Optimal Metrics Research Articles

Optimizing soft robot design and tracking with and without evolutionary computation: an intensive survey

AbstractSoft robotic devices are designed for applications such as exploration, manipulation, search and rescue, medical surgery, rehabilitation, and assistance. Due to their complex kinematics, various and often hard-to-define degrees of freedom, and nonlinear properties of their material, designing and operating these devices can be quite challenging. Using tools such as optimization methods can improve the efficiency of these devices and help roboticists manufacture the robots they need. In this work, we present an extensive and systematic literature search on the optimization methods used for the mechanical design of soft robots, particularly focusing on literature exploiting evolutionary computation (EC). We completed the search in the IEEE, ACM, Springer, SAGE, Elsevier, MDPI, Scholar, and Scopus databases between 2009 and 2024 using the keywords “soft robot,” “design,” and “optimization.” We categorized our findings in terms of the type of soft robot (i.e., bio-inspired, cable-driven, continuum, fluid-driven, gripper, manipulator, modular), its application (exploration, manipulation, surgery), the optimization metrics (topology, force, locomotion, kinematics, sensors, and energy), and the optimization method (categorized as EC or non-EC methods). After providing a road map of our findings in the state of the art, we offer our observations concerning the implementation of the optimization methods and their advantages. We then conclude our paper with suggestions for future research.

Preparation, structural characterization, and functional properties of wheat gluten amyloid fibrils-chitosan double network hydrogel as delivery carriers for ferulic acid

It has been demonstrated that ferulic acid (FA) can be effectively encapsulated using wheat gluten amyloid fibrils (AF) and chitosan (CS) in a double network hydrogel (DN) form, with cross-linking mediated by Genipin (GP). Within this system, the DN comprising gluten AF-FA and CS-FA exhibited optimal loading metrics at a formulation designated as DN8, achieving a load efficiency of 88.5 % and a load capacity of 0.78 %. Analysis through fluorescence quenching confirmed that DN8 harbored the highest quantity of FA. Fourier-transform infrared spectroscopy (FTIR) further verified a significant increase in β-sheet content post-hydrogel formation, enhancing the binding capacity for FA. Rheological assessments indicated a transition from solution to gel, delineating the phase state of the DN. Comprehensive in vitro digestion studies revealed that DN8 provided superior sustained release properties, exhibited the highest total antioxidant capacity, and displayed potent inhibitory activities against angiotensin I converting enzyme (ACE) and acetylcholinesterase (Ach-E). Additionally, the DN significantly bolstered the stability of FA against photothermal degradation. Collectively, these findings lay foundational insights for the advancement of the wheat gluten AF-based delivery system for bioactive compounds and provided a theoretical basis for the development of functional foods.

Performance enhancement of optical pulse coding phase-sensitive OTDR system with genetic-optimized code.

Optical pulse coding (OPC) in phase-sensitive optical time-domain reflectometry (φ-OTDR) traditionally relies on standard coding sequences as probe pulse trains, which usually suffer the problem of distorted probe pulse caused by the gain saturation in erbium-doped fiber amplifier (EDFA). To improve the signal-to-noise ratio (SNR) and mitigate distortion caused by the EDFA gain saturation, a genetic-optimized code (Go-code) based OPC φ-OTDR (GOPC φ-OTDR) scheme is proposed in this paper. The unique Go-code sequence generated by the distributed genetic algorithm (DGA) is explored in the field of φ-OTDR for the first time, and the coding sequence was optimized to adapt to the vibration sensing requirements in φ-OTDR. Theoretical analyses focused on EDFA gain saturation effects and coding gain were conducted. The experiment of GOPC φ-OTDR demonstrated significant improvements compared with Golay φ-OTDR, achieving less distortion in decoding response and approximately 7.48 dB phase demodulation SNR enhancement within a 10-km sensing range. This research marks Go-code's pioneering exploration into φ-OTDR, demonstrating substantial advancements in optimization metrics, and opening avenues for exploring advanced OPC technologies.

