Signal Processing Functions Research Articles

Algorithmic Method for the Design of Sequential Circuits with the Use of Logic Elements

This article presents issues related to the design of sequential control systems. The algorithmic design method of sequential control systems is discussed, which allows the design of a diagram of any sequential system. The algorithmic method uses the description in the form of a connection formula. The connection formula defines the order of actuations of driver elements, in this case actuators. The algorithmic method is used, among others, for systems with actuators cooperating with distributors controlled electrically on both sides. The process of creating a system graph has been characterized. The operation of the system has been shown graphically. On the basis of the created graph describing the functions of signal processing, a method for rapid programming of sequential electro-pneumatic systems with the use of logic elements has been provided. A separate dedicated timing unit has been used to perform memory functions. Its operation is based on successive states, in such a way that the next state deletes the previous one. Graph-based systems have been validated through simulation using Festo’s FluidSim computer-aided design software.

Integral order photonic RF signal processors based on a soliton crystal micro-comb source

Soliton crystal micro-combs are powerful tools as sources of multiple wavelength channels for radio frequency (RF) signal processing. They offer a compact device footprint, a large number of wavelengths, very high versatility, and wide Nyquist bandwidths. Here, we demonstrate integral order RF signal processing functions based on a soliton crystal micro-comb, including a Hilbert transformer and first, second and third-order differentiators. We compare and contrast the results and the trade-offs involved with varying the comb spacing, and tap design and shaping methods.

Classification of Doppler Ultrasound Blood Flow Signals of Lower Extremity Arteries for the Early Detection of Diabetic Foot

Peripheral arterial disease is one of the key indicators of diabetic foot, which can be easily identified by ultrasound diagnostic techniques. The work aims to detect diabetic foot in early stages by classifying the blood flow signals of lower extremity arteries being captured by ultrasound doppler methods. Samples are collected from diabetic patients with and without having probable symptoms of arterial diseases. Doppler examination has been conducted on posterior tibial artery for 354 subjects with a transducer of 8 MHz frequency. The auscultation, method of listening sounds of internal organs, is employed as medical diagnostic tool for identifying pathological conditions. Each artery in the human body has a unique profile of Doppler flow. This fixed profile may be changed with the presence of a particular disease. The received signal has a spectrum of Doppler-shifted signals with respect to the existence of a velocity profile across the vessel lumen. Changes to the shape of this profile is an indicator of the severity of disease. Various features are extracted by using various statistical and signal processing functions. The feature analysis was accomplished with machine learning algorithms. Naïve Bayes, Tree and SVM algorithms are employed with MATLAB Toolboxes. Comparing the performance of these algorithms, the Tree method is found superior than the others. So, the proposed classification methodology can be employed as a key factor for the early stage detection of diabetic foot. As diabetic foot is correlated with many other parameters which effects the pressure and flow velocity of lower extremities, an integrated disease prediction model is proposed by incorporating the ultrasound doppler technique.

Nonreciprocal Transmission of Incoherent Magnons with Asymmetric Diffusion Length.

Unequal transmissions of spin waves along opposite directions provide useful functions for signal processing. So far, the realization of such nonreciprocal spin waves has been mostly limited at a gigahertz frequency in the coherent regime via microwave excitation. Here we show that, in a magnetic bilayer stack with chiral coupling, tunable nonreciprocal propagation can be realized in spin Hall effect-excited incoherent magnons, whose frequencies cover the spectrum from a few gigahertz up to terahertz. The sign of nonreciprocity is controlled by the magnetic orientations of the bilayer in a nonvolatile manner. The nonreciprocity is further verified by measurements of the magnon diffusion length, which is unequal along opposite transmission directions. Our findings enrich the knowledge on magnetic relaxation and diffusive transport and can lead to the design of a passive directional signal isolation device in the diffusive regime.

