Search

Optical Coherence Tomography (OCT) is a crucial imaging modality for diagnosing and monitoring retinal diseases. However, the accurate segmentation of fluid regions and lesions remains challenging due to noise, low contrast, and blurred edges in OCT images. Although feature modeling with wide or global receptive fields offers a feasible solution, it typically leads to significant computational overhead. To address these challenges, we propose LKMU-Lite, a lightweight U-shaped segmentation method tailored for retinal fluid segmentation. LKMU-Lite integrates a Decoupled Large Kernel Attention (DLKA) module that captures both local patterns and long-range dependencies, thereby enhancing feature representation. Additionally, it incorporates a Multi-scale Group Perception (MSGP) module that employs Dilated Convolutions with varying receptive field scales to effectively predict lesions of different shapes and sizes. Furthermore, a novel Aggregating-Shift decoder is proposed, reducing model complexity while preserving feature integrity. With only 1.02 million parameters and a computational complexity of 3.82 G FLOPs, LKMU-Lite achieves state-of-the-art performance across multiple metrics on the ICF and RETOUCH datasets, demonstrating both its efficiency and generalizability compared to existing methods.

In the 5G and beyond networks, low-latency digital signatures are essential to ensure the security, integrity, and non-repudiation of massive data in communication processes. The binary finite field-based elliptic curve digital signature algorithm (ECDSA) is particularly suitable for achieving low-latency digital signatures due to its carry-free characteristics. This paper proposes a low-latency and universal architecture for point multiplication (PM) and double point multiplication (DPM) based on the differential addition chain (DAC) designed for signing and verification in ECDSA. By employing the DAC, the area-time product of DPM can be decreased, and throughput efficiency can be increased. Besides, the execution pattern of the proposed architecture is uniform to resist simple power analysis and high-order power analysis. Based on the data dependency, two Karatsuba–Ofman multipliers and four non-pipeline squarers are utilized in the architecture to achieve a compact timing schedule without idle cycles for multipliers during the computation process. Consequently, the calculation latency of DPM is minimized to five clock cycles in each loop. The proposed architecture is implemented on Xilinx Virtex-7, performing DPM in 3.584, 5.656, and $7.453~\mu s$ with 8135, 13372, and 17898 slices over GF(2163), GF(2233), GF(2283), respectively. In the existing designs that are resistant to high-order analysis, our architecture demonstrates throughput efficiency improvements of 36.7% over GF(2233) and 9.8% over GF(2283), respectively.

With the development of mobile communication, digital signatures with low latency, low area, and high security are in increasing demand. Elliptic curve cryptography (ECC) is widely used because of its security and lightweight. Elliptic curve scalar multiplication (ECSM) is the basic arithmetic in ECC. Based on this background information, we propose our own research objectives. In this paper, a low-latency and low-area ECSM architecture based on the comb algorithm is proposed. The detailed methodology is as follows. The recoding-k algorithm and randomization-Z algorithm are used to improve security, which can resist sample power analysis (SPA) and differential power analysis (DPA). A low-area multi-functional architecture for comb is proposed, which takes into account different stages of the comb algorithm. Based on this, the data dependency is considered and the comb architecture is optimized to achieve a uniform and efficient execution pattern. The interleaved modular multiplication algorithm and modified binary inverse algorithm are used to achieve short clock cycle delay and high frequency while taking into account the need for a low area. The proposed architecture has been implemented on Xilinx Virtex-7 series FPGA to perform ECSM on 256-bits prime field GF(p). In the hardware architecture with only 7351 slices of resource usage, a single ECSM only takes 0.74 ms, resulting in an area-time product (ATP) of 5.41. The implementation results show that our design can compete with the existing state-of-the-art engineering in terms of performance and has higher security. Our design is suitable for computing scenarios where security and computing speed are required. The implementation of the overall architecture is of great significance and inspiration to the research community.

This paper describes an N-path sub-GHz ultra-low power receiver exploiting an N-path notch filter topology in 90nm CMOS process. The receiver achieves the flexibility in the operating frequency due to an adjustable internal impedance matching network with an N-path notch filter. The receiver consists of two current-reused topologies, which greatly simplifies the structure and achieves ultra-low power consumption. Specifically, the receiver incorporates: 1) an amplifier with an N-path notch filter, which acts as the first stage of the receiver to provide an input impedance of $50~\Omega $ without an inductor. This input matching network is frequency flexible and adjustable to suit different frequencies of the input signal. Meanwhile, the N-path notch filter can suppress out-of-band interference to cope with limited frequency bands. 2) N-path passive mixers are reused for simultaneous filtering and down-conversion. 3) Amplifiers are frequency-division multiplexed to amplify both RF and baseband signals simultaneously. Finally, the receiver is fabricated in 90 nm CMOS and operates at 0.6 V supply voltage with a power consumption of $780~\mu \text{W}$ . The receiver achieves a conversion gain of 41.2 ± 3.2 dB, a noise figure of 5.7 ± 0.3 dB and an OB-IIP3 of 14.6 ± 0.5 dBm. The chip area of the implemented receiver is 0.08 mm2.