Consideration of Optimal Evaluation Metrics for Internal Gross Tumor Dose Relevant to Tumor Response in Multi-fraction Stereotactic Radiosurgery of Brain Metastasis.

Introduction In stereotactic radiosurgery (SRS) for brain metastasis (BM), the target dose inhomogeneity remains highly variable among modalities, irradiation techniques, and facilities, which can affect tumor response during and after multi-fraction SRS. Volumetric-modulated arcs (VMAs) can provide a concentrically-layered steep dose increase inside a gross tumor volume (GTV) boundarycompared to dynamic conformal arcs. This study was conducted to review the optimal evaluation method for the internal GTV doses relevant to maximal response and local control, specifically to examine the significance of the doses 2 mm and 4 mm inside the GTV boundary in VMA-based SRS. Materials and methods This was a planning study for the clinical scenario of a single BM and targeted 25 GTVs of >0.50 cc, including eight spherical models with diameters of 10-45 mm and 17 clinical BMs (GTV: 0.72-44.33 cc). SRS plans were generated for each GTV using VMA with a 5-mm leaf-width multileaf collimator and the optimization that prioritized the steepness of the dose gradient outside the GTV boundary without any internal dose constraints. The dose prescription and evaluation were based on the GTV D V-0.01 cc, a minimum dose of GTV minus 0.01 cc. Two planning systems were compared for the GTV - 2 mm and GTV - 4 mm structures that were generated by equally reducing 2 mm and 4 mm from the GTV surface. The D eIIVs, a minimum dose of the irradiated isodose volume equivalent to the GTV - 2 mm and GTV - 4 mm, were compared to other common metrics. Results The GTV - 2 mm and GTV - 4 mm volumes differed significantly between the systems. In the spherical GTVs, the irradiated isodose surfaces of GTV D 80% and D 50% corresponded to 0.4-1.6 mm (<2 mm) and 1.0-4.6 mm inside the GTV boundary, respectively. In the 25 GTVs, the GTV - 2 mm coverage with the D eIIV varied from 83.7% to 98.2% (95-98% in 68% of the cases), while the GTV coverage with the GTV - 2 mm D eIIV was 20.2-75.9%. In the 23 GTVs of ≥1.26 cc, the GTV coverage with the GTV - 4 mm D eIIVvaried from 1.9% to 55.6% (<50% in 87% of the cases). No significant difference was observed between the GTV D 50% and the GTV - 2 mm D eIIV, while the GTV - 4 mm D eIIV was significantly higher than the GTV D 50%. No significant correlations were observed between the GTV D 50% and the D eIIVs of the GTV - 2 mm and GTV - 4 mm. Conclusions The doses 2 mm and 4 mm inside a GTV have low correlations with the GTV D 50% and may be more relevant tomaximal response and local control for SRS of BM. The D eIIVinstead of the minimum dose of a fixed % coverage (e.g. D 98%) is suitable for reporting the doses 2 mm and 4 mm inside the GTV boundary in terms of avoiding the over- or under-coverage, with consideration tosubstantial variability inminus margin addition functions among planning systems. In VMA-based SRS with a steep dose gradient, the doses 2-4 mm inside a GTV decrease significantly as the GTV increases, which can attenuate the excessive dose exposure to the surrounding brain in a large BM due to the GTV shrinkage during multi-fraction SRS.

