Design patterns are best practices for known problems in a specific context. Many patterns have been proposed in different domains, such as object-orientated programming, software architecture, and workflows, to name a few. However, blockchain interoperability is a recent area of work and, to our knowledge, no design patterns have been defined yet. The purpose of this work was to identify blockchain interoperability patterns that may exist in blockchain interoperability solutions. We identified six patterns through the observation of 35 interoperability solutions. A specification was built for each pattern using the Alexandrian template. The specification was evaluated with five semi-structured interviews with blockchain experts to collect data on the comprehension, completeness, and utility of the patterns. The results show that all interviewees identified the patterns. However, the pattern specification has different degrees of confidence in terms of clarity, completeness, and utility. Finally, all interviewees thought that the proposed patterns may be helpful to software architects in their first blockchain interoperability project.

This paper proposes an artificial intelligence (AI) framework that leverages integrated sensing and communication (ISAC), aided by the age of sensing (AoS) to ensure the timely location updates of the users for a holographic MIMO (HMIMO)- enabled wireless network. The AI-driven framework guarantees optimal power allocation for efficient beamforming by activating the minimal number of grids from the HMIMO base station. An optimization problem is formulated to maximize the sensing utility function, aiming to maximize the signal-to-interference-plus-noise ratio (SINR) of the received signal, beam-pattern gains to improve the sensing SINR of reflected echo signals and maximizing the evidence lower bound minus loss function, which in turn minimizes the losses of the ISAC process, and maximizes achievable rate for efficient power allocation. A novel AI-driven framework is presented to tackle the formulated NP-hard problem by decomposing it into two problems: a sensing problem and a power allocation problem. The sensing problem is solved by employing a variational autoencoder (VAE)-based mechanism that obtains the sensing information leveraging AoS, which is used for the location update. Subsequently, a deep deterministic policy gradient-based deep reinforcement learning scheme is devised to allocate the desired power by activating the required grids based on the findings achieved with the VAE-based mechanism. Simulation results demonstrate the superior performance of the proposed AI framework compared to advantage actor-critic and deep Q-network-based methods, achieving a cumulative average SINR improvement of 8.5 dB and 10.27 dB, and a cumulative average achievable rate improvement of 21.59 bps/Hz and 4.22 bps/Hz, respectively.

Convolutional Neural Networks (CNNs) have opened up new possibilities in a variety of fields, including plant pathology. Through image analysis, this study aims to take advantage of CNNs' capacity to recognize and categorize plant illnesses. Automated disease identification has potential for prompt intervention and crop preservation in modern times, when agricultural output assumes critical relevance. This work elaborates on the application and assessment of a CNN-based model designed for the detection of plant diseases. The technology separates healthy plants from those with problems by taking pictures of plants and putting them through the trained model. The growing demand for effective disease monitoring and management in the agriculture industry highlights the urgency of this research. The suggested model shows promising accuracy and dependability by utilising a large dataset and stringent evaluation metrics. This research highlights the potential of CNNs as a workable tool for upgrading agricultural practices by using a systematic approach. The results of this study shed light on both the strengths and weaknesses of CNNs in the context of automated plant disease diagnosis.

Despite the impressive performance of current deep learning models in the field of medical imaging, the transfer of the lung segmentation task in X-ray images to clinical practice is still a pending task. In this study, we explored the performance of a fully automatic framework for lung fields segmentation in chest X-ray images, based on the combination of the Segment Anything Model (SAM) with prompt capabilities, and the You Only Look Once (YOLO) model to provide effective prompts. Transfer learning, loss functions and several validation strategies were intensively assessed. This provides a complete benchmark that enables future research studies to fairly compare new segmentation strategies. The results achieved demonstrate significant robustness and generalization capability against the variability in sensors, populations, disease manifestations, device processing and imaging conditions. The proposed framework is computationally efficient, can address bias in training over multiple datasets, and has the potential to be applied across other domains and modalities.