STUDY ON CARDIOVASCULAR MONITORING AND DIAGNOSIS OF ATHLETES

ABSTRACT Introduction: The study and collection of athletes’ heart function index parameters and the correct and reasonable evaluation of body functions can effectively adjust training plans and avoid athletes’ bodily exhaustion. Objective: To study the diagnosis of myocardial injury by cardiovascular monitoring in athletes from two aspects: extraction of characteristic parameters of heart function and research of signal processing. Methods: The heart function intelligent evaluation algorithm was studied by using multi-source information fusion, and embedded technology; miniature sensors were used as well. Results: The incidence of severe ventricular arrhythmia was lower in both groups. The incidence of sinus arrhythmia and intermittent second degree I atrioventricular block in the high-intensity group was significantly higher than that in the control group. The number of atrial and ventricular premature beats was lower in the control group, but increased significantly in the high-intensity group. Conclusions: This study applied the theory of multi-source information fusion to carry out representative research on the intelligent monitoring and evaluation of the heart function of elite athletes, centering on the application requirements of the heart function monitoring of elite athletes. Level of evidence II; Therapeutic studies - investigation of treatment results.

Microwave Photonic I/Q Mixer With Phase Shifting Ability

A compact system with multiple signal processing functions is of interest in many applications. The purpose of this paper is to present a microwave photonic structure that is capable of simultaneously realising in-phase/quadrature (I/Q) mixing and phase shifting operations. The multi-function signal processor has a simple structure and can be constructed using off-the-shelf components. It is designed to enable commercial modulator bias controllers to be incorporated into the system to provide accurate phase shift and to improve system stability. Non-ideal effects in the system that cause deviation in the two output IF signal phase difference from 90° can be mitigated by adjusting a modulator bias voltage. The multi-function signal processor is theoretically analysed and experimentally demonstrated. Its performance including two output IF signals with a quadrature phase difference, wideband operation, continuous 0° to 360° IF signal phase shift and long-term stable operation are verified experimentally.

Spoof surface plasmon polariton beam splitters integrated with broadband rejection filtering function

In this paper, a new kind of spoof surface plasmon polariton (SSPP) beam splitter integrated with band-rejection filtering functions is presented. Firstly, the mechanism of single-band rejection phenomena of the SSPP transmission lines (TLs) with and without T-shaped decorations are revealed through dispersion analyses. Based on the single-band rejection filter, multi-band rejection filters are further designed by introducing different intervals between the symmetric T-shaped decorations. Furthermore, we construct a compact Y-shaped beam splitter integrated with a broadband band-rejection filtering function using the SSPP TLs, which consists of an SSPP TL with rectangular grooves and two filtering branch SSPP TLs. Multiple band-rejection integrated beam splitters can also be realized by incorporating different intervals between the T-shaped decorations. The integration of the SSPP beam splitter and band-rejection filter offers compact areas, lower insertion loss and controllable passband features. It is strongly believed that that the proposed idea will raise promising possibilities in the integration of multiple microwave functions in the same component, e.g. filtering antennas, filtering switches, multifunctional radiofrequency (RF) passive and active components capable of performing several RF analog signal-processing functions in the same circuit volume, and so on.

Self‐Programming Synaptic Resistor Circuit for Intelligent Systems

Unlike artificial intelligent systems based on computers which have to be programmed for specific tasks, the human brain “self‐programs” in real time to create new tactics and adapt to arbitrary environments. Computers embedded in artificial intelligent systems execute arbitrary signal‐processing algorithms to outperform humans at specific tasks, but without the real‐time self‐programming functionality, they are preprogrammed by humans, fail in unpredictable environments beyond their preprogrammed domains, and lack general intelligence in arbitrary environments. Herein, a synaptic resistor circuit that self‐programs in arbitrary and unpredictable environments in real time is demonstrated. By integrating the synaptic signal processing, memory, and correlative learning functions in each synaptic resistor, the synaptic resistor circuit processes signals and self‐programs the circuit concurrently in real time with an energy efficiency about six orders higher than those of computers. In comparison with humans and a preprogrammed computer, the self‐programming synaptic resistor circuit dynamically modifies its algorithm to control a morphing wing in an unpredictable aerodynamic environment to improve its performance function with superior self‐programming speeds and accuracy. The synaptic resistor circuits potentially circumvent the fundamental limitations of computers, leading to a new intelligent platform with real‐time self‐programming functionality for artificial general intelligence.