Associating reservoir operations with 2D inundation risk and climate uncertainty

ABSTRACT Bridging the research gap between reservoir operations and inundation risks under the future climate, this study integrates a hydrologic reservoir management model with a 2D hydrodynamic model, comparing the conventional regulations and the optimized reservoir operations based on the particle swarm optimization (PSO) algorithm. Results reveal that optimized operations using the PSO algorithm consistently outperform conventional strategies by better-managing peak discharges and controlling downstream inundation. The study further differentiates between PSO-optimized plans: PSO1, which focuses on minimizing inundation areas, and PSO2, which prioritizes peak reduction at the flood control point. Interestingly, PSO2 proves superior for single-point peak reduction, typically the primary objective in current practices, whereas PSO1, despite lesser peak reduction, achieves a smaller inundation area, enhancing basin-scale flood resilience. This discrepancy reveals the need to consider downstream inundation risks as critical evaluation metrics in reservoir optimization, a factor often overlooked in existing studies. The research underscores the importance of updating operational frameworks to incorporate 2D inundation risks and adapt to increased flood risks under changing climate conditions. Despite optimization, future climate scenarios predict increased flood exposure, indicating that the current safety discharge rates and flow regulations at control points are outdated and require revision.

Intelligent tongue diagnosis model for gastrointestinal diseases based on tongue images

BackgroundCurrently, the diagnosis of gastrointestinal diseases relies heavily on electronic endoscopy, which is not suitable for all individuals and insufficient for dynamic patient monitoring. Therefore, there is an urgent need for a convenient and noninvasive examination method to enhance the diagnosis and monitoring of gastrointestinal diseases. MethodsSince the tongue is a significant reflection of the digestive system and closely associated with gastrointestinal diseases, our study aims to develop a diagnostic framework based on tongue features. We have collected a dataset of 2,167 tongue coating images from 949 consecutive patients, including 905 images from healthy individuals and 1,262 images from patients with various gastrointestinal conditions. Notably, each patient sample may represent distinct gastrointestinal diseases. This study introduces a novel approach to information fusion detection, integrating hand-crafted and auto-encoded features, alongside disease detection employing Squeeze and Excitation (SE) and Slot attention mechanisms. These two branches respectively analyze image feature and spatial position information when utilizing tongue images for diagnosing gastrointestinal diseases. The amalgamation of predictions from both aspects yields the final detection outcome. ResultsFor both healthy and diseased cohorts, the optimal classification metrics were achieved: AUC = 0.886, ACC = 0.849, and TPR = 0.965, indicating satisfactory performance. Notably, our framework demonstrated promising results in detecting five common gastrointestinal diseases: Helicobacter pylori infection, bile reflux, reflux esophagitis, gastric erosion, and duodenal erosion. Ablation experiments underscored the efficacy of mixed features in multi-color space, showcasing an increase in AUC and ACC by 13.48 % and 12.05 % respectively. Furthermore, the attention mechanism, by focusing on the middle region of tongue images, contributed to a 6.12 % increase in AUC and a 6.35 % increase in ACC. ConclusionsIn this study, we successfully develop and validate a diagnosis framework for gastrointestinal diseases based on tongue image features. By utilizing tongue images, we achieve intelligent diagnosis of gastrointestinal diseases, establishing tongue diagnosis as a convenient, cost-effective, and noninvasive method for diagnosing and screening gastrointestinal diseases.

New Virus Variant Detection Based on the Optimal Natural Metric.

The highly variable SARS-CoV-2 virus responsible for the COVID-19 pandemic frequently undergoes mutations, leading to the emergence of new variants that present novel threats to public health. The determination of these variants often relies on manual definition based on local sequence characteristics, resulting in delays in their detection relative to their actual emergence. In this study, we propose an algorithm for the automatic identification of novel variants. By leveraging the optimal natural metric for viruses based on an alignment-free perspective to measure distances between sequences, we devise a hypothesis testing framework to determine whether a given viral sequence belongs to a novel variant. Our method demonstrates high accuracy, achieving nearly 100% precision in identifying new variants of SARS-CoV-2 and HIV-1 as well as in detecting novel genera in Orthocoronavirinae. This approach holds promise for timely surveillance and management of emerging viral threats in the field of public health.

