A method for rejecting 3k-th harmonics in bandpass 6N-path filters

In this paper, a method for 3 -th ( ) harmonics rejection in 6 -path filters is proposed, and the related analysis is provided. Using a single-ended-input to differential-output structure, the filter selectivity around even harmonics are also suppressed. Accordingly, a proof-of-concept band-pass filter is designed, and postlayout simulations in the 90-nm CMOS technology are carried out, which covers an input frequency range from 200 MHz to 1.2 GHz with a channel bandwidth of 10 to 15.5 MHz. The achieved third harmonic rejection at 1-GHz local oscillator (LO) frequency is about 43 dB. Over the entire radio frequency (RF) range, the in-band IIP3 and noise figure are better than 1.5 dBm and 5.3 dB, respectively. The power consumption of the analog circuitry is 21 mW from the 1.2-V supply, whereas the digital clock generation circuitry consumes between 0.9 and 5.2 mW, depending on the center frequency of the filter.     

FPGA-based system for effective IQ imbalance mitigation of RF power amplifiers

To provide an adequate signal integrity to a power amplifier (PA), we propose a digital system for the degradation at the transmitter path, and it is implemented on a field-programmable gate array (FPGA) board. The proposed system offers the following features: A -ary quadrature amplitude modulation (QAM) digital signal generation and in-phase/quadrature (IQ) imbalance mitigation, and by default, it performs as a predistortion model extraction from PA-measured data. The simulations and tests provided are performed to effectively verify the PA linearity by using 256-QAM signals. The nonlinearities are predicted as a reliable solution for linearizing the PA from measurements of AM/AM and AM/PM conversion curves. The performance is evaluated in terms of linearity, computation complexity, and FPGA hardware synthesis according to a dependability compliance of digital signal processing. Finally, the model is validated with input/output data observations to linearize the model with a fitting normalized mean squared error (NMSE) of around  dB, a spurious free dynamic range of 40 dBm, and an adjacent channel power ratio reduction by  dBm, for a class-AB broadband radio frequency PA GaN HEMT of 10 W working at 2.34 GHz.     

A new method to realize nonrational driving-point functions

This paper presents a new synthesis procedure for nonrational driving-point functions by defining and using the operator. The operator is defined, and its properties are explored. Applying the Stieltjes transform on the operator, the Padé approximant or the continued fraction form of the nonrational network function can be achieved with reduced computational complexity. Thus, the classical Foster and Cauer form or other techniques may be applied to synthesize network functions. The application of this work is demonstrated by considering certain functions such as the square root, inverse tangent, logarithm, and Lambert's W function. A set of conditions called synthesis criteria is proposed, which should be satisfied by a nonrational function to be realizable.     

An efficient hardware implementation of the elliptic curve cryptographic processor over prime field,

Elliptic curve cryptography (ECC) schemes are widely adopted for the digital signature applications due to their key sizes, hardware resources, and higher security per bit than Rivest-Shamir-Adleman (RSA). In this work, we proposed a new hardware architecture for elliptic curve scalar multiplication (ECSM) in Jacobian coordinates over prime field, . This is a combination of point doubling and point addition architecture, implemented using resource sharing concept to achieve high speed and low hardware resources, which is synthesized both in field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC). The proposed ECSM takes 1.76 and 2.44 ms on Virtex-7 FPGA platform over 224-bit and 256-bit prime field, respectively. Similarly, ASIC (GF 40 nm complementary metal-oxide semiconductor [CMOS]) technology implementation provides energy efficient with a latency of 0.46 and 0.6 ms over prime field and , respectively. This design provides better area-delay product and high throughput value in both FPGA and ASIC when compared with other designs.     

LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

Robust nonlinear adaptation algorithms for multitask prediction networks

High fidelity behavior prediction of intelligent agents is critical in many applications, which is challenging due to the stochasticity, heterogeneity, and time-varying nature of agent behaviors. Prediction models that work for one individual may not be applicable to another. Besides, the prediction model trained on the training set may not generalize to the testing set. These challenges motivate the adoption of online adaptation algorithms to update prediction models in real-time to improve the prediction performance. This article considers online adaptable multitask prediction for both intention and trajectory. The goal of online adaptation is to improve the performance of both intention and trajectory predictions with only the feedback of the observed trajectory. We first introduce a generic -step adaptation algorithm of the multitask prediction model that updates the model parameters with the trajectory prediction error in recent steps. Inspired by extended Kalman filter (EKF), a base adaptation algorithm modified EKF with forgetting factor (MEKF) is introduced. In order to improve the performance of MEKF, generalized exponential moving average filtering techniques are adopted. Then this article introduces a dynamic multiepoch update strategy to effectively utilize samples received in real time. With all these extensions, we propose a robust online adaptation algorithm: MEKF with moving average and dynamic multiepoch strategy (MEKF_{MA − ME} ). We empirically study the best set of parameters to adapt in the multitask prediction model and demonstrate the effectiveness of the proposed adaptation algorithms to reduce the prediction error.     

Input-to-state learning of recurrent neural networks with delay and disturbance

This article deals with the issue of input-to-state stabilization for recurrent neural networks with delay and external disturbance. The goal is to design a suitable weight-learning law to make the considered network input-to-state stable with a predefined -gain. Based on the solution of linear matrix inequalities, two schemes for the desired learning law are presented via using decay-rate-dependent and decay-rate-independent Lyapunov functionals, respectively. It is shown that, in the absence of external disturbance, the proposed learning law also guarantees the exponential stability of the network. To illustrate the applicability of the present weight-learning law, two numerical examples with simulations are given.     

Performance improvement of DC-DC converter using L-D-based modified GaN-FET driver

Bootstrap capacitor in FET gate driver plays an important role in the transient performance of the half bridge configured synchronous buck DC-DC converter especially in the top switch. In this paper, a new bootstrap capacitor based GaN-FET driver is proposed. This new GaN-FET driver is tested in a synchronous buck converter for performance verification like immunity, transient response, and voltage ringing. A comparison study with the existing LM5113 (Texas Instrument)–based driver for GaN-FET and IR2110-based Si-MOSFET driver on a DC-DC converter is carried out to show the performance improvement using the proposed GaN-FET driver. The simulation study is performed on spice-based NI-Multisim 14.1. Finally, the designed GaN-FET driver is tested on a 60-W synchronous buck DC-DC converter in open-loop and closed-loop configuration.     

Robust real-time parameter estimation for linear systems affected by external noises and uncertainties

A robust adaptive parameter estimation method, based on the application of a full-order filter capable of rejecting exogenous disturbances, is proposed in this article. A linear matrix inequality condition is proposed to synthesize the desired robust filter, assuming the presence of a known input control with constraints. The filter uses the output of the system to estimate the desired signal that will be employed in the adaptive estimation procedure and, to assure robustness to exogenous noise and unstructured uncertainties, the guaranteed cost is minimized in the synthesis condition. The filtered signals are then applied to an adaptive procedure to estimate the unknown system's internal parameters, which is also proposed in this article. It is shown that lower values for the guaranteed cost from the disturbance input to the error output of the filter imply more accurate estimations of the parameters. The efficiency of the proposed estimation technique is illustrated through a simulated model and a physical system has been considered to validate real-time estimation.     

An enhanced state estimation for linear parabolic PDE systems with mobile sensors

This paper studies an enhanced state estimation problem of distributed parameter processes modeled by a linear parabolic partial differential equation using mobile sensors. The proposed estimation scheme contains a state estimator and the guidance of mobile sensors, where the spatial domain is decomposed into multiple subdomains according to the number of sensors and each sensor is capable of moving within the respective subdomain. The state estimator is desired to make the state estimation error system exponentially stable while providing an performance bound. The mobile sensor guidance is used to enhance the transient performance of the error system. By the Lyapunov direct technique, an integrated design of state estimator and mobile sensor guidance laws is developed in the form of bilinear matrix inequalities (BMIs) to meet the desired design objectives. Moreover, to make the performance bound as small as possible, a suboptimal enhanced state estimation problem is formulated as a BMI optimization one, which can be solved via an iterative linear matrix inequality algorithm. Finally, numerical simulations are given to show the effectiveness of the proposed method.     

