Constant Signal Research Articles

A Three-Level RF-PWM Method Based on Phase-Shift Control and MPWM for ADTx

A radio frequency pulse width modulation (RF-PWM) scheme based on phase-shift control and mapping PWM (MPWM) is described. Pulse coding is the key to improving the coding efficiency, flexibility, and configurability of the all-digital transmitter (ADTx). To solve the problem that the real-time performance of the system is limited by the time resolution, the phase-shift control principle is adopted to constrain the output pulse state. It decomposes the original signal into two phase-modulated constant envelope signals, and directly converts the two-level pulse waveform by MPWM. Finally, the fast generation of the three-level digital RF modulated signal is completed by vector synthesis. In this way, the rear power amplifier can be directly driven, and the difficulty of physical implementation is greatly reduced. Different from the traditional mapping strategy of traversal search, the proposed scheme does not require complicated error calculation and comparison. Simulation and offline experiments show that the proposed scheme has better comprehensive performance than other mapping schemes. For 16QAM modulated signals at a 300 MHz carrier, the proposed scheme can achieve nearly 70% coding efficiency (CE), less than −50 dBc, and 1% adjacent channel power ratio (ACPR) and error vector magnitude (EVM).

Interpolation-based, minimum-time piecewise constant control of linear continuous-time SISO systems

We present a novel approach to the system inversion problem for linear, scalar (i.e. single-input, single-output, or SISO) plants. The problem is formulated as a constrained optimization program, whose objective function is the transition time between the initial and the final values of the system’s output, and the constraints are (i) a threshold on the input intensity and (ii) the requirement that the system’s output interpolates a given set of points. The system’s input is assumed to be a piecewise constant signal. It is formally proved that, in this frame, the input intensity is a decreasing function of the transition time. This result lets us to propose an algorithm that, by a bisection search, finds the optimal transition time for the given constraints. The algorithm is purely algebraic, and it does not require the system to be minimum phase or nonhyperbolic. It can deal with time-varying systems too, although in this case it has to be viewed as a heuristic technique, and it can be used as well in a model-free approach. Numerical simulations are reported that illustrate its performance. Finally, an application to a mobile robotics problem is presented, where, using a linearizing pre-controller, we show that the proposed approach can be applied also to nonlinear problems.

Applying Multiple Functional Connectivity Features in GCN for EEG-Based Human Identification.

EEG-based human identification has gained a wide range of attention due to the further increase in demand for security. How to improve the accuracy of the human identification system is an issue worthy of attention. Using more features in the human identification system is a potential solution. However, too many features may cause overfitting, resulting in the decline of system accuracy. In this work, the graph convolutional neural network (GCN) was adopted for classification. Multiple features were combined and utilized as the structure matrix of the GCN. Because of the constant signal matrix, the training parameters would not increase as the structure matrix grows. We evaluated the classification accuracy on a classic public dataset. The results showed that utilizing multiple features of functional connectivity (FC) can improve the accuracy of the identity authentication system, the best results of which are at 98.56%. In addition, our methods showed less sensitivity to channel reduction. The method proposed in this paper combines different FCs and reaches high classification accuracy for unpreprocessed data, which inspires reducing the system cost in the actual human identification system.