A Comprehensive Decision-Making Approach for Strategic Product Module Planning in Mass Customization

This paper explores the integrated optimization of complex coupled industrial manufacturing systems and production strategies based on user customization needs. Two optimization metrics are considered: one is whether the production process of engineering manufacturing is simplified, and the other is whether it is based on the customization requirements of the customer. These two metrics are interrelated, and cases may even be conflicting. Considering the interdependence between engineering manufacturing and user requirements, this paper develops an integrated customized modular engineering manufacturing process to minimize production and maintenance costs and improve efficiency while meeting user customization requirements. This paper takes expert evaluation as an important decision indicator and optimizes the production process strategy on this basis. Finally, a case study is given to illustrate the applicability of the proposed process model.

Predicting the shear strength of rectangular RC beams strengthened with externally-bonded FRP composites using constrained monotonic neural networks

Fiber-reinforced polymer (FRP) composites bonded externally to reinforced concrete beams have shown promise for increasing shear load-carrying capacity. However, accurately predicting the shear strength contribution of FRP remains challenging due to the complexity of the shear failure mechanisms and interactions between the contributing components. Existing design provisions have limitations in modeling this behavior. Recently, machine learning has shown promise for addressing such complex problems but lacks transparency. Therefore, this study developed a data-driven Constrained Monotonic Neural Network (CMNN) approach implemented using Python libraries Keras and Tensorflow, which integrates domain knowledge to provide reliable FRP shear-strength predictions. This framework directly encodes expected monotonic input-output relationships through constraints, harnessing both predictive power and monotonicity guarantees. A comprehensive database of 273 beam tests was used to train and evaluate the model. Bayesian optimization through the Optuna framework and 4-fold cross-validation were used to tune the hyperparameters of the CMNN model. Extensive evaluation demonstrated the CMNN model's superior accuracy, yielding an R² value over 0.9, a CI above 0.93 and a low MAE under 16 kN on both training and unseen test data. The model also outperformed existing design code provisions, achieving significantly optimal performance metrics across various strengthening configurations. The analysis of the predictions revealed logical sensitivity trends aligned with the principles of shear mechanics. Such physics-informed machine learning presents a next-generation technique for performance-based structural design, contributing an effective and interpretable tool to advance knowledge on quantifying the shear capacity contribution of externally bonded FRP reinforcement in concrete beams.

The optimal metric for viral genome space

Understanding the structural similarity between genomes is pivotal in classification and phylogenetic analysis. As the number of known genomes rockets, alignment-free methods have gained considerable attention. Among these methods, the natural vector method stands out as it represents sequences as vectors using statistical moments, enabling effective clustering based on families in biological taxonomy. However, determining an optimal metric that combines different elements in natural vectors remains challenging due to the absence of a rigorous theoretical framework for weighting different k-mers and orders. In this study, we address this challenge by transforming the determination of optimal weights into an optimization problem and resolving it through gradient-based techniques. Our experimental results underscore the substantial improvement in classification accuracy achieved by employing these optimal weights, reaching an impressive 92.73% on the testing set, surpassing other alignment-free methods. On one hand, our method offers an outstanding metric for virus classification, and on the other hand, it provides valuable insights into feature integration within alignment-free methods.

DPP: deep phase prior for parallel imaging with wave encoding

Objective. In Magnetic Resonance (MR) parallel imaging with virtual channel-expanded Wave encoding, limitations are imposed on the ability to comprehensively and accurately characterize the background phase. These limitations are primarily attributed to the calibration process relying solely on center low-frequency Auto-Calibration Signals (ACS) data for calibration. Approach. To tackle the challenge of accurately estimating the background phase in wave encoding, a novel deep neural network model guided by deep phase priors is proposed with integrated virtual conjugate coil (VCC) extension. Concretely, within the proposed framework, the background phase is implicitly characterized by employing a carefully designed decoder convolutional neural network, leveraging the inherent characteristics of phase smoothness and compact support in the transformed domain. Furthermore, the proposed model with wave encoding benefits from additional priors, which incorporate transmission sparsity of the latent image and coil sensitivity smoothness. Main results. Ablation experiments were conducted to ascertain the proposed method’s capability to implicitly represent CSM and the background phase. Subsequently, the superiority of the proposed method is demonstrated through confidence comparisons with competing methods, employing 4-fold and 5-fold acceleration experiments. In achieving 4-fold and 5-fold acceleration, the optimal quantitative metrics (PSNR/SSIM/NMSE) are 44.1359 dB/0.9863/0.0008 (4-fold) and 41.2074/0.9846/0.0017 (5-fold), respectively. Furthermore, the generalizability of the proposed method is further validated by conducting acceleration experiments with T1, T2, T2*, and various undersampling patterns. In addition, the DPP delivered much better performance than the conventional methods by exploring accelerated phase-sensitive SWI imaging. In SWI accelerated imaging, it also surpasses the optimal competing method in terms of (PSNR/SSIM/NMSE) with 0.096%/0.009%/0.0017%. Significance. The proposed method enables precise characterization of the background phase in the integrated VCC and wave encoding framework, supported via theoretical analysis and empirical findings. Our code is available at: https://github.com/sober235/DPP.

