Electronics Letters最新文献

Augmented reality (AR) technology can provide immersive and natural interactive interfaces for brain-computer interface (BCI) systems. The control architecture of existing AR-BCIs is at joint-level (JL) or action-level (AL), which brings a huge user burden and poor interactive experiences. A task-level (TL) BCI control method was proposed in this study to enhance interactive experiences. The TL AR-BCI system based on steady-state visual evoked potentials was implemented controlling a robotic arm to grab and drop blocks. The online experiment of ten subjects shows TL AR-BCI can effectively reduce the number of control steps and stimulation time while maintaining the same performance as JL and AL AR-BCIs. The performance of three AR-BCIs (JL, AL, TL) was calculated (mean accuracy: 90.66%, 92.52% and 92.2%. Mean information transfer rates: 77.56, 80.06, and 82.71 bits/min. Mean numbers of control steps: 35.48, 17.32, and 13.05. Mean stimulation time: 0.97, 0.97 and 0.89 s). The results show that TL AR-BCI can effectively reduce the number of control steps and stimulation time while maintaining the same performance as JL and AL AR-BCIs.

This article presents an offset cancellation technique for single-ended hybrid continuous-time/discrete-time (CT/DT) ΣΔ current-to-digital converters (CDCs). By introducing an auxiliary amplifier in the main amplifier's output branch, the proposed method suppresses structural offset caused by integration node clamping. Simulation results show a ∼20 dB reduction in DC offset and improved robustness against process variations. With the proposed amplifier integrated, system-level simulation achieves a Figure of Merit (FoM) of 177.6 dB, highlighting a strong trade-off between power, speed, and resolution.

Deployment of low-cost Wi-Fi sensing applications may impose strict constraints on available computational power, thereby making the reduction of time required to process channel state information (CSI) measurements a matter of interest. Most works on passive sensing for classification tasks achieve this via dimensionality reduction of CSI data prior to the training of machine learning models, which in itself still imposes some computational burden. Subcarrier selection, which is a faster approach and is widely used in another application domain (i.e., vital signal monitoring), is seldom considered; in the few works where it is used, only a variance-based unsupervised strategy is applied. In this letter, a supervised linear-complexity subcarrier selection strategy is proposed for enhanced sensing classification accuracy. The approach is validated through practical experiments whose results show that the classification performance can approach or even surpass that obtained via dimensionality reduction, with substantial time savings.

In order to meet the cold testing requirements of high-frequency systems for gyrotron travelling wave tube (Gyro-TWT), a novel broadband circular waveguide TE₃₁ mode converter is designed and verified in this letter. The designed mode converter adopts a power-divider waveguide network with twisted waveguides to realise efficient broadband conversion from rectangular waveguide TE₁₀ mode to circular waveguide TE₃₁ mode. Simulation results demonstrate that the proposed circular waveguide TE₃₁ mode converter exhibits an input port return loss better than 21 dB and an insertion loss less than 0.11 dB. It realises high conversion efficiency (>97.5%) in a relative bandwidth of 41%. A prototype circular waveguide TE₃₁ mode converter is fabricated and characterised through back-to-back cold-test and infrared field pattern imaging. The experimental results closely align with the theoretical predictions, with a deviation of less than 0.1 dB between the two values.

This paper presents a wideband and miniaturized LNA from 25 to 90 GHz in GaAs process. A periodical cascading amplifier structure with resistive feedback is proposed for size reduction. To address the challenges of wideband gain flattening, a periodical eigenvalue is derived, which allows for the quantization and subsequent flattening of the overall amplifier response. The prototype achieves a maximum gain of 25 dB and a noise figure of 3.5 dB in the operating frequency band, with core area of only 0.53 mm².

With the increasing development of underground spaces, position technologies for underground environments have attracted growing attention. Traditional methods based on high-frequency electromagnetic waves are limited due to poor penetration. In contrast, the low-frequency (LF) electromagnetic waves offer better penetration and robustness, but traditional LF antennas are huge. The rotating permanent magnet based mechanical antenna (RPMMA) can transmit LF signals while maintaining a compact and portable design. This paper investigates an underground positioning approach based on RPMMA. The spatial radiation characteristics of the antenna are analysed, and different position estimation models are proposed. Simulations are conducted to identify the optimal method, which is validated through experiments in a complex underground environment.

The surging demand for large model inference—driven by billions of daily artificial intelligence (AI) interactions—has intensified the need for energy-efficient AI devices. This work presents a configurable Booth-encoded digital computing-in-memory (CIM) macro in 28 nm CMOS, supporting both accurate and approximate INT8 computations to explore a trade-off among accuracy, flexibility, and power, performance, and area (PPA). Its computing array leverages row-shared Booth encoders integrated with dual-bit SRAM storage to generate signed partial products simultaneously across multiple channels to achieve double throughput compared to bit-serial implementations, incurring only 37.5% area overhead. Critically, implementing sign compensation bit pre-processing on these partial products simplifies the subsequent addition, thereby reducing the adder area. The configurable approximate compressor tree reduces power by 23.49% at 500 MHz with an error compensation scheme reducing offset errors in approximate mode. Combined with truncation-aware compensation, the macro achieves 512 GOPS and 35.41 TOPS/W at < 0.28% hardware error without retraining.

Time series anomaly detection (TSAD) is utilized for identifying abnormal patterns from normal patterns in various fields. Defining semantic anomaly patterns in a specific sequence is a problem for existing methods, resulting in loss of diversity and incorrect detection problems. In order to address these problems, this study recommends using a dual-encoder generative adversarial network. The proposed model employs a generator and two encoders to comprehend the intricate dependencies and subtle alterations of data, guaranteeing accurate generation. An adaptive threshold is used with anomaly scores that combine reconstruction and discrimination errors to clearly identify anomalies when detecting anomalies. Experimental results in NASA, NAB and UCR benchmark datasets show that the proposed model outperforms ARIMA, LSTM-AE and TadGAN in terms of F1 score by 0.135, 0.222 and 0.05, respectively.

Chiplet-based systems leveraging a domain-specific reusable interposer (DSRI) technology achieve superior performance, power, and cost (PPC) advantages. This paper proposes AutoDSRI, an automated framework for chiplet generation and topology synthesis (TS) in DSRI design. AutoDSRI introduces a hierarchical graph partitioning method, first partitioning domain-generic esults show an average of 29.18% improvement in PPC over traditional methods.

This study proposes a structure that replaces the stepped metallic ridge of an inline waveguide-to-coaxial adapter operating in the 40–60-GHz band with a ridge structure based on a printed circuit board (PCB). The PCB-based ridge is designed using stepped via holes and patterned copper foil to provide an impedance-matching function without the need for metal processing. A prototype device was manufactured and measurements were made using a vector network analyser (VNA). Based on simulations and measurements, reflection coefficients of less than –15 dB were achieved across the entire band. Therefore, the proposed adaptor performs well in the U-band at low cost.