МЕТОДИКА УМЕНЬШЕНИЯ ВЫБРОСА ПЕРЕХОДНОГО ПРОЦЕССА ЦИФРОВОМ ФИЛЬТРЕ НИЖНИХ ЧАСТОТ С УПРАВЛЯЕМОЙ ЧАСТОТОЙ СРЕЗА

Digital signal processing is widely used in modern technology, including robotics, medicaltechnology, etc. Thus, the controlled digital filters are used to eliminate the constant component ofthe output signal at the output of an analog-to-digital converter. This also reduces the low-frequencyinterference level spread out on the frequency axis to the left of the lower boundary of the signalspectrum. In actual situations, signals are subjected to a variety of disruptions and noises; however,applying a filter may suppress these noises and produce a clean signal. Controllability means theexplicit dependence of the filter coefficients on the cutoff frequency. A transient occurrence can arisein a digital filter, which is indicated in the overshoot of the signal. A change in the cutoff frequencyduring filtering operation could cause this transient event. In this report, a Butterworth LPF filter isused to offer a compensation strategy for reducing this overshoot. A transient is an overshoot (drive)in the result timing chart. This drive is an after effect of the adjustment of the coefficients (boundaries)of the filter during filtering (this is classified "on-the-fly tuning"). By using the MATLAB program,the transient process resulting from the restructuring of the filter was investigated and theformulas were checked compensation of this transient process. It was found that the application ofsuch compensation reduced the negative effects of the transition process. This decrease depends onthe order of the filter, the adjustment coefficient (the ratio of cut frequencies before and after adjustment)and the moment of adjustment (for the periodic signal).

Optimizing machine learning logic circuits with constant signal propagation

Implementing dedicated machine learning hardware is becoming essential in many circumstances, raising the challenge for integrated circuit designers to reach resource requirements. Dedicated hardware for machine learning models tends to be extensively power and area-consuming. This work revisits two previously published logic simplification approaches. Moreover, it proposes two new methods to optimize circuits representing trained machine learning models as AND-Invert Graphs (AIGs) through an approximate computing strategy. We calculate the signal probability of AIG nodes based on the training set and define nodes as constants when they surpass a certain probability threshold. The constants are propagated along the AIG, removing unnecessary nodes. We take different strategies to define probability thresholds and precisely set unnecessary nodes as constants. Experimental results considering two Deep Neural Networks and a Random Decision Forest model that classifies the MNIST dataset have reached reductions of 40.9% in size and 22.1% in logic depth with a minimal impact on accuracy.

The prospects for radiofrequency electromagnetic fields control approaches improvement under 5G wireless communication technologies introduction

5G mobile communication system networks improvement leads to scenarios for the use of electromagnetic energy in various sectors of the economy variety increase and radiofrequency electromagnetic fields (EMF) person’ exposure with expansion of the exposed by occupational and non-occupational exposure contingent including. Base station EMF distinctive feature is its complex frequency-time and spatial dynamics with constant control signals that should be taken into account in modern EMF control methods. International practice in case of cellular base stations EMF assessment uses maximal values determination: theoretical and actual levels. EMF theoretical maximal values characterize base station operation mode under highest network load, highest data traffic and time-frequency radio channel resource full usage at the maximum permitted transmission power. Actual maximum EMF levels determination approach is base stations EMF evaluation alternative principle and is based on practically achievable maximum EMF emission, stochastic nature of base station signals with taking into account. The approaches to statistical assessment of actual maximum exposure levels are becoming a priority for international practice of base station EMF assessment and control due to adaptive antenna technologies in 5G cellular networks development. This approach to realistic EMF exposure assessment is fundamentally new for Russian practice and will require methodological updating of regulatory framework as well as comprehensive researches with network operators, including approaches to selective measurement results extrapolation for implementation. Ethics. The study did not require the ethics committee conclusion.

A method for infrared sensing based on oscillating zero power microelectromechanical photoswitches

This paper presents an interesting new method for measuring infrared (IR) radiation using a micromechanical photoswitch (MP). By leveraging the event-driven narrowband IR detection capability of an MP, electrostatic pull-in, and a novel integrated thermomechanical feedback mechanism, we show for the first time a zero-standby power IR sensor that can not only detect above-threshold IR power levels (>600 nW), but also generates an oscillating electromechanical pulse waveform whose frequency is a function of the incident IR power level. The device design, the theoretical model for the proposed idea, and the experimental demonstration of the mechanism are shown. The unique capability of zero-standby power IR detection and frequency-tunable AC waveform generation from a constant input signal (IR radiation) without the need for additional electronics promises to enable new functionalities for such remote wake-up sensors beyond simply detection, with potential uses in microrobotics and low-power computing.

