unequal number of input and output channels). It is shown that for some fundamental terms of the theory of automatic control, such as controllability, reachability, observability, stability and some others, there are special definitions of them in the case of considering this type of objects. The term non‒square object is proposed for use, which is used mainly in foreign literature. Some restrictions on the modal synthesis of regulators by a method using a polynomial matrix separation of the object and the regulator are considered. Examples of internally and asymptotically unstable systems are given. A hypothesis is put forward about the stability of the controlled system. An example of a multichannel system "inverted pendulum on a cart" is considered, which is an object with a non-square matrix transfer function (in this example, the number of input actions is less than the number of output parameters). Using the static characteristics of this object, it is demonstrated that not always controlled systems can be stabilized in a given position. For example, in the case of setting the desired angle of an inverted pendulum other than zero, it is impossible to hold the position of the cart in a given coordinate. At the same time, if you set the angle at the equilibrium point as the desired angle of the inverted pendulum, then stabilization of the cart at a given coordinate becomes possible.

Probing the structure and function of locus coeruleus projections to CNS motor centers.

The brainstem nucleus locus coeruleus (LC) sends projections to the forebrain, brainstem, cerebellum and spinal cord and is a source of the neurotransmitter norepinephrine (NE) in these areas. For more than 50 years, LC was considered to be homogeneous in structure and function such that NE would be released uniformly and act simultaneously on the cells and circuits that receive LC projections. However, recent studies have provided evidence that LC is modular in design, with segregated output channels and the potential for differential release and action of NE in its projection fields. These new findings have prompted a radical shift in our thinking about LC operations and demand revision of theoretical constructs regarding impact of the LC-NE system on behavioral outcomes in health and disease. Within this context, a major gap in our knowledge is the relationship between the LC-NE system and CNS motor control centers. While we know much about the organization of the LC-NE system with respect to sensory and cognitive circuitries and the impact of LC output on sensory guided behaviors and executive function, much less is known about the role of the LC-NE pathway in motor network operations and movement control. As a starting point for closing this gap in understanding, we propose using an intersectional recombinase-based viral-genetic strategy TrAC (Tracing Axon Collaterals) as well as established ex vivo electrophysiological assays to characterize efferent connectivity and physiological attributes of mouse LC-motor network projection neurons. The novel hypothesis to be tested is that LC cells with projections to CNS motor centers are scattered throughout the rostral-caudal extent of the nucleus but collectively display a common set of electrophysiological properties. Additionally, we expect to find these LC projection neurons maintain an organized network of axon collaterals capable of supporting selective, synchronous release of NE in motor circuitries for the purpose of coordinately regulating operations across networks that are responsible for balance and movement dynamics. Investigation of this hypothesis will advance our knowledge of the role of the LC-NE system in motor control and provide a basis for treating movement disorders resulting from disease, injury, or normal aging.

High efficient low cost gamma-ray radiation sensor based on IoT platform

In the radiation sensing community, it's very important to detect gamma-ray radiation using remote sensing like IoT platforms. In this paper, high efficient low cost gamma-ray radiation sensor is designed to monitor and early detect radioactive materials that are present in the environment, and this is to be achieved based on IoT platform. Such platform provides a safer monitoring technique that helps avoiding the direct exposure to radiation and allows the exploration of inaccessible places. This paper conducts a deep informed study of N-channel (NMOS) and P-channel (PMOS) MOSFETs to be used as a dosimeter and sensor for gamma-ray radiation. The proposed technique herein uses the active load configuration of MOSFETs to detect the output current or the channel on resistance.MOSFET drain current and channel resistance values are changed based on variations of threshold voltage (VT) that are affected directly by the dose of absorbed gamma-ray radiation. For the purpose of sensing the ionizing radiation, the change ratios of output current or channel resistance values; due to threshold voltage shifting, are measured. A complete prototype of the proposed gamma-ray radiation IoT monitoring solution has been built and tested in a real gamma radiation environment over a dose range from 10 Gy up to 40 Gy. The highest current sensitivity of the proposed dosimeter was 0.579 mA/Gy and 0.529 mA/Gy for NMOS and PMOS respectively. The practical results show that the IoT monitoring radiation system is able to efficiently detect the gamma radiation remotely using low cost MOSFET transistors.