In this paper, a new method based on machine learning methods is used to find prime numbers. In this way, by training a regression model using the sum of the previous prime numbers, the next prime number can be calculated with a very small error. Even the next one hundred prime numbers can be guessed. The error of this calculation is very small and this error is alternating (sine or cosine) and it becomes zero in some places. Therefore, it can be said that due to zero error, the next prime number must exist and this theorem proves the infinity of prime numbers.

In situations where visible lighting is inadequate for sensing, infrared sensors are commonly employed. However, they often yield blurry images lacking clear textures and terrain/object boundaries. Unfortunately, human visibility diminishes even with infrared sensors providing more visual information than visible sensors, especially at night aerial imagery. To enhance the visibility of aerial infrared images, we propose adopting semantic segmentation, which assigns pixel-wise class labels to various input images, thereby clarifying substantial boundaries. However, training an accurate semantic segmentation model necessitates extensive pixel-wise annotations corresponding to input images, which are lacking in aerial infrared images with ground truth datasets. To address this challenge, we introduce a novel method that automatically generates pixel-wise class labels using solely infrared images and metadata such as GPS coordinates. Our method comprises two pivotal functions: coarse alignment with metadata in geographic information system (GIS) space and fine alignment based on multimodal image registration between aerial images. Aerial image datasets spanning three domains-day, twilight, and night-were created using shortwave infrared (SWIR) and mid-wave infrared (MWIR) images captured by optical sensors mounted on helicopters. Experimental results demonstrate that training on GIS data as label images enables high-precision semantic segmentation across both daytime and nighttime conditions.

The development of emerging photovoltaic technology has promoted the innovation of building-integrated photovoltaics (BIPV), not only in lower cost and simpler processing technology but also in a variety of additional features, such as flexibility and transparency. Semi-transparent solar cells that allow partial transmission of visible light are excellent candidates for BIPVs owing to their unique properties and potential for integrated energy solutions. In this work, we present a straightforward and highly reproducible protocol for depositing extremely uniform and ultra-thin perovskite layers. Our solution-processed perovskite solar cells, fabricated on flexible polymer 2 substrates with large active area (1 cm2), achieved a noteworthy 5.7% power conversion efficiency (PCE) under standard conditions (AM 1.5G radiation, 100 mW cm-2) accompanied by an Average Visible Transmittance (AVT) of 21.5% for full device architecture with 10 nm thick silver electrode. We present a simple yet elegant fabrication procedure for semi-transparent perovskite solar cells without any additional antireflective layers. Furthermore, we fabricated working perovskite solar cells with the thinnest active layer of spin-coated MAPbI3 reported so far (10 nm) exhibiting 1.9% PCE and 41.1% AVT (62.9% AVT without electrode). These results hold great promise for the integration of perovskite-based semi-transparent solar cells into real-world applications, advancing the landscape of renewable energy.

1) Contribution: Course aimed at addressing the documented enculturation process in engineering education, which leads to a cognitive dissonance in students between technical and social dimensions, immobilizing the latter. From a learning design perspective, resolving this cognitive dissonance requires the development of learning strategies targeted at achieving a conceptual change. We argue that the minimum needed prior knowledge for this change to happen is already present in students. 2) Background: Engineering education does not happen in isolated but social contexts and is, subsequently, inherently sociotechnical. Despite the growing recognition of the necessity to promote a broader comprehension of engineering identities, there is a disparity in the way curricula are effectively designed and implemented, if they are. The intricate nature of ethics within engineering, encompassing individual, institutional, and cultural dimensions, requires approaches that are both transformative and feasible. 3) Intended Outcomes: The course was designed following the ICAP (Interactive, Constructive, Active, and Passive) framework on cognitive engagement with a focus on mobilizing existing prior knowledge in both technical and social realms, and assessing its impact on students' approach to learning and their identity as engineers. 4) Application Design: The examination of pre-and post-learning tasks embedded in the learning design indicates that the deliberate activation of students' pre-existing knowledge in ethical, political, cultural, and social domains enhances their awareness of their cognitive dissonance. 5) Findings: The findings suggest that the course activates students' pre-existing knowledge, fostering awareness of cognitive dissonance, and encouraging purposeful engagement with broader knowledge when tackling engineering challenges.