Multiple echoes in beam spin-echo spectroscopy and their effect on measurements of ultra-fast dynamics

Helium (3He) spin-echo is a powerful experimental technique used to probe ultra-fast atomic scale surface dynamics. The analysis of these measurements is typically performed assuming there is only a single spin-echo condition, expected to produce a constant signal for pure elastic scattering, a monotonically decaying signal for quasi-elastic scattering and oscillations from inelastic scattering events. In the present work, we show that there are in fact four spin-echoes which must be correctly accounted for, and that even in the case of elastic scattering these additional echoes lead to oscillations which could mistakenly be interpreted as being due to inelastic scattering. We demonstrate that it is possible to accurately simulate the experimental data by propagating the 3He through the measured magnetic field profile of the apparatus and considering the geometry of the machine, allowing the effect of these additional echoes to be disentangled from inelastic scattering events in future 3He spin-echo measurements.

A Novel Virtual Voltage Comparison Compensation for Dynamic Voltage Restorer

In view of the high-quality requirements of the power supply quality of the user-side sensitive equipment, the system for voltage sag in the grid operation is stable and efficient. This paper proposes a new virtual voltage comparison compensation dynamic voltage recovery method. The algorithm that uses the invariant constant signal as the input quantity, and the real-side input load side signal reduces the influence of the input quantity uncertainty change on the generated compensation signal and improves the simplicity of the system operation control and the system stability. The flexibility of the proposed control strategy, as well as the effectiveness of the optimal design method, is verified by both simulation and experimental results.

A Propensity Score–Matched Comparison Between Knotless Layer-by-Layer and En Masse Suture Bridge Techniques for Delaminated Rotator Cuff Tears

Background: Because the articular layer is more prone to retraction than the bursal layer of the supraspinatus tendon, it is important to restore each layer anatomically while repairing delaminated rotator cuff tears (RCTs). Purpose: To compare clinical outcomes and tendon integrity between knotless layer–by–layer and conventional en masse repair techniques for delaminated RCTs. Study Design: Cohort study; Level of evidence, 3. Methods: We retrospectively reviewed data from 174 consecutive patients with delaminated RCTs treated by arthroscopic suture bridge repair. Only 115 patients with medium to large supraspinatus tears with delamination were included. The 33 patients treated using the knotless layer–by–layer technique (group 2) were matched 1:1 with patients treated using en masse repair with the suture bridge technique (group 1) based on propensity scores. Tendon thickness was measured on magnetic resonance imaging (MRI). Signal changes in the bursal, articular, and intratendinous layers were assessed using T2-weighted MRI. Results: Postoperatively, statistically significant improvements were seen in both groups compared with preoperatively functional scores. At the final follow–up, there was a statistically significant difference in the Constant score, which was higher in group 2 than in group 1 (91.4 ± 6.0 and 84.3 ± 16.4, respectively; P = .005). There was 1 case of a retear in each group, representing a 3% retear rate. Group 2 had thicker tendons than group 1 (6.9 ± 1.1 and 6.0 ± 1.2 mm, respectively; P = .017). On T2-weighted MRI, a low signal intensity in the articular layer was more common in group 2 (P = .046). Conclusion: En masse repair using the suture bridge technique and the knotless layer–by–layer technique were both effective. Regarding tendon healing, no significant differences were seen in retear rates. However, superior results in terms of the Constant score, tendon thickness, and signal intensity in the articular layer were observed using the knotless layer–by–layer technique.

