Aeu-International Journal of Electronics and Communications最新文献

Inductor-less CMOS filters with bandwidth exceeding several GHz are required in high-speed data converter applications. This paper introduces two complementary biquad filters, one N-based and the other P-based, utilizing the well-established flipped voltage follower (FVF) stage. These filters exhibit more than 7 GHz cut-off frequency and a low power consumption of 0.54 mW/pole for the N-type biquad, and 0.3 mW/pole for the P-type one, demonstrating impressive figures-of-merit (FOMs) even considering bandwidth and dynamic range. The implementation of these biquads in the STMicroelectronics FD-SOI 28-nm CMOS process, along with extensive simulations, ensures stable performance under process, supply voltage and temperature (PVT) variations and mismatches, as confirmed by post-layout simulations. Notably, the area occupied by each biquad is merely 246 μm² for N-type biquad and 193 μm² for P-type, marking one of the smallest footprints in the existing literature. The achieved figures-of-merit are noteworthy, showcasing excellent power efficiency, minimal area occupation, and commendable dynamic range.

This study introduces a methodology tailored to analog hardware architecture for implementing an artificial neural network. The fundamental components of the architecture include current-mode circuits, representing the class, and a voltage-mode comparator. Specifically, the current mode circuits comprise the Mahalanobis distance circuit, Sigmoid function circuit, analog multiplier, and current mirrors. Regarding the voltage comparator, which receives the final decision, a folded-cascode operational amplifier is employed. The operational principles of the architecture are extensively explained and applied in a power-efficient configuration (operating under 976nW) with low power supply rails (0.6 V). The proposed implementation is tested on real-world biomedical classification tasks, achieving classification accuracy exceeding 91.6%. The designs are implemented using a $90\mathrm{nm}$ CMOS process and developed using the Cadence IC Suite for both schematic and layout design. Monte-Carlo analysis, encompassing both process and mismatch, as well as corner analysis, are provided to confirm the robust characteristics of the proposed classifier. Through comparative analysis of post-layout simulation results with an equivalent software-based classifier and related literature, the proper operation of the proposed architecture is confirmed.

This study introduces a compact microstrip lowpass filter that offers a wide stopband and a low transition band. The couple radial stubs are designed for having sharp roll-off. The filter has a −3 dB cut off frequency of 1.6 GHz, with a transition band ranging from 1.6 to 1.7 GHz with attenuation levels of −3 dB and −20 dB, respectively. To ensure a wide stopband, the filter utilizes bended transmission lines with stepped impedance suppressing cells, resulting in a stopband attenuation of 20 dB from 1.7 to 28 GHz. The insertion loss in the passband is less than 0.1 dB, and the overall size of the filter is 0.127 × 0.087 λg².

This paper presents a simple yet robust technique for identification of the presence of contaminants in food samples by detecting the variation in the transmission coefficient ($S_{21}$) in a near field measurement setup. A compact step-notched antipodal Vivaldi antenna with a frequency range of operation 3 $-$ 18 GHz, and realized gain of 12.81 dB at 16 GHz having compact dimensions $60mm\times 40mm\times 0.51mm$, i.e., $0.6\times 0.4\times 0.005{\lambda }_0^3$ (where ${\lambda }_0$ is the free space wavelength at the lowest frequency of operation) is designed for contamination detection due to its compact size, high directivity and high boresight gain for near-field measurement. An equivalent image is constructed from the S $-$ parameters, measured at different positions and different antenna polarizations over the frequency range of 3-18 GHz for metallic contaminants, i.e., iron ball and aluminum foil. An algorithm is developed for detection of contamination from the images using the reference case of no-contamination scenario. The proposed algorithm is validated using full-wave simulation.

In order to enhance spectrum utilization and decrease transmission delay, the 6th generation mobile networks (6G) system adopts full-duplex (FD) mode. Simultaneous operation of the same frequency inevitably causes the receiver to receive the signal sent by the transmitter, leading to serious nonlinear self-interference due to the nonlinear distortion generated by the power amplifier (PA). This paper proposes hybrid layer structures SW-GRU-TPA for the implementation of digital self-interference cancellation (SIC), combined with Sliding Window (SW), Gated Recurrent Unit (GRU), and Temporal Pattern Attention (TPA). The model can not only effectively utilize the influence of historical information on the signal, but also capture the spatial and temporal dependencies in the data to fit the nonlinear characteristics of the PA and the delay effect in multipath transmission. Meanwhile, by utilizing the $L_1$ norm model pruning, the model parameters can be optimized, leading to improved processing efficiency. The model is verified using two methods: injection and antenna reception. Compared to the SW-GRU model with Attention Mechanism (AM), the Interference Cancellation Ratio (ICR) of injection and antenna increases by 3.13 dB and 2.01 dB, respectively. When the TPA is compressed by 42.17%, the ICR of injection and antenna decreases by 1.58% and 2.6%.

In this paper, we present a lightweight, broadband metamaterial absorber that exhibits polarization stability and is insensitive to incident angles. The absorber is composed of one layer of indium tin oxide impedance surface, three layers of quartz fiber composite material, and one layer of paper honeycomb on a metal sheet. Our proposed absorber has a 90 % absorptivity bandwidth of 13.19 GHz, ranging from 4.83 GHz to 18.12 GHz at TEM plane wave normal incidence, covering the entire X and Ku band. We illustrate the variation of design parameters and the distribution of electromagnetic field and current to investigate the absorption mechanism. To demonstrate the effectiveness of our design, we fabricate a prototype sample and measure its absorption performance. The results show a bandwidth of 12.38 GHz, ranging from 4.91 GHz to 17.29 GHz, with a low profile of 0.095λL at the lowest operating frequency. The experimental results are in good agreement with the numerical simulation, effectively verifying our proposed design.