Process-induced variability modeling of subthreshold leakage power considering device stacking

The dominance of leakage currents in circuit design has been impelled by steady downscaling of MOSFET into nanometer regime, and has become a significant component of total IC power dissipation. The issue is further aggravated with the inability to gauge the tolerance of process parameters around their nominal value. Consequently, the drive to improve the static power prediction has enticed accurate and reliable modeling of leakage current, specifically for ultralow power applications. In contrast to gate- and band-to-band-tunneling leakages, subthreshold leakage exhibits high susceptibility to process variations and hence has been considered for variability modeling. Fluctuations in the device electrical and geometry parameters result in a wider distribution of subthreshold leakage current. Hence, taking into account stacking effect, an analytical variability model to estimate subthreshold leakage power in subthreshold circuits, in the presence of threshold voltage variations is proposed. Further, the impact of threshold voltage variability on subthreshold leakage power is modeled in conjunction with simultaneous variations in gate length and width. The leakage power variability is characterized by model-generated distributions obtained using Monte Carlo analysis and validated against SPICE simulations. The proposed model is about 700 computationally faster than SPICE simulations with mean error being less than 0.19%.     

Ultraefficient imprecise multipliers based on innovative 4:2 approximate compressors

This study proposes two ultraefficient imprecise multipliers based on innovative 4:2 approximate compressor designs. The first proposed multiplier employs an ultracompact 8‐transistor 4:2 compressor to reduce the transistor count and energy dissipation. To improve the accuracy of the first proposed imprecise multiplier, the second multiplier also benefits from a semiaccurate 24‐transistor 4:2 approximate compressor for the high‐order bits. The 7‐nm fin field‐effect transistor (FinFET) technology, as one of the leading commercial technologies, is utilized to simulate the proposed multipliers in the environment. The simulation results indicate that the proposed imprecise multipliers show significant improvements regarding transistor count, delay, power, and power‐delay product as compared to their state‐of‐the‐art imprecise and exact counterparts. Along with the superior hardware efficiency, the MATLAB simulations demonstrate that the proposed multipliers also provide reasonable levels of accuracy. Moreover, a figure of merit (FOM) considering hardware efficiency and output quality is considered in order to evaluate the multipliers comprehensively. The FOM simulation results indicate that the proposed imprecise multipliers make a significant trade‐off between hardware efficiency and quality for approximate‐computing applications dealing with image multiplication.     

Single switch quadratic boost converter with continuous input current for high voltage applications

In this paper, a novel non-isolated very high step-up DC-DC converter is presented. The introduced converter benefits from various advantages, namely, very high voltage gain, low voltage stress on the active switch, and continuous input current with low ripple. Therefore, the presented converter is suitable for renewable energy applications. In addition, the energy of the leakage inductance of the coupled inductor is successfully recovered, and the voltage spike of the active switch is clamped during the turn-off process. Hence, a switch with low can be used, which decreases the conduction losses as well as cost of the converter. Furthermore, the voltage stress of the output diode is decreased, which reduces the reverse recovery problem. The steady-state analysis and design considerations of the proposed converter are discussed. Finally, the theoretical analysis is validated with the experimental results at an output power of 150 W.     

Schwarz lemma for driving point impedance functions and its circuit applications

In this paper, a boundary version of the Schwarz lemma is investigated for driving point impedance functions and its circuit applications. It is known that driving point impedance function, Z(s) = 1 + c_p(s − 1)^p + c_{p + 1}(s − 1)^{p + 1} + ..., p > 1, is an analytic function defined on the right half of the s-plane. Two theorems are presented using the modulus of the derivative of driving point impedance function, |Z′(0)|, by assuming the Z(s) function is also analytic at the boundary point s = 0 on the imaginary axis with . In the obtained inequalities, the value of the function at s = 1 and the derivatives with different orders have been used. Finally, the sharpness of the inequalities obtained in the presented theorems is proved. Simple LC circuits are obtained using the obtained driving point impedance functions.     

Multilayer thermal object identification in frequency domain using IR thermography and vector fitting

This paper deals with the identification of the thermal parameters of multilayer objects using the concept of thermal impedance. In order to perform such identification, temperature evolution in time is obtained by an infrared camera after power excitation is applied in the investigated structure. Infrared thermography offers the advantage of being a noncontact temperature detection and measurement method. In many practical cases, it is impossible to use contact temperature measurements. Typically, the power in the form of a step function is applied. In order to calculate the thermal impedance of an object, temperature and power are converted into the frequency domain using the Laplace transform for s = jω. Then, the poles of the thermal impedance are identified using vector fitting, which allows calculating the thermal impedance as a sum of partial fractions. This corresponds directly to the Foster network of a thermal object. In addition, the vector fitting method offers much better convergence in comparison with other methods using the polynomial rational approximation of thermal impedance. A considerable improvement of the numerical Laplace transform in high frequency range is proposed. In this approach, the variable s = jω is replaced by , and then, the integration result is corrected by the Taylor series. It leads to a kind of filtering of the temperature signal.     