Examining the Impact of Rotated Neck and Trunk Postures on Vertical Seat-to-Head Vibration Transmissibility and Self-Reported Discomfort

Adopting non-neutral sitting postures while exposed to whole-body vibration (WBV) can put heavy equipment operators at an increased risk for lower back pain and may cause damage to the spinal tissue. A laboratory experiment involving 11 participants (5 females, 6 males) completing four 45-min test sessions incorporating different seated conditions (vibration versus no vibration, and rotation versus no rotation) was used to assess seat-to-head transmissibility (STHT) and self-reported discomfort while in four rotated neck and trunk postures. The vibration exposure profile was a constant vertical sinusoidal signal with a frequency of 3 Hz and 0.7 m/s2 acceleration. Vibration measured at the head was greater than at the seat under all conditions, with a statistically significant effect of time (F1,10 = 101.73, p < 0.001, Eta2 = 0.910) and posture (F3,8 = 5.64, p = 0.023, Eta2 = 0.679). Mean self-reported discomfort ratings revealed increased participant discomfort in rotated neck and trunk positions in both vibration and non-vibration conditions. Increasing time also had a significant (F(1,10) = 15.53, p = 0.003) impact on higher rates of participant discomfort. Overall, it was found that increasing the degree of rotated neck and trunk position from neutral amplified the STHT and self-reported discomfort.

Self-triggered MPC with performance guarantee for tracking piecewise constant reference signals

This paper considers a self-triggered MPC controller design strategy for tracking piecewise constant reference signals. The proposed triggering scheme is based on the relaxed dynamic programming inequality and the idea of reference governor; such a scheme computes both the updated control action and the next triggering time. The resulting self-triggered tracking MPC control law preserves stability and constraint satisfaction and also satisfies certain a priori chosen performance requirements without the need to impose stabilizing terminal conditions. An illustrative example shows the effectiveness of this self-triggered tracking MPC implementation.

Minimizing L 1 over L 2 norms on the gradient

In this paper, we study the L 1/L 2 minimization on the gradient for imaging applications. Several recent works have demonstrated that L 1/L 2 is better than the L 1 norm when approximating the L 0 norm to promote sparsity. Consequently, we postulate that applying L 1/L 2 on the gradient is better than the classic total variation (the L 1 norm on the gradient) to enforce the sparsity of the image gradient. Numerically, we design a specific splitting scheme, under which we can prove subsequential and global convergence for the alternating direction method of multipliers (ADMM) under certain conditions. Experimentally, we demonstrate visible improvements of L 1/L 2 over L 1 and other nonconvex regularizations for image recovery from low-frequency measurements and two medical applications of magnetic resonance imaging and computed tomography reconstruction. Finally, we reveal some empirical evidence on the superiority of L 1/L 2 over L 1 when recovering piecewise constant signals from low-frequency measurements to shed light on future works.

Monitoring of pesticide amount in water and drinkable food by a fluorescence-based biosensor.

The identification of pollutants is crucial to protect water resources and ensure food safety. The available analytical methodologies allow reliable detection of organic pollutants such as pesticides; however, there is the need for faster, direct and continuous methodologies for real‐time monitoring of pesticides. Fluorescent‐based biosensors have been recently proposed as a valid alternative due to their advantage of being easy, cheap and specific. In this context, the aim of the present EU‐FORA fellowship programme was to develop and apply a fluorescence‐based biosensing device for the detection of organophosphate (OP) pesticides in water samples and drinkable food. The study was addressed using a mutant of the thermostable esterase‐2 from Alicyclobacillus acidocaldarius (EST2‐S35C) as a bioreceptor for OP pesticides. The use of EST2 involves some significant advantages including specificity and affinity towards OPs, and high stability over time in a different range of temperatures and pH. The protein was labelled to the fluorescent probe IAEDANS and fluorescence measurements of quenching in solution and in immobilised form were performed. The results showed good stability and sensitivity, reaching low limits of detection and quantification and a constant signal intensity over time. The addition of paraoxon quenched the fluorescence of the complex, reaching a plateau at 100 pmol paraoxon. The decrease of enzymatic activity of EST2‐S35C‐IAEDANS in the presence of paraoxon correlated the inhibition of the labelled enzyme with the decrease in fluorescence. The results from the application of the biosensor with real samples showed a decrease in fluorescence in surface water samples, contaminated by OPs. The use of the developed fluorescence‐based biosensor demonstrated its applicability for real samples monitoring and could ensure the production of large amounts of data in a short period of time which can be used to address environmental and food safety risk assessment.