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.

A Lightweight Residual Model for Corrosion Segmentation with Local Contextual Information

Metal corrosion in high-risk areas, such as high-altitude cables and chemical factories, is very complex and inaccessible to people, which can be a hazard and compromise people’s safety. Embedding deep learning models into edge computing devices is urgently needed to conduct corrosion inspections. However, the parameters of current state-of-the-art models are too large to meet the computation and storage requirements of mobile devices, while lightweight models perform poorly in complex corrosion environments. To address these issues, a lightweight residual deep-learning model based on an encoder–decoder structure is proposed in this paper. We designed small and large kernels to extract local detailed information and capture distant dependencies at all stages of the encoder. A sequential operation consisting of a channel split, depthwise separable convolution, and channel shuffling were implemented to reduce the size of the model. We proposed a simple, efficient decoder structure by fusing multi-scale features to augment feature representation. In extensive experiments, our proposed model, with only 2.41 MB of parameters, demonstrated superior performance over state-of-the-art segmentation methods: 75.64% mean intersection over union (IoU), 86.07% mean pixel accuracy and a 0.838 F1-score. Moreover, a larger version was designed by increasing the number of output channels, and model accuracy improved further: 79.06% mean IoU, 88.07% mean pixel accuracy, and 0.891 F1-score. The size of the model remained competitive at 8.25 MB. Comparison work with other networks and visualized results were used for validation and to determine the accuracy of metal corrosion surface segmentation with limited resources.

High performance chirality-distinguishing beamsplitter based on the dielectric-metal hybrid waveguides

Chirality of photons plays an important role in light-matter interaction, however, efficient and broadband chirality distinguition in the photonic integrated circuits (PICs) is a great challenge. In this Letter, a novel chirality-distinguishing beamsplitter (CDBS) based on the dielectric-metal hybrid waveguides is proposed. It consists of an integrated polarization converter and a polarization beam splitter (PBS). Left-handed and right-handed circularly polarized (LCP and RCP) light are first respectively converted into the orthogonal fundamental transverse-electric and transverse-magnetic​ modes in an integrated polarization converter, which are then split to different output channels of a PBS. Our CDBS shows the advantages of one of the lowest insertion losses (<1.3 dB), a small footprint (11.6 × 2 ×2μm3), a broad operating bandwidth (1.45–1.65μm) and one of the highest chirality isolations (>11.7 dB) between the LCP and RCP light. In addition, the proposed device can be used for emitting photons with a selective spin angular momentum due to the reciprocal transmission of light. Our device offers robust functionality of circularly polarized light distinguition and generation by using PICs, and has potential applications in nanophotonics, biology and quantum information processing.

Proportional-Integral Event-Triggered Control of Networked Systems With Unmatched Uncertainties

This article proposes a proportional-integral (PI) event-triggered control strategy for networked nonlinear systems subject to unmatched uncertainties. The influences of the unmatched uncertainties and external disturbances on the output channel are attenuated by an extended-state-observer-based output feedback controller through a state transformation. The outputs of the controller are transmitted in an event-triggered fashion by comparing the difference between the current sampling error and its integration with respect to time. We prove the stability of the closed-loop event-triggered control system by regulating the trajectories of the observation and tracking errors into a compact set. Further, the relationships between the control performance and designed parameters are investigated quantitatively. In addition, Zeno-freeness is guaranteed by proving the existence of a nonzero minimum inter-event time. Finally, the effectiveness of the proposed PI-type event-triggered control strategy in reducing signal transmission rate is validated through numerical examples and various experiments.