In this work, we report the first demonstration of an ultraviolet light-emitting diode (LED) with boron-containing multiple quantum wells. Electroluminescence emission from the BAlGaN LED was observed at 350 nm, with higher intensity compared to the AlGaN reference LED. A higher operating voltage compared to the reference LED was also observed which may be attributable to a nanomasking behaviour of boron in (Al)GaN alloys.

Consider general minimum variance distortionless response (MVDR) robust adaptive beamforming problems based on the optimal estimation for both the desired signal steering vector and the interference-plus-noise covariance (INC) matrix. The optimal robust adaptive beamformer design problem is an array output power maximization problem, subject to three constraints on the steering vector, namely, a (convex or nonconvex) quadratic constraint ensuring that the direction-of-arrival (DOA) of the desired signal is separated from the DOA region of all linear combinations of the interference steering vectors, a double-sided norm constraint, and a similarity constraint; as well as a ball constraint on the INC matrix, which is centered at a given data sample covariance matrix. To tackle the nonconvex problem, a new tightened semidefinite relaxation (SDR) approach is proposed to output a globally optimal solution; otherwise, a sequential convex approximation (SCA) method is established to return a locally optimal solution. The simulation results show that the MVDR robust adaptive beamformers based on the optimal estimation for the steering vector and the INC matrix have better performance (in terms of, e.g., the array output signal-to-interference-plus-noise ratio) than the existing MVDR robust adaptive beamformers by the steering vector estimation only.

This letter presents a sub-6 GHz wideband low noise amplifier (LNA) based on double L-type load network and negative feedback technique. Using the cascode structure combined with the above techniques, a single-stage wideband LNA with high gain and low noise figure (NF) can be realized. Fabricated in 110-nm SOI CMOS technology, the proposed LNA achieves a maximum power gain of 15.2 dB, noise figure (NF) of 1.0–1.56 dB. The 3-dB bandwidth ranges from 3.05–4.55 GHz. The minimum power input at 1dB compression point (IP1dB) is -17.1 dBm. The LNA core area is 0.18 mm2 and dissipates a total power of 11.5 mW from 1.4 V power supply.

Strong clutter seriously affects target-of-interest detection in synthetic aperture radar (SAR) images. This letter proposes an unsupervised target detection method (U-TDM) based on a complex-valued extreme learning machine (CV-ELM), the essence of which is to transform the problem of target detection into a pixel binary classification problem. The SAR image is first divided into several unlabeled patches, and fuzzy c-means (FCM) is used to construct the reference target patch set and the clutter patch set. Based on these two patch sets, CV-ELM is used to classify the neighboring patch of the pixel to be detected. Since the pixel intensity and distribution of target-of-interest and clutter are different, unsupervised pixel classification could be realized without ground-truth through U-TDM. Experimental results on GF-3 data and Sentinel-1 data show the efficiency of the proposed method in target detection with a heterogeneous clutter environment.

In this paper, a wideband cylindrical conformal microstrip antenna array employing a proximity-coupled feeding mechanism with a cavity-backed configuration is designed and fabricated. Compared with other conformal microstrip patch antennas by using linear subarrays assembled piecewise, this design uses Teflon instead of traditional dielectric layers, and makes it possible to process the whole conformal array without splicing, obtaining the freedom in unit size and array radius adjustment except ease of manufacturing and assemblage. Combined with the optimization of the cavity size, an array with 44 elements is obtained which has a bandwidth of 40% from 8 to 12GHz and a gain of 16.4 dB.

Computing in-memory technology is a promising way of solving the “memory wall” problem when processing large-scale data,accelerating data processing, and improving energy efficiency. However, calculations in the analog domain are limited in terms of accuracy and sensitivity to process, voltage, and temperature (PVT) changes. In this study, we proposed a computing in memory multiply-and-accumulate (CIM MAC) circuit which could generate pulses automatically and which endows the time-domain approach with a digital character. The MAC result was reflected by the pulse edge and converted into the final digital output using a dual-edge counter quantization circuit, improving the accuracy of the MAC operation and reducing the difficulty of quantization. The performance of the proposed CIM circuit was evaluated using a 28-nm process. It could achieve 4-bit multiplication without errors with an energy efficiency of 40.34 to 883.37 TOPS/W.