An Augmented Lagrangian Model for Signal Segmentation

In this paper, we provide a new insight to the two-phase signal segmentation problem. We propose an augmented Lagrangian variational model based on Chan–Vese’s original one. Using both energy methods and PDE methods, we show, in the one-dimensional case, that the set of minimizers to the proposed functional contains only binary functions and it coincides with the set of minimizers to Chan–Vese’s one. This fact allows us to obtain two important features of the minimizers as a by-product of our analysis. First of all, for a piecewise constant initial signal, the jump set of any minimizer is a subset of the jump set of the given signal. Second, all of the jump points of the minimizer belong to the same level set of the signal, in a multivalued sense. This last property permits to design a trivial algorithm for computing the minimizers.

Reproducible fabrication of gold nanostar monolayers for surface‐enhanced Raman spectroscopy‐based trace detection

AbstractRapid, simple, and reliable detection of toxics at trace level is of great significance for public health. Surface‐enhanced Raman spectroscopy (SERS) as a powerful vibrational spectroscopic technique for molecules probing provides great advantages in trace detection; however, the low reproducibility of SERS signals from spot to spot and substrate to substrate remains a great challenge restricting its real applications. Here, we report a facile method for reproducibly preparing highly uniform self‐assembled monolayers of gold nanostars (Au NSs SAM) as SERS substrates using the classic Langmuir–Blodgett technique and introduced a statistical method to explore the signal reproducibility by t test. Our results show that the Au NSs SAM substrates fabricated in the same batch and different batches generated constant SERS signals without statistical differences and could detect dye molecules CV, R6G, and RhB in solution at concentrations as low as 10−8, 10−8, and 10−7 M, respectively. We then applied the Au NSs SAM for trace detection of Tetracycline (TC), one of the most widespread antibiotics entering the food chain and threatening the ecological balance. A detection limit of 0.05 μg/ml was achieved, with a linear response between SERS signal and TC concentration in the range of 0.05–10 μg/ml. Overall, this uniform Au NSs SAM could be reproducibly fabricated to provide a reliable platform for trace detection of TC residues or other toxics in water and is expected to have broad applications in different fields including environmental monitoring, food security, and so on.

FPGA Correlator for Applications in Embedded Smart Devices.

Correlation has a variety of applications that require signal processing. However, it is computationally intensive, and software correlators require high-performance processors for real-time data analysis. This is a challenge for embedded devices because of the limitation of computing resources. Hardware correlators that use Field Programmable Gate Array (FPGA) technology can significantly boost computational power and bridge the gap between the need for high-performance computing and the limited processing power available in embedded devices. This paper presents a detailed FPGA-based correlator design at the register level along with the open-source Very High-Speed Integrated Circuit Hardware Description Language (VHDL) code. It includes base modules for linear and multi-tau correlators of varying sizes. Every module implements a simple and unified data interface for easy integration with standard and publicly available FPGA modules. Eighty-lag linear and multi-tau correlators were built for validation of the design. Three input data sets—constant signal, pulse signal, and sine signal—were used to test the accuracy of the correlators. The results from the FPGA correlators were compared against the outputs of equivalent software correlators and validated with the corresponding theoretical values. The FPGA correlators returned results identical to those from the software references for all tested data sets and were proven to be equivalent to their software counterparts. Their computation speed is at least 85,000 times faster than the software correlators running on a Xilinx MicroBlaze processor. The FPGA correlator can be easily implemented, especially on System on a Chip (SoC) integrated circuits that have processor cores and FPGA fabric. It is the ideal component for device-on-chip solutions in biosensing.