Physically unclonable functions (PUFs) are crucial for enhancing cybersecurity by providing unique, intrinsic identifiers for electronic devices, thus ensuring their authenticity and preventing unauthorized cloning. The SRAM-PUF, characterized by its simple structure and ease of implementation in various scenarios, has gained widespread usage. The soft-decision Reed–Muller (RM) code, an error correction code, is commonly employed in these designs. This paper introduces the design of an RM code soft-decision attack algorithm to reveal its potential security risks. To address this problem, we propose a soft-decision SRAM-PUF structure based on the elliptic curve digital signature algorithm (ECDSA). To improve the processing speed of the proposed secure SRAM-PUF, we propose a custom ECDSA scheme. Further, we also propose a universal architecture for the critical operations in ECDSA, elliptic curve scalar multiplication (ECSM), and elliptic curve double scalar multiplication (ECDSM) based on the differential addition chain (DAC). For ECSMs, iterations can be performed directly; for ECDSMs, a two-dimensional DAC is constructed through precomputation, followed by iterations. Moreover, due to the high similarity of ECSM and ECDSM data paths, this universal architecture saves hardware resources. Our design is implemented on a field-programmable gate array (FPGA) and an application-specific integrated circuit (ASIC) using a Xilinx Virtex-7 and an TSMC 40 nm process. Compared to existing research, our design exhibits a lower bit error rate (2.7×10−10) and better area–time performance (3902 slices, 6.615 μs ECDSM latency).

Identifying the classes and locations of prohibited items is the target of security inspection. However, X-ray security inspection images with insufficient feature extraction, imbalance between easy and hard samples, and occlusion lead to poor detection accuracy. To address the above problems, an object-detection method based on YOLOv8 is proposed. Firstly, an ASFF (adaptive spatial feature fusion) and a weighted feature concatenation algorithm are introduced to fully extract the scale features from input images. In this way, the model can learn further details in training. Secondly, CoordAtt (coordinate attention module), which belongs to the hybrid attention mechanism, is embedded to enhance the learning of features of interest. Then, the slide loss function is introduced to balance the simple samples and the difficult samples. Finally, Soft-NMS (non-maximum suppression) is introduced to resist the conditions containing occlusion. The experimental result shows that mAP (mean average precision) achieves 90.2%, 90.5%, 79.1%, and 91.4% on the Easy, Hard, and Hidden sets of the PIDray and SIXray public test set, respectively. Contrasted with original model, the mAP of our proposed YOLOv8n model increased by 2.7%, 3.1%, 9.3%, and 2.4%, respectively. Furthermore, the parameter count of the modified YOLOv8n model is roughly only 3 million.

Abstract The sum–difference coarray is the union of difference coarray and the sum coarray, which is capable to obtain a higher number of degrees of freedom (DOF) than the difference coarray. However, this method fails to use all information provided by the coprime array because of the existence of holes. In this paper, we introduce the virtual array interpolation into the sum–difference coarray domain. After interpolating the virtual array, we estimate the DOA by reconstructing the covariance matrix to resolve an atomic norm minimization problem in a gridless way. The proposed method is gridless and can effectively utilize the DOF of a larger virtual array. Numerical simulation results verify the effectiveness and the superior performance of the proposed algorithm.

High-performance Elliptic Curve Cryptography (ECC) implementation in encryption authentication severs has become a challenge due to the explosive growth of e-commerce’s demand for speed and security. Point multiplication (PM) is the most common and complex operation in ECC which directly determines the performance of the whole system. This article proposes a 6CC-6CC (clock cycle) dual-field PM architecture and a 6CC-4CC dual-field PM architecture based on maximizing utilization of Karatsuba multipliers and re-ordering schedule strategy in PM. The Montgomery Ladder algorithm used in PM is modified to a 4CC algorithm for better resource utilization and parallel computation. To solve the frequency drop problem while working on large finite field, the PM architectures for high and low field are carefully studied to have universal critical path length and balanced performance. Both of the architectures are implemented over GF(2571) and GF(2283) on Xilinx Virtex-5 and Virtex-7 FPGAs (Field-Programmable Gate Array) for comparison. The 6CC-6CC architecture is shown to have the best performance on GF(2571), which achieves one PM operation in $17.44~\mu s$ using 81549 LUTs (Look-Up-Table) with the frequency of 197.2 MHz on Virtex-5, and $12.55~\mu s$ using 80970 LUTs with the frequency of 274.1 MHz on Virtex-7. The 6CC-4CC architecture performs better on GF(2283) with the shortest computation time. It takes only $3.21~\mu s$ to finish one PM operation on Virtex-5 and $2.22~\mu s$ on Virtex-7, which are faster than all the previous designs. The implementation results prove that the proposed architectures have state-of-the-art performance as well as higher versatility for ECC designs.