Relation classification (RC) is a fundamental task in knowledge discovery. Prototypical networks are commonly used for few-shot RC tasks to tackle labeled data scarcity and long-tail relations, but they always overlook classification reliability and outlier features, potentially leading to sub-optimal similarity measurement. In a dynamic world with emerging novel semantic relations, incremental few-shot learning of relations gains attention, involving learning of both base and novel relations in new tasks while retaining base relation knowledge. However, this becomes challenging with increasing novel relations, requiring that models should reduce reliance on learning base relations for the new task. Therefore, this paper explores class-incremental few-shot relation classification (CIFRC) using a Gaussian-augmented prototypical network (GA-Proto). GA-Proto refines the similarity measurement by incorporating discrepancies between ideal and actual classifications via Gaussian Mixture Model and analyzing Gaussian outlier features of query instances. It also employs reliability learning and knowledge distillation to mitigate encoding space distortion and base relation forgetting by enhancing classification reliability and transferring base relation knowledge, respectively. Additionally, GA-Proto uses label smoothing to alleviate novel relation overfitting. Experimental results on three public datasets demonstrate that GA-Proto outperforms existing methods on CIFRC, achieving up to 19.64% improvement in accuracy.

The paper focuses on the multi-azimuth interpolation task of inverse synthetic aperture radar (ISAR) images for aircraft targets and complements incomplete ISAR image datasets. ISAR image automatic target recognition (ATR) has been widely applied in remote sensing and many fields. However, the imaging process is more challenging when compared to capturing optical and SAR image data, which reduces the accuracy and generalization performance of the ATR system. Therefore, in this paper, we leverage existing limited ISAR data to achieve autonomous data expansion. This approach helps mitigate the impact of low sample quantity and unbalanced distribution, ultimately improving the accuracy of the ATR system for target recognition. Most existing methods use generative networks for ISAR image expansion, but few focus on generating ISAR images with specific azimuths. The paper proposes a novel two-stage coarse-to-fine framework for ISAR object view interpolation (C2FIPNet) that combines flow estimation and GAN to interpolate ISAR images with intermediate azimuths using a set of ISAR image pairs. Flow estimation is employed for coarsegrained generation, determining the position and intensity of strong scattering points in the ISAR image. The GAN, on the other hand, is used for fine-grained completion to correct image distortion caused by flow estimation and enhance image details. Additionally, a suitable loss function is designed, incorporating both global and local features, allowing for priority generation in the region of strong scattering points. In conclusion, extensive simulation and comparative experiments have demonstrated that the interpolated ISAR images generated by the proposed C2FIPNet exhibit greater pixel-level authenticity.

A document by Luke Byrne. Click on the document to view its contents.

This article explored the spiral development process, sometimes called "evolutionary acquisition" in military sectors, and reviewed major non-software applications in the engineering literature. The spiral development process was originally developed for the design of software while minimizing and managing risk, but the principles can be applied to a wide variety of systems engineering problems where risk management is a priority. The major application domains discussed in this review were product design and development, robotics, agriculture and construction systems, product-service and human-technology systems, medical systems and devices, military and aerospace systems, and data management, enterprise systems, and information technology systems. This exploration and accompanying discussion is useful for system designers, systems engineering educators, and other major stakeholders as it shows successful applications in a wide variety of non-software technology sectors and provides guidance for application in new areas. Several new and expanded application sectors were identified during the review.