Transfer learning for high‐precision trajectory tracking through adaptive feedback and iterative learning

Robust and adaptive control strategies are needed when robots or automated systems are introduced to unknown and dynamic environments where they are required to cope with disturbances, unmodeled dynamics, and parametric uncertainties. In this paper, we demonstrate the capabilities of a combined adaptive control and iterative learning control (ILC) framework to achieve high‐precision trajectory tracking in the presence of unknown and changing disturbances. The adaptive controller makes the system behave close to a reference model; however, it does not guarantee that perfect trajectory tracking is achieved, while ILC improves trajectory tracking performance based on previous iterations. The combined framework in this paper uses adaptive control as an underlying controller that achieves a robust and repeatable behavior, while the ILC acts as a high‐level adaptation scheme that mainly compensates for systematic tracking errors. We illustrate that this framework enables transfer learning between dynamically different systems, where learned experience of one system can be shown to be beneficial for another different system. Experimental results with two different quadrotors show the superior performance of the combined ‐ILC framework compared with approaches using ILC with an underlying proportional‐derivative controller or proportional‐integral‐derivative controller. Results highlight that our ‐ILC framework can achieve high‐precision trajectory tracking when unknown and changing disturbances are present and can achieve transfer of learned experience between dynamically different systems. Moreover, our approach is able to achieve precise trajectory tracking in the first attempt when the initial input is generated based on the reference model of the adaptive controller.     

Event-triggered adaptive backstepping tracking control for a class of nonlinear fractional order systems

This article investigates the problem of event-trigger based adaptive backstepping control for a class of nonlinear fractional order systems. By introducing an appropriate transformation of frequency distributed model, the fractional-order indirect Lyapunov method with is obtained. In addition, the event-triggered adaptive controller is developed by employing the event-triggered control approach. Meanwhile, by the proposed adaptive control scheme, all the closed-loop signals are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Finally, simulation results are provided to testify the availability of the presented controller.     

Efficient hardware implementations of point multiplication for binary Edwards curves

This paper presents efficient and fast hardware implementations of the complete point multiplication on binary Edwards curves (BECs). The implementations are based on extremely fast complete differential addition and doubling formulas. These new complete differential addition formulas are performed for general and special cases of BECs with cost of 5 M + 4 S + 2 D and 5 M + 4 S + 1 D , respectively, where, M , S , and D denote the costs of a field multiplication, a field squaring and a field multiplication by a constant, respectively. In the general case of the BECs, proposed structures are implemented based on 3 and 1 pipelined digit‐serial Gaussian normal basis multipliers. In the design by 3 multipliers, computation of point addition and point doubling is performed concurrently. But in the second implementation for low‐cost design with low number of hardware resources, these computations are implemented by 1 multiplier. Also, in the special case of BECs, 2 structures are proposed for achieving the highest degree of parallelization and utilization of resources by using 3 and 2 field multipliers. Implementation results of the proposed architectures based on Virtex‐5 XC5VLX110, Virtex‐4 XC4VLX100, and Arria‐10 10AX115U4F45I3SG FPGAs for 2 fields and are achieved. The results show improvements in terms of execution time, area, and efficiency for the proposed structures compared with previous works.     

Robust adaptive passivity-based PIλD control

In this article, we develop proportional, fractional-integral, and derivative () controllers for the regulation and tracking problems of nonlinear systems. The analytic results are obtained by extending the passivity-based approach to include fractional operators. Robustness under parametric uncertainty is dealt with by a combination with an adaptive scheme. It is also shown their robustness under additive noise and their robustness under uncertainty in the derivation order. The advantages in the controlled system performance and in the control energy consumption in comparison to classic PI and proportional integral derivative controllers are illustrated for the quadratic boost converter and a benchmark system in the literature.