Colorimetric aptasensor for sensitive detection of quinclorac based on exonuclease III-assisted cyclic release of phosphorodiamidate morpholino oligomer mimic enzyme strategy

Here, a colorimetric aptasensor was constructed for sensitively detecting quinclorac (QNC), a common herbicide. The aptasensor involved a novel amplification strategy and a classical strand displacement strategy. The amplification strategy, termed exonuclease III (Exo III)-assisted cyclic release of phosphorodiamidate morpholino oligomer (PMO) mimic enzyme strategy, was developed based on two new findings on PMO: 1) DNA hybridized with PMO could resist Exo III digestion; 2) a designed G-rich PMO (named P2) could bind to hemin to form a G-quadruplex PMOzyme with peroxidase-like activity. In this strategy, a designed DNA-PMO duplex (D1-P1) completely hybridized with DNA2 (D2) in the other designed DNA-PMO duplex (D2-P2) to trigger D2 degradation by Exo III and cyclic release of P2. After that, the hemin-binding P2 catalyzed colorless tetra-methyl benzidine (TMB) to blue TMB+. The cycle process was performed at high Exo III concentrations without strict control and with constant background signals. In that case, the developed strategy was sensitive, efficient, easy to operate, reliable, and ultralow background. Meanwhile, a QNC aptamer was used to develop the strand displacement strategy based on magnetic beads. The colorimetric aptasensor was sensitive and selective for QNC detection with a detection limit of 7.1 ng mL−1. It was successfully applied to detect QNC in soil and river water with good recovery rates (92–98%) and a relative standard deviation (n = 3) <5%. The success of this study could provide a general reference strategy for developing sensitive aptasensors and other nucleic acid-related sensors.

Design of a Low-Cost Low-Profile Energy Efficient 64-QAM Load Modulator

The design of a 64-QAM I/Q modulator based on load modulation is presented. The modulator is designed to operate at 3 GHz. In any standard M-QAM modulation, the RF circuitry of the voltage source generates currents on the antenna corresponding to the transmitted signals. With load modulation, the waveforms of the M-QAM constellation points are generated by changing the internal parameters of the load modulator connected to the antenna. At the same time, the voltage source is kept constant. Thus, the power amplifier amplifies a constant signal all the time and does not need to operate in the back-off region. An optimization algorithm is developed to find the design parameters of the load modulator circuit. The design parameters obtained from the algorithm have been used in the advanced design system (ADS) software to design, simulate, and fully characterize the RF load modulator circuit. The hardware prototype of the load modulator circuit is realized using microstrip lines and a diode-based switching circuit. The measurements show that the load modulator can generate outputs on the <inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula>-parameter complex plane matched to the 64-QAM constellation diagram. A microcontroller is programmed to serve the required bias voltages to the p-i-n diodes in the load modulator based on a predetermined lookup table (LUT). The proposed architecture does not require a digital-to-analog converter (DAC). A credible agreement between the simulation results and measurements is obtained.

CPD-Structured Multivariate Polynomial Optimization

We introduce the Tensor-Based Multivariate Optimization (TeMPO) framework for use in nonlinear optimization problems commonly encountered in signal processing, machine learning, and artificial intelligence. Within our framework, we model nonlinear relations by a multivariate polynomial that can be represented by low-rank symmetric tensors (multi-indexed arrays), making a compromise between model generality and efficiency of computation. Put the other way around, our approach both breaks the curse of dimensionality in the system parameters and captures the nonlinear relations with a good accuracy. Moreover, by taking advantage of the symmetric CPD format, we develop an efficient second-order Gauss–Newton algorithm for multivariate polynomial optimization. The presented algorithm has a quadratic per-iteration complexity in the number of optimization variables in the worst case scenario, and a linear per-iteration complexity in practice. We demonstrate the efficiency of our algorithm with some illustrative examples, apply it to the blind deconvolution of constant modulus signals, and the classification problem in supervised learning. We show that TeMPO achieves similar or better accuracy than multilayer perceptrons (MLPs), tensor networks with tensor trains (TT) and projected entangled pair states (PEPS) architectures for the classification of the MNIST and Fashion MNIST datasets while at the same time optimizing for fewer parameters and using less memory. Last but not least, our framework can be interpreted as an advancement of higher-order factorization machines: we introduce an efficient second-order algorithm for higher-order factorization machines.