An FCNN-Based Super-Resolution Mmwave Radar Framework for Contactless Musical Instrument Interface

In this article, we propose a framework for contactless human-computer interaction (HCI) using novel tracking techniques based on deep learning-based super-resolution and tracking algorithms. Our system offers unprecedented high-resolution tracking of hand position and motion characteristics by leveraging spatial and temporal features embedded in the reflected radar waveform. Rather than classifying samples from a predefined set of hand gestures, as common in existing work on deep learning with mmWave radar, our proposed imager employs a regressive full convolutional neural network (FCNN) approach to improve localization accuracy by spatial super-resolution. While the proposed techniques are suitable for a host of tracking applications, this article focuses on their application as a musical interface to demonstrate the robustness of the gesture sensing pipeline and deep learning signal processing chain. The user can control the instrument by varying the position and velocity of their hand above the vertically-facing sensor. By employing a commercially available multiple-input-multiple-output (MIMO) radar rather than a traditional optical sensor, our framework demonstrates the efficacy of the mmWave sensing modality for fine motion tracking and offers an elegant solution to a host of HCI tasks. Additionally, we provide a freely available software package and user interface for controlling the device, streaming the data to MATLAB in real-time, and increasing accessibility to the signal processing and device interface functionality utilized in this article.

Halide perovskite two-terminal analog memristor capable of photo-activated synaptic weight modulation for neuromorphic computing

Emulation of biological signal processing, learning and memory functions is essential for the development of artificial learning circuitry. Two terminal artificial synapses are supposed to be more feasible with biological system in terms of energy efficiency and processing. Here, we report on the fabrication of organic–inorganic hybrid perovskite based two-terminal artificial synapse in which the synaptic plasticity is modified by both voltage pulses and light illumination. The device emulates important synaptic characteristics, including analog memory switching, short-term plasticity, and long-term plasticity, analogous to the biological system. The change in conductance is attributed to the ion migration under external electric field. In addition, the improved post-synaptic current in optical exposer could be related to the generation of excitons and lowered Schottky barrier at perovskite/electrode interface under external electric field.

Using Transparent Adhesive Tape as New Substrate for Integrated Flexible Enzymatic Sensor: Good Adhesion and Better Printability

AbstractSubstrate plays an essential role in the construction of flexible electrode and related wearable sensors. Compared with conventional flexible substrates such as Polyethylene terephthalate (PET), the common transparent adhesive tape exhibits the unique advantages in the non‐adhesive surface with good printability, allowing the conductive layer to be deposited directly on its surface, and in another adhesive surface with good fastening, thus facilitating the fabrication of as‐prepared electrode in subsequent wearable sensors. Herein, we constructed a new type of flexible sensor to detect ascorbic acid which is closely related to human health in sweat by integrating flexible electrode based on transparent adhesive tape with potentiostat that incorporate the critical signal conditioning, processing, and transmission functions. Experiment results show that resulting electrode still has the good electrochemistry performance even after 1000 bending cycles (20 % bending degrees). By connecting as‐prepared flexible electrode to the potentiostat to carry out real time analysis, the resulting sensor exhibits excellent sensitivity, detection limit and repeatability (0.15 V detection potential vs printed Ag/AgCl reference electrode, 3.8 μM detection limit, 25 μM‐1 mM linearity, and good selectivity).

Parallel Analog Computing Based on a 2×2 Multiple-Input Multiple-Output Metasurface Processor With Asymmetric Response

We present a polarization-insensitive metasurface processor to perform spatial asymmetric filtering of an incident optical beam, thereby allowing for real-time parallel optical processing. To enable massive parallel processing, we introduce a novel Multi Input-Multi Output (MIMO) computational metasurface with an asymmetric optical response that can perform spatial differentiation on two distinct input signals regardless of their polarization. In our scenario, two distinct signals set in x and y directions, parallel and perpendicular to the incident plane, illuminate simultaneously the metasurface processor, and the resulting differentiated signals are separated from each other via appropriate Spatial Low Pass Filters (SLPF). By leveraging Generalized Sheet Transition Conditions (GSTCs) and surface susceptibility tensors, we design an asymmetric meta-atom augmented with normal susceptibilitiesto reach asymmetric optical response at normal beam illumination. Proof-of-principle simulations are also reported along with the successful realization of signal processing functions. The proposed metasurface overcomes major shortcomings imposed by previous studies such as large architectures arising from the need of additional subblocks, slow responses, and most importantly, supporting only a single input with a given polarization. Our results set the path for future developments of material-based analog computing using efficient and easy-to-fabricate MIMO processors for compact, fast, and integrable computing elements without any Fourier lens.