This study aimed to develop a novel shared control framework for robotic prosthetic hands, consisting of an electromyography (EMG)-based decoder of hand motion and grip force and an autonomous grip force controller for slip prevention. Mimicking human hand control mechanisms during grasping, our framework uses EMG-based neural control to drive hand motion before grasping and estimate the initial contact force for grip force control, and applies autonomous reflexive grip force control during grasping. To evaluate the ability of the autonomous grip force controller to stop object slip, we first designed a custom test bench to accelerate a prosthetic hand vertically while grasping objects to simulate object slippage. Then, a human subject was asked to complete grasping tasks using both the proposed shared control framework and a grip force controller based on EMG input only for comparison. We found that when using the shared control framework, the subject achieved better task performance with lower physical effort. The proposed shared control framework may further improve the function and usability of prosthetic hands for performing activities of daily living.

Understanding and optimizing the behavior of artificial magnetic conductors require modeling them through analytical means, which can be challenging due to their complex structures. In this paper, a triple-band artificial magnetic conductor is presented, exhibiting an in-phase reflection of incident waves at 1.21 GHz, 1.61 GHz, and 2.46 GHz. The design is based on a triple square loop structure, where each frequency is controlled by one square loop. Additionally, an analytical model is proposed to predict the operating frequencies of the artificial magnetic conductor.

In the realm of Internet of Things (IoT) security, the Physical Unclonable Function (PUF) emerges as a viable solution for generating individualized secure keys. These keys play a vital role in authentication, chip protection, and supply chain security, serving as crucial safeguards for resource-constrained IoT devices. This work introduces Boosted Configurable Ring Oscillator PUF (BC-PUF), an area-efficient variant of CROPUF that suits resource-constrained IoT devices. Additionally, BC-PUF employs an absorbent approach for the intermediate responses to optimize the utilization of CMOS delay configurations. Moreover, BC-PUF mitigates the risk of Machine Learning (ML)-based modeling attacks. BC-PUF is a multi-bit response architecture that has been developed, analyzed, and evaluated across 50 FPGAs, from each dataset of 10K Challenge-Response Pairs (CRPs) is extracted. Experimental results report average values of 50.2%, 40.4%, 42%, 3.925%, and 83% for uniformity, diffuseness, uniqueness, reliability, and Shannon entropy, respectively. In comparison to state-of-the-art designs, the BC-PUF design achieves area reductions ranging from 15% to 96.7% across various architectures and implementations, along with a power reduction of 9.7%. Moreover, BC-PUF demonstrates resilience against ML-based attacks, achieving a prediction accuracy of 51%.

Data mining is an evolutionary process, and data and information are also manifestations of knowledge, but people see concepts, rules, patterns, laws and constraints more as knowledge. Based on data mining, an expert system works by reasoning using expert knowledge, and it consists of knowledge acquisition, knowledge base, reasoning machine, and interpretation part. The knowledge base is used to store experts' experience, knowledge and existing knowledge in the field, and whether it is perfect or not determines the performance of the expert system. Data mining is getting more and more attention, while the existing expert systems or decision support systems still mainly rely on manual collection and manual input of required knowledge and information, which is not only time-consuming and expensive, but also lags behind the time when making time-sensitive decisions and reduces the quality of decision-making.

We present a model and a rigorous method to calculate the transmission coefficient of silicon micro-rings with generic waveguide cross-section including non-linear effects and self-heating. The method is applied to the design of MRRs in the SISCAP platform with high Q and reduced non-linearity. We demonstrate that the free carrier diffusion in rib waveguides and Shockely-Read-Hall recombination play a fundamental role in reducing the impact of non-linearities of the ring.

Safety-critical control of autonomous vehicles requires estimating modeling errors and uncertainties. This can augment the vehicle's ability to execute a safety-necessitated aggressive and evasive maneuver to avoid an accident while the tire forces are pushed toward saturation. This work experimentally validates an input-to-state stable nonlinear observer previously introduced by the authors to estimate errors in modeling dynamics of the bicycle model used for vehicle control. These errors are translated into improved estimates of tire-road cornering forces. Tracing the evolution of these forces against the lateral slip of the bicycle tires provides a qualitative measure of the vehicle's proximity to its limits-ofhandling. The accuracy and robustness of the observer validated using cornering maneuvers in a one-tenth scale car demonstrates the potential of the observer for model correction and timely detection of tire-road force saturation.