Evaluating the reliability of bi-temporal canopy height model generated from airborne laser scanning for monitoring forest growth in boreal forest region

ABSTRACT The discrepancies in data across different phases and the unexplored optimal spatial resolution present challenges when using multi-temporal canopy height models to accurately discern actual forest growth. In this study, we evaluated the reliability of bi-temporal CHMs to characterize growth changes in a boreal natural forest over a four-year period. A maximum mosaic method was introduced to construct a CHM from various flight strips, aimed at minimizing data alignment errors. Subsequently, the canopy height and height changed derived from six different height percentile metrics and five spatial resolutions were evaluated. The results showed that higher resolution (e.g., < 2 m) and lower height metrics (e.g., 85th height percentile) consistently underestimated. Tree growth correlation with individual segmentation surpassed field-measurements at all resolutions and height metrics. for the optimal resolution and height metrics, the results suggest that using a 95th height percentile the 10 m scale effectively represents both canopy height ( R 2012 2 = 0.95, RMS E 2012 = 0.88 m, rRMS E 2012 = 5.83%; R 2016 2 = 0.96, RMS E 2016 = 1.11 m, rRMS E 2016 = 6.91%) and height changes ( R 2 = 0.59, RMSE = 0.86 m, rRMSE = 18.38%). This study demonstrates the necessity of carefully evaluating data characteristics and resolutions when employing multi-temporal CHM for forest dynamics monitoring.

A Systematic Review of Multi-Objective Evolutionary Algorithms Optimization Frameworks

The study of evolutionary algorithms (EAs) has witnessed an impressive increase during the last decades. The need to explore this area is determined by the growing request for design and the optimization of more and more engineering problems in society, such as highway construction processes, food and agri-technologies processes, resource allocation problems, logistics and transportation systems, microarchitectures, suspension systems optimal design, etc. All of these matters refer to specific highly computational problems with a huge design space, hence the obvious need for evolutionary algorithms and frameworks, or platforms that allow for the implementing and testing of such algorithms and methods. This paper aims to comparatively analyze the existing software platforms and state-of-the-art multi-objective optimization algorithms and make a review of what features exist and what features might be included next as further developments in such tools, from a researcher’s perspective. Additionally, it is essential for a framework to be easily extendable with new types of problems and optimization algorithms, metrics and quality indicators, genetic operators or specific solution representations and results analysis and comparison features. After presenting the most relevant existing features in these types of platforms, we suggest some future steps and the developments we have been working on.

The Removal of Total Knee Arthroplasty From the Inpatient-Only List has Improved Patient Optimization.