In this letter, the colored point cloud quality assessment in Augmented Reality (AR) environment was fully studied through subjective test. Firstly, we present a point cloud dataset, named Point Cloud Quality Dataset-AR (PCQD-AR), including ten reference point clouds and their 90 distorted versions, which were encoded by the reference software of Video-based Point Cloud Compression (V-PCC) under different pairs of geometry and texture quantization parameters. Then, the impact of geometry and texture distortions on perceived quality of point clouds in the AR environment was discussed in detail. Moreover, we evaluate the performance of existing objective point cloud quality assessment metrics on the proposed dataset. The subjective dataset including the values of Mean Opinion Score (MOS) will be released after acceptance.

With the increasing innovation of network communication technology, short burst communication has been developed to a certain extent in the time compression technology, which brings great challenges to the radio signal blind reception technology .In the blind reception of non-cooperative signals in short-time burst communication, signal time domain detection is the premise and key of signal modulation parameter estimation, signal demodulation, decoding, interpretation, information acquisition and interference guidance. In this paper, the blind demodulation method of short-time burst FM-MFSK signal is studied under the condition of low signal-to-noise ratio. A short-time burst FM-MFSK blind demodulation method based on STFT is proposed. When SNR is higher than 2dB, the correct demodulation probability for FM-MFSK signal can reach 90%.

Automatic Modulation Recognition (AMR) is a fundamental research topic in the field of signal processing and wireless communication, which has widespread applications in cognitive radio, non-collaborative communication, etc. However, current AMR methods are mostly based on unimodal inputs, which suffer from incomplete information and local optimization. In this paper, we focus on the modality utilization in AMR. The proxy experiments show that different modalities achieve a similar recognition effect in most scenarios, while the personalities of different inputs are complementary to each other for particular modulations. Therefore, we mine the universal and complementary characteristics of the modality data in the domain-agnostic and domain-specific aspects, yielding the Universal and Complementary subspaces accordingly (dubbed as UCNet). To facilitate the subspace construction, we propose universal and complementary losses accordingly, where the former minimizes the heterogeneous feature gap by an adversarial constraint and the latter consists of an orthogonal constraint between universal and complementary features. The extensive experiments on the RadioML2016.10A dataset demonstrate the effectiveness of UCNet, which has achieved the highest recognition accuracy of 93.2% at 10 dB, and the average accuracy is 92.6% at high SNR greater than zero.

This study investigates the development of InAs quantum dot (QD) lasers on a InP(001) substrate, utilizing only III-arsenide layers. This approach avoids the issues associated with the use of phosphorus compounds, which are evident in the crystal growth of conventional C/L-band QD lasers, making the manufacturing process safer, simpler, and more cost-effective. The threshold current density of the fabricated QD laser was 633 A/cm2, which is the lowest value for QD lasers in the 1.6 μm-wavelength region. This result suggests a high cost-effectiveness and paved the way toward a large-scale production technology for high-performing C/L/U-band QD lasers.

The knife-edge diffraction model (KED) and the uniform geometrical theory of diffraction (UTD) have been widely used to predict the shadowing effect at millimetre-wave (mmWave) bands. This letter proposes a mathematical derivation to rigorously prove that, for an absorbing screen, UTD applying the narrow-angle Fresnel approximation is equivalent to KED. The simulation scenarios are designed to validate the proposal by comparing KED with UTD in the narrow-angle (less than 20○) and wide-angle (over 20○) regions at mmWave bands (20 GHz - 100 GHz). Simulated results agree with the proposal that KED is identical to UTD with a low error of less than 0.1 dB in the narrow-angle region, while they have a difference with an error of over 1 dB in the wide-angle region. In addition, the average computational time is measured and results in both UTD and KED taking approximately 8.0 ms for one test. From the proposal, it can be theoretically explained the differences and similarities between KED and UTD for an absorbing screen.

This paper proposes a background calibration scheme for the pipelined-SAR ADC based on the neural network. Due to the nonlinear function fitting capability of the neural network, the linearity of the ADC is improved effectively. However, the hardware complexity of the neural network limits its application and promotion in ADC calibration. Hence, this paper also presents the optimization schemes, including the neuron-based sharing neural network and the partially binarized with fixed neural network, in terms of calibration architecture and algorithm. A 60 MS/s 14-bit pipelined-SAR ADC prototyped in 28-nm technology is utilized to verify the feasibility of the proposed calibration method. The measurement results show that the proposed calibration enhances the SFDR and SNDR from 68.3 dB and 44.6 dB to 95.4 dB and 65.4 dB at low frequency, and from 56.8 dB and 35.6 dB to 90.6 dB and 63.6 dB at Nyquist frequency. Meanwhile, the original calibrator and improved calibrator are synthesized in Synopsys Design Compiler to compare their hardware complexity. Compared with the unoptimized version, the optimized schemes can decrease the logic area and the network weights up to 76% and 52%, with negligible loss in calibration performance.