Red‐Light‐Stimulated Photonic Synapses Based on Nonvolatile Perovskite‐Based Photomemory

Inspired by the low energy consumption, high parallel and fault‐tolerance of visual perception system in human brain, optoelectronic devices integrated with the sensing, memory, and signal processing functionalities become the candidate to emulate functions of photonic synapse. Herein, red‐light‐stimulated nonvolatile perovskite‐based photomemory through optimized thickness of poly(3‐hexylthiophene‐2,5‐diyl)(P3HT) is demonstrated. The device emulates important synaptic functions which can be manipulated by light intensity, light pulse interval, and number of presynaptic spikes. The lower photon energy triggered photonic device paves the way for developing low energy consumption of neuromorphic computing in the future.

Real-Time Error Detection in Nonlinear Control Systems Using Machine Learning Assisted State-Space Encoding

Successful deployment of autonomous systems in a wide range of societal applications depends on error-free operation of the underlying signal processing and control functions. Real-time error detection in nonlinear systems has mostly relied on redundancy at the component or algorithmic level causing expensive area and power overheads. This paper describes a real-time error detection methodology for nonlinear control systems for detecting sensor and actuator degradations as well as malfunctions due to soft errors in the execution of the control algorithm on a digital processor. Our approach is based on creation of a redundant check state in such a way that its value can be computed from the current states of the system as well as from a history of prior observable state values and inputs (via machine learning algorithms). By checking for consistency between the two, errors are detected with low latency. The method is demonstrated on two test case simulations - an inverted pendulum balancing problem and a sliding mode controller driven brake-by-wire (BBW) system. In addition, hardware results from error injection experiments in an ARM core representation on an FPGA and artificial sensor degradations on a self-balancing robot prove the practical feasibility of implementation.

A 3D integrated neuromorphic chemical sensing system

Neuromorphic systems are essential for next-generation neuro-robotics and neuro-prosthetics with biological signal processing and memory functions. In this work, we propose a 3D integrated neuromorphic system for chemical sensing with 3D printed components, including a sensor, an electrical oscillator, and a synaptic transistor, which mimic sensory receptors, neurons, and synapses in biological systems, respectively. The 3D neuromorphic system was fabricated by 3D printing of multiple materials, resulting in significant improvements in the volumetric efficiency of the system. Through this system, different ion concentrations are detected, self-activated, and transmitted. Finally, the electrochemical neuromorphic sensing function is demonstrated by the integrated 3D neuromorphic system with artificial sensory receptors, artificial neurons, and artificial synapses for application in the detection of low concentrations of soil nutrients in smart farms.

Simulation Training of E‐Sports Players Based on Wireless Sensor Network

The application of wireless sensors in sports competitions is becoming more and more common. This research mainly discusses the simulation training of e‐sports players based on a wireless sensor network. Under the same experimental conditions, in order to avoid mutual influence and interference between the hand grip test and continuous endurance load, the exercise experiment for each subject was repeated twice. At the same time, the EMG signal collection and reaction time test during the endurance load are performed. All tests are data records before and after 40 minutes of the DOTA game competition. Before the start of each experimental test, the experimental equipment is calibrated and the parameters of the required indicators are set; the software was opened to run and checked whether it is normal; before the measurement, let the subjects perform simple preparation activities, train the subjects, and understand and be familiar with the action essentials required by the test to reduce the error. The original surface EMG signal recorded directly uses the built‐in signal processing function in the MR‐XP 1.08 master edition software to perform full‐wave rectification and smoothing. Processing of original EMG data: firstly, the EMG signal during endurance contraction is intercepted. In order to exclude individual differences in sEMG indicators of different subjects, the starting point is the first rise of each subject to 60% MVC or 25% MVC. The arrival time is the end point. In e‐sports, the reaction speed when the prompt is effective is significantly faster than when the prompt is invalid (p < 0.05). At this time, the time interval between the cue prompt and the target stimulus is 500 ms. This study is helpful to improve the athletes’ technical and tactical level.