L-shaped array (LA) is traditionally used as two one-dimensional uniform linear arrays (ULAs) for two-dimensional direction of arrival (DOA) estimations. With elevation and azimuth angles estimated separately, the detectable source number is strictly limited by the number of elements in the ULAs. To increase the detection ability, in this paper, we propose to exploit the diff-sum co-array (DSCA) concept on the L-shaped array. As such, a virtual element in the quadrants of DSCA can be generated by only one pair of sensors in the LA. Therefore, it can acquire much higher number of degrees of freedom for DOA estimation. To further reduce the redundancy of elements on the horizontal and vertical axes of DSCA, an element moving strategy is proposed. It is proved that the DSCA can remain intact with any one or more physical sensors relocated to the centrosymmetric positions. Based on this strategy, a generalized L-shape array concept and a new configuration referred to as generalized L-shaped array with odd-even locations (GLA-OEL) are developed. For GLA-OEL, the number of sensor pairs with small spacing is as small as one, which dramatically reduces the mutual coupling of adjacent elements. Besides, with the same number of sensors, the GLA-OEL can generate much larger virtual uniform rectangular array in the DSCA. Simulations verify the superiorities of the proposed array in terms of detection performance and estimation accuracy.

The thyroid nodule segmentation of ultrasound images is a critical step for the early diagnosis of thyroid cancers in clinics. Due to the weak edge of ultrasound images and the complexity of thyroid tissue structure, it is still challenging to accurately segment the delicate contour of thyroid nodules. A local and context-attention adaptive network (LCA-Net) for thyroid nodule segmentation is proposed to address these shortcomings, which leverages both local feature information from convolution neural networks and global context information from transformers. Firstly, since most existing thyroid nodule segmentation models are skilled at local detail features and lose some context information, we propose a transformers-based context-attention module to capture more global associative information for the network and perceive the edge information of the nodule contour. Secondly, a backbone module with 7×1, 1×7 convolutions and the activation function Mish is designed, which enlarges the receptive field and extracts more feature details. Furthermore, a nodule adaptive convolution (NAC) module is introduced to adaptively deal with thyroid nodules of different sizes and positions, thereby improving the generalization performance of the model. Simultaneously, an optimized loss function is proposed to solve the pixels class imbalance problem in segmentation. The proposed LCA-Net, validated on the public TN-SCUI2020 and TN3K datasets, achieves Dice scores of 90.26% and 82.08% and PA scores of 98.87% and 96.97%, respectively, which outperforms other state-of-the-art thyroid nodule segmentation models. This paper demonstrates the superiority of the proposed LCA-Net for thyroid nodule segmentation, which possesses strong generalization performance and promising segmentation accuracy. Consequently, the proposed model has wide application prospects for thyroid nodule diagnosis in clinics.

This paper introduces a novel speed-oriented architecture of point multiplication in elliptic curve cryptography. A balanced full-precision multiplier is proposed to shorten latency, and a new modular inversion architecture is integrated to reduce the total number of clock cycles in point multiplication. A modified Montgomery Ladder algorithm that takes three clock cycles to calculate one input bit is proposed to best utilize hardware resources. A mixed-pipeline technique is used to balance the delay of different paths and increase frequency. The proposed architecture is implemented on GF(2163) and GF(2571), based on Xilinx Virtex-5 and Virtex-7 FPGA. For GF(2163), the design reaches 211 MHz, with 29309 LUTs, and 547 clock cycles or $2.6~\mu \text{s}$ latency on Virtex-5; 320.5 MHz, with 28911 LUTs and $1.7~\mu \text{s}$ latency on Virtex-7. For GF(2571), the design reaches 186 MHz, with 286400 LUTs, and 1813 clock cycles or $9.6~\mu \text{s}$ latency on Virtex-5; 267 MHz, 290001 LUTs and $6.79~\mu \text{s}$ latency on Virtex-7. The proposed design achieves the lowest latency among all existing works, and its performance is also among the top. Furthermore, it is demonstrated that the proposed architecture maintains a high speed for larger binary fields, making it more suitable to be implemented in large-bit-length platforms with a higher security level. Since the multiplier and its segments work in different bit-length and refer to different fields, the proposed architecture can also be upgraded to a reconfigurable design to support multiple-field point multiplication in the future.