On January 1, 2018, the Centers for Medicare and Medicaid Services removed total knee arthroplasty (TKA) from the inpatient-only (IPO) list, expanding outpatient TKA (oTKA) to include patients with insurance coverage through their programs. These regulatory changes reinforced the need for preoperative optimization to ensure a safe and timely discharge after surgery. This study compared modifiable preoperative optimization metrics in patients who underwent oTKA pre-IPO and post-IPO removal. The authors hypothesized that patients post-IPO removal would demonstrate improvement in the selected categories. Outpatient TKA in a national database was identified and stratified by surgical year (2015 to 2017 versus 2018 to 2020). Preoperative optimization thresholds were established for the following modifiable risk factors: albumin, hematocrit, sodium, smoking, and body mass index. The percentage of patients who did not meet thresholds pre-IPO and post-IPO removal were compared. In total, 2,074 patients underwent oTKA from 2015 to 2017 compared with 46,480 from 2018 to 2020. Patients undergoing oTKA after IPO removal were significantly older (67.0 versus 64.4 years; P < 0.01). A lower percentage of patients in the post-IPO cohort fell outside the threshold for all modifiable risk factors. Results were significant for preoperative sodium (10.7% versus 8.8%; P < 0.01), body mass index (12.4% versus 11.0% P = 0.05), and smoking history (9.9% versus 6.6%; P < 0.01). Outpatient TKA has increased considerably post-IPO removal. As this regulatory change has allowed older patients with increased comorbidities to undergo oTKA, the need for appropriate preoperative optimization has increased. The current data set demonstrates that surgeons have improved preoperative optimization efforts for select modifiable risk factors.

Efficient Energy Management for Intelligent Microrobotic Swarms: Design and Impact

AbstractEnergy serves as the foundational element for all active functions within microrobots. Harvesting devices, such as photovoltaic cells and coils, play a crucial role in converting diverse forms of energy into electricity, while energy storage devices enable uninterrupted operation and liberate microrobots from dependence on external sources. Despite the evident importance of energy, there exists a significant disconnect between the development of energy devices and their integration into microrobotic systems, hampering the deployment of microrobots across diverse fields from agriculture to microsurgery. Here, for transcending the “material for material's sake” focus on simple generic metrics for material optimization, such as energy density, advocating attention to the higher tier transformation of material metrics that determine device‐level performance and so make a meaningful contribution to microrobotic technology is argued. Appropriate metrics for microrobotic swarms challenge the stereotype that tiny energy supplies are impractical; instead, swarms simplify individual microrobotic functions, reducing single energy supply requirements and utilizing swarm energy distribution and management. In addition to essential evaluations of the environmental and social impacts of intelligent microrobotic swarms for their safe and beneficial implementation, research targeting these higher‐tier metrics is crucial to connecting energy material research and microrobot developments effectively.

A Machine learning perspective on hardness prediction in multicomponent Al-Mg based lightweight alloys

Achieving the desired hardness in Al-Mg alloys through experimentation is challenging, costly, and time-consuming, given the extensive composition variations and diverse aging conditions. This research utilizes a range of machine learning (ML) techniques to expedite the advancement of high-performance Al-Mg alloys. The research focused on Al-Mg-X (where X represents elements like Cu, Zn, etc.) alloys, compiling data from literature sources that included composition of alloy, the conditions under which aging occurred (including both time and temperature factors), various physical properties, and the measurement of hardness. These datasets were employed to train seven different ML algorithms aimed at predicting Al alloys with enhanced hardnesses. The findings indicated that the CatBoost model proved effective in forecasting the hardness with excellent predictive performance, surpassing other machine learning approaches, as evidenced by various optimisation metrics.

Optimized Probabilistic Sink Node Deployment for Enhanced Performance in Wireless Sensor Networks

This paper presents an innovative mechanism to deploy sink nodes in Wireless Sensor Networks (WSN), which play an essential role in the gathering sensed data from sensor nodes across diverse applications. Our mechanism is different from existing mechanisms, which deploy sink nodes based on the residual status of buffer and energy of the sensor nodes. Instead, proposed mechanism’s sink node deployment is decided based on the optimal probabilistic metrics related to buffer and energy status during data processing; aim to extend the network performance with respect to risk of buffer overflow and energy consumption. We analyse and validate the performance of the proposed work through simulation and compared the results with existing residual-based sink node deployment mechanisms. The results indicate the signification extension in the performance in terms of energy, packet loss and lifetime by mitigating bottlenecks at nodes. The work contributes to the improvement of WSN design and deployment mechanisms by significantly enhancing the performance and reliability of the network.