A RECONFIGURABLE DATA FLOW ARCHITECTURE FOR SIGNAL PROCESSING APPLICATIONS

This paper aims to devise a data flow model of computation for signal processing applications in which the operational nodes are signal/image processing functions such as Pixsum, Edge, Smooth. These functions are configured during run time from a pool of reconfigurable FPGAS. Thus because of the data flow model of computation, the signal processing functions execute concurrently. At the same time, these functions by exploiting their inherent spatial parallelism execute at high speed. There is a two fold speed up in the execution of image/signal processing applications one at the architecture level wherein a node of the dataflow model executes a digital signal processing (DSP) function rather than a low level machine operation. The second speed up is due to the fact that each DSP function is configured to execute in an FPGA by using maximally the concurrent operations that such a function permits. Another significant benefit that arises from our proposed architecture is that by reconfiguring an FPGA for a DSP function at run time, the reusability of the hardware elements results in reduced cost of operations. In this paper we provide an outline of the data flow architecture and its operational aspects.

Digital signal processsing functions for ultra-low frequency calibrations

The demand from industry to produce accurate acceleration measurements down to ever lower frequencies and with ever lower noise is increasing. Different vibration transducers are used today for many different purposes within this area, like detection and warning for earthquakes, detection of nuclear testing, and monitoring of the environment. Accelerometers for such purposes must be calibrated in order to yield trustworthy results and provide traceability to the SI-system accordingly. For these calibrations to be feasible, suitable ultra low-noise accelerometers and/or signal processing functions are needed. <br />Here we present two digital signal processing (DSP) functions designed to measure ultra low-noise acceleration in calibration systems. The DSP functions use dual channel signal analysis on signals from two accelerometers measuring the same stimuli and use the coherence between the two signals to reduce noise. Simulations show that the two DSP functions are estimating calibration signals better than the standard analysis. <br />The results presented here are intended to be used in key comparison studies of accelerometer calibration systems, and may help extend current general low frequency range from e.g. 100 mHz down to ultra-low frequencies of around 10mHz, possibly using somewhat same instrumentation.

Brain‐Inspired Synaptic Resistor Circuits for Self‐Programming Intelligent Systems

Unlike artificial intelligent systems based on computers, which need to be preprogrammed for specific tasks, restricting their functions to their preprogrammed ranges, the human brain does not need to be preprogrammed, and has general intelligence to create new tactics in complex and erratic environments. The basic element in the brain, a synapse, has the function to process and learn from signals in real time by following Hebb's rule, which is a critical function missing from the transistor, the basic device in computers. In this work, a computing circuit based on synaptic resistors (synstors) with signal processing and Hebbian learning functions is modeled and analyzed. A synstor circuit emulates a neurobiological network to concurrently execute signal processing and learning algorithms in parallel mode, does not need to be preprogrammed, and has the capability to optimize and create new algorithms in complex and erratic environments with speed and energy efficiency significantly superior to those of existing computing circuits. The synstor circuit can potentially circumvent the fundamental limitations of existing computing circuits, leading to a new computing platform with real‐time self‐programming functionality and general intelligence in complex and erratic environments.

Performance comparison of all-optical logic gates using electro-optic effect in MZI-based waveguide switch at 1.46 µm

Abstract Nowadays to process information on high speed and to reduce the complexity of digital devices, optical digital computational techniques are required. Many complex signal processing functions such as multiplexing, optical switching, addressing, coding/decoding are implemented using optical digital computational techniques. Optical logic gates are one of the most essential computational elements to design combinational and sequential circuits in optical signal processing. In this paper, all optical logic gates are designed with optimized structure of 2 × 2 Mach–Zehnder interferometer optical switch using Ti:LiNbO3 based waveguide at 1.46 µm employing electro-optic effect. Different design structures to realize all-optical logic gates are presented and compared with the previously designed optical logic gates. The mathematical description of all optical logic gates using cascaded structure of Mach–Zehnder interferometer switch is also defined. The performance parameters of designed optical logic gates have been analyzed by beam propagation method and the obtained results are verified by MATLAB.