The respective simplifications of prime-point DFT and ultra-long-point IDFT in PRACH are proposed. The former is an equivalent substitution of DFT function by using the property of ZC sequence, and the latter is an approximation based on cubic spline interpolation, which not only reduces the IDFT points, but also is easy to construct.

Intrusion Detection and Prevention (IDPS) is a critical cybersecurity task that involves monitoring network traffic for malicious activity and taking appropriate action to stop it. However, insufficient training data or improperly chosen thresholds often limit the accuracy of such systems, resulting in high false positive rates. To improve the accuracy of an IDPS, blockchain technology can be used. Blockchain technology provides a secure, decentralized, immutable ledger that can track suspicious activity over time and identify intrusions globally. Security teams can use blockchain technology to create immutable records of suspicious activity, give users visibility into the system, and improve the accuracy of intrusion detection systems. In this paper, we propose a novel methodology to improve the accuracy of blockchain-based intrusion detection and prevention systems, which is based on combining different intrusion detection algorithms and using a blockchain-integrated architecture. Our experimental results show that the proposed system significantly increases the accuracy while reducing the false positive rate, opening up new opportunities for the development of highly accurate networks.

There can be numerous electronic components on a given PCB, making the task of visual inspection to detect defects very time-consuming and prone to error, especially at scale. There has thus been significant interest in automatic PCB component detection, particularly leveraging deep learning. However, deep neural networks typically require high computational resources, possibly limiting their feasibility in real-world use cases in manufacturing, which often involve high-volume and high-throughput detection with constrained edge computing resource availability. As a result of an exploration of efficient deep neural network architectures for this use case, we introduce PCBDet, an attention condenser network design that provides state-of-the-art inference throughput while achieving superior PCB component detection performance compared to other state-of-the-art efficient architecture designs. Experimental results show that PCBDet can achieve up to 2× inference speed-up on an ARM Cortex A72 processor when compared to an EfficientNet-based design while achieving ∼2-4% higher mAP on the FICS-PCB benchmark dataset.

To improve the detection rate of pulmonary nodules in early lung cancer screening, a low-dose CT pulmonary nodule detection algorithm based on 3D CNN-CapsNet (3D convolution neural network and capsule network) was presented. However, the convolution kernel size of the traditional CNN is relatively simple at each layer, and it is difficult to obtain more abundant features, which is not effective for medical images with a hierarchical structure and does not fully consider the spatial information of medical sequence data. CapsNet is a new network architecture that can be used to classify, using a group of neurons as a capsule to replace the traditional neural networks, it may be made to the attribute information and spatial feature extraction. The network structure we designed includes FCN and CapsNet. First, the convolution kernels of different sizes are used to extract features at different scales. Then enter the initial feature map to obtain the first part into the designed CapsNet to get the final classification result. Through the experimental verification of the ELCAP database, the nodule detection rate is 95.19%, the sensitivity is 92.31%, the specificity is 98.08% and the F1-score is 0.95 which are much better than other baseline methods.

The target and sea clutter Doppler domains frequently overlap due to the frequent passage of slow ship targets through the sea clutter zone. In this letter, a novel sea clutter suppression method is suggested as a solution to this issue, whose key is a novel singular value zeroing criterion guided by the search results of two-dimensional spectral peaks. Verified by simulations, the method proposed can improve signal-to-clutter ratio (SCR) from -8 dB to 41 dB in the frequency domain and be more effective than the conventional SVD-FRFT method in[7] and improved SVD-FRFT method in[8] .

As detection technology continually advances, the survivability of targets on the battlefield is significantly challenged. Therefore, microwave absorbers with stealth capabilities have become a focal point of research in modern military science. To address the issues of narrow bandwidth and complex structures in existing absorbers, we propose a model for an ultra-wideband absorber based on a hybrid structure. In this study, we design, manufacture, and characterize a polarization-insensitive ultra-wideband absorber (PIUWA), which demonstrates impressive absorptivity of over 90% across a range of 4-24.53GHz (a fractional bandwidth of 144%). This is achieved by inducing multiple resonance peaks within the hybrid structure. Moreover, the subwavelength periodicity of the PIUWA theoretically contributes to its angular stability under full-wave polarizations. We observed that absorption performance remains stable under incident conditions within 45 degrees. Furthermore, the operational mechanism of the PIUWA is elucidated through an equivalent circuit model, with design validity confirmed via experimental measurements. This study paves the way for the design and fabrication of ultra-wideband microwave absorbers that offer high absorptivity, robust angular stability, and simpler assembly processes, thereby broadening the potential for application in other absorber types.