This article presents a novel dual-band metamaterial bandpass quasi-elliptic filter (DBBPF) for wireless communication applications, addressing the critical need for compact, high-performance multi-band filters in modern systems. The proposed filter leverages substrate-integrated waveguide cavity (SIWC) technology combined with innovative complementary metamaterial resonators to achieve significant miniaturization while maintaining excellent electrical performance. The filter’s design incorporates four complementary resonators of modified rectangular shape (CMSRR) on the metalized top face, generating targeted operating bands below the SIW cutoff frequency. To control their resonances, the size of each CMSRR is optimized for electrical dimensions of just 0.154 ${\lambda }_0\times$ 0.115 ${\lambda }_0\times$ 0.028 ${\lambda }_0$. We present a comprehensive analysis of both full mode (FMSIWC) and half mode (HMSIWC) configurations, demonstrating dual-bandpass behavior with central frequencies at 5.79 and 9.82 GHz for FMSIWC, and 5.72 and 9.74 GHz for HMSIWC. The electromagnetic behavior of the basic cell of the filter is analyzed based on the frequency characteristics of its permittivity and permeability. A parametric study according to the location of the metamaterial resonators in the filter is conducted to have the optimized dimensions. Additionally, the confinement of the electric field in the two filter configurations is discussed to better understand their behavior. The fabricated HMSIWC filter, implemented on an FR4-Epoxy substrate measuring only 54.60 × 25.75 × 1.5875 mm³, achieves remarkable miniaturization while maintaining desired performance characteristics. Experimental results validate the simulated performance, demonstrating excellent agreement for both configurations. Key performance metrics include fractional bandwidths of 3.77 and 6.98 %, and insertion losses of 1.33 dB and 2.14 dB for the two passbands in the HMSIWC design. This work advances the state-of-the-art SIW filter design by combining half-mode techniques with optimized CMSRR structures, resulting in a compact, high-performance dual-band filter. The proposed DBBPF’s simple design and small form factor make it an ideal candidate for integration into various wireless communication systems, particularly where space constraints are critical.

To address the specific requirements of antennas in Unmanned Aerial Vehicle (UAV) joint radar-communication (RadCom) systems, a novel low-profile reconfigurable metantenna with the capability of frequency and pattern reconfiguration is proposed in this paper. The proposed antenna consists of a metasurface layer with 3 × 3 square unit cells and a simple feeding network with two pairs of PIN diodes. Specifically, the designed metantenna has a low profile of 0.064 λ₀ (λ₀ is the free space wavelength at 5.5 GHz). And measured results show that, for the broadside radiation mode, the −10 dB operating bandwidth is about 12.1 %, ranging from 5.05 to 5.7 GHz with a maximum gain of 8.9 dBi. For the conical radiation mode, the −10 dB operating bandwidth is about 21.9 %, ranging from 5.88 to 7.33 GHz with a maximum gain of 6.83 dBi. Given its excellent radiation performance, the metantenna designed for UAV joint RadCom systems holds significant research significance and value in the field of multifunctional reconfigurable antennas.

In this work, a sampled-data feedforward comb filter suitable for time-mode signal processing (TMSP) is proposed. In the proposed system the input voltage is sampled and converted to a pulse-width (PWM); the pulse width is proportional to the input amplitude. The pulse width is the quantity which is being processed by the time-mode feedforward comb filter. Both input and output are synchronous with the sampling clock. The benefits of the proposed filter topology are (a) the modular and (b) hierarchical design which can be dealt with as a pure digital comb filter implementation counterpart. The frequency response of the proposed filter is digitally programmable. As a result, this filter can be used as building block for the implementation of higher-order time-mode systems. The comb filter was designed and verified through post-layout simulations in TSMC 65 nm technology process. The sampling rate was 5 MHz, the voltage supply was 1.2 V, consuming 540 μW for the use of 9-unit delay tap network and occupying a total area of 254 μm × 160 μm.

Development of a rapid sensors for detecting volatile organic compounds (VOCs) is a need of the hour to effectively mitigate the adverse effect of VOCs on environmental pollution. In this line, the current paper presents the design and development of a non-invasive split-ring resonator (SRR)-based microwave sensor for detecting liquid VOCs, specifically isopropyl alcohol (IPA), acetone, ethanol, and methanol. Artificial intelligence (AI) based algorithms are gaining popularity in developing a highly-selective sensor circuit. In the proposed sensor, the SRR circuit is optimized for better detection sensitivity and the multi resonant behavior of the circuit offers adequate selectivity. The designed sensor offers better re-usability and thereby supporting AI-based algorithms for continuous monitoring of VOCs in real-time. Transmission coefficient ($S_{21}$) of the sensor is measured over the frequency range of 0.8–6 GHz for different VOCs with varying concentrations. Analysis of variance (ANOVA) and post hoc Tukey tests are employed to discern significant variations in the measured data. Principle component analysis (PCA) and discriminant analysis are performed over the measured data to classify the VOCs. These analytical results show that the proposed sensor can be used for generating huge data set to support AI based algorithms in detecting VOCs in real-time.