Development of High-Temperature Permanent Magnet Synchronous Motor for Autonomous Robotic Navigation: A Design and Finite Element Analysis Approach

The traditional industrial robots come with the prime mover, i.e. Electric Motors (EM) which ranges from a few hundred too few kilo watts of power ratings. However, for autonomous robotic navigation systems, we require motors which are light weighted with the aspect of high torque and power density. This aspect is very critical, when the EMs in robotic navigations are subjected to harsh high temperature survival conditions, where the sustainability of the performance metrics of the electromagnetic system of the EMs degrade with the prevailing high temperature conditions. Hence, this research work address and formulate the design methodology to develop a 630 W High Temperature PMSM (HTPMSM) in the aspect of high torque and power density, which can be used for the autonomous robotic navigation systems under high temperature survival conditions of 200°C. Two types of rotor configurations i.e. the Surface Permanent Magnet type (SPM) and the Interior Permanent Magnet type (IPM) of HTPMSM are examined for its optimal electromagnetic metrics under the temperature conditions of 200°C. The 630 W HTPMSM is designed to deliver the rated torque of 2 Nm within the volumetric &amp;amp; diametric constraints of D x L which comes at 80 x 70 mm at the rated speed of 3000 rpm with the survival temperature of 200°C with the target efficiency of greater than 90%. The FEM based results are validated through the hardware prototypes for both SPM and IPM types, and the results confirm the effectiveness of the proposed design methodology of HTPMSM for sustainable autonomous robotic navigation applications.

FSformer: A combined frequency separation network and transformer for LDCT denoising

Low-dose computed tomography (LDCT) has been widely concerned in the field of medical imaging because of its low radiation hazard to humans. However, under low-dose radiation scenarios, a large amount of noise/artifacts are present in the reconstructed image, which reduces the clarity of the image and is not conducive to diagnosis. To improve the LDCT image quality, we proposed a combined frequency separation network and Transformer (FSformer) for LDCT denoising. Firstly, FSformer decomposes the LDCT images into low-frequency images and multi-layer high-frequency images by frequency separation blocks. Then, the low-frequency components are fused with the high-frequency components of different layers to remove the noise in the high-frequency components with the help of the potential texture of low-frequency parts. Next, the estimated noise images can be obtained by using Transformer stage in the frequency aggregation denoising block. Finally, they are fed into the reconstruction prediction block to obtain improved quality images. In addition, a compound loss function with frequency loss and Charbonnier loss is used to guide the training of the network. The performance of FSformer has been validated and evaluated on AAPM Mayo dataset, real Piglet dataset and clinical dataset. Compared with previous representative models in different architectures, FSformer achieves the optimal metrics with PSNR of 33.7714 dB and SSIM of 0.9254 on Mayo dataset, the testing time is 1.825 s. The experimental results show that FSformer is a state-of-the-art (SOTA) model with noise/artifact suppression and texture/organization preservation. Moreover, the model has certain robustness and can effectively improve LDCT image quality.

Quantum Implementation of AIM: Aiming for Low-Depth

Security vulnerabilities in the symmetric-key primitives of a cipher can undermine the overall security claims of the cipher. With the rapid advancement of quantum computing in recent years, there is an increasing effort to evaluate the security of symmetric-key cryptography against potential quantum attacks. This paper focuses on analyzing the quantum attack resistance of AIM, a symmetric-key primitive used in the AIMer digital signature scheme. We present the first quantum circuit implementation of AIM and estimate its complexity (such as qubit count, gate count, and circuit depth) with respect to Grover’s search algorithm. For Grover’s key search, the most important optimization metric is depth, especially when considering parallel search. Our implementation gathers multiple methods for a low-depth quantum circuit of AIM in order to reduce the Toffoli depth and full depth (such as the Karatsuba multiplication and optimization of inner modules; Mer, LinearLayer).