Abstract The continuous generalized wavelet transform (GWT) which is regarded as a kind of time-linear canonical domain (LCD)-frequency representation has recently been proposed. Its constant-Q property can rectify the limitations of the wavelet transform (WT) and the linear canonical transform (LCT). However, the GWT is highly redundant in signal reconstruction. The discrete linear canonical wavelet transform (DLCWT) is proposed in this paper to solve this problem. First, the continuous linear canonical wavelet transform (LCWT) is obtained with a modification of the GWT. Then, in order to eliminate the redundancy, two aspects of the DLCWT are considered: the multi-resolution approximation (MRA) associated with the LCT and the construction of orthogonal linear canonical wavelets. The necessary and sufficient conditions pertaining to LCD are derived, under which the integer shifts of a chirp-modulated function form a Riesz basis or an orthonormal basis for a multi-resolution subspace. A fast algorithm that computes the discrete orthogonal LCWT (DOLCWT) is proposed by exploiting two-channel conjugate orthogonal mirror filter banks associated with the LCT. Finally, three potential applications are discussed, including shift sampling in multi-resolution subspaces, denoising of non-stationary signals, and multi-focus image fusion. Simulations verify the validity of the proposed algorithms.

The constant false alarm rate (CFAR) technology plays an important role in radar target detection. In cell averaging CFAR detectors, a reference window is usually adopted to estimate the average interference power in the range–Doppler matrix and to obtain the detection threshold, which should be set high enough to limit false alarm rate to a tolerably small rate. The conventional rectangular window, cross window, and column window are investigated in this study, which may suffer from a degraded detection performance due to involving some extraneous reference cells. Therefore, a modified reference window for two-dimensional CFAR is proposed to improve performance, depending on the distribution characteristics of radar targets in range and Doppler dimensions. The experimental results demonstrate its performance advantages on detection probability, CFAR loss, and figure of merit.

Cloud detection is a fundamental yet challenging topic in remote-sensing image processing. The authors propose a method for multi-dimensional feature extraction and superpixel segmentation, and use a voting-based clustering ensemble to capture the whole target shape. In order to further identify clouds, snow-covered lands, and bright buildings on remote-sensing images, they first implement an Ostu threshold to get high grey-level sub-regions, and then extract the descriptors of these sub-regions and put them into the softmax regression classifier. Regarding these methods, the authors conduct experiments using GF-1 remote-sensing images. The results demonstrate the effectiveness and excellency of their proposed method.

In this paper, an algorithm of direction finding is proposed in the presence of unknown mutual coupling. The preliminary direction of arrival (DOA) is estimated using the whole array for high resolution. Further refinement can then be conducted by estimating the angularly dependent coefficients (ADCs) with the subspace theory. The mutual coupling coefficients are finally determined by solving the least squares problem with all of the ADCs utilized without discarding any. Simulation results show that the proposed method can achieve better performance at a low signal-to-noise ratio (SNR) with a small-sized array and is more robust, compared with the similar processes employing the initial DOA estimation and further improvement iteratively.

The performance of underwater acoustic (UWA) communication is heavily dependent on channel estimation, which is predominantly researched by simulating UWA channels modelled in complex and dynamic underwater environments. In UWA channels modelling, the measurement-based approach provides an accurate method. However, acquirement of environment data and simulation processes are scenario-specific and thus not cost-effective. To overcome such restraints, this article proposes a comprehensive simulation platform that combines UWA channel modelling with orthogonal frequency division multiplexing (OFDM) channel estimation, allowing users to model UWA channels for different ocean environments and simulate channel estimation with configurable input parameters. Based on the simulation platform, three independent simulations are conducted to determine the impacts of receiving depth, sea bottom boundary, and sea surface boundary on channel estimation. The simulations show that UWA channel estimation is greatly affected by underwater environments. The effect can be mainly attributed to changing acoustic rays tracing which result in fluctuating time delay and amplitude. With 10 m receiving depth and flat sea bottom, the channel estimation achieves optimal performance. Further study indicates that the sea surface has stochastic effects on channel estimation. As the significant wave height (SWH) increases, the average performance of channel estimation shows improvements.

Atherosclerosis (AS) is a significant global health concern due to its high morbidity and mortality rates. Extensive efforts have been made to replicate the cardiovascular system and explore the pathogenesis, diagnosis, and treatment of AS. Microfluidics has emerged as a valuable technology for modeling the cardiovascular system and studying AS. Here a brief review of the advances of microfluidic-based cardiovascular systems for AS research is presented. The critical pathogenetic mechanisms of AS investigated by microfluidic-based cardiovascular systems are categorized and reviewed, with a detailed summary of accurate diagnostic methods for detecting biomarkers using microfluidics represented. Furthermore, the review covers the evaluation and screening of AS drugs assisted by microfluidic systems, along with the fabrication of novel drug delivery carriers. Finally, the challenges and future prospects for advancing microfluidic-based cardiovascular systems in AS research are discussed and proposed, particularly regarding new opportunities in multi-disciplinary fundamental research and therapeutic applications for a broader range of disease treatments. [ABSTRACT FROM AUTHOR]