As the photovoltaics (PV) industry grows in sophistication, so must the extent to which systems are characterized. UV Fluorescence (UVF) imaging is a valuable, easy-to-perform, high throughput, non-intrusive technique for characterizing modules in the field and in the lab. However, UVF is still a relatively new technique, and many in the PV industry are still unaware of its potential. We provide a guideline for obtaining, processing, and interpreting UVF images. We have provided a list of considerations for imaging hardware and settings, a suggested pipeline for image processing, and details on a survey of features shown in UVF images. A database with UVF images of 7,190 modules is provided in https://osf.io/t8x9k/. A database curated by BrightSpot Automation is available on https://brightspotautomation.com/publications/.

This paper presents an efficient method for enhancing time series load flow analysis, accounting of variabilities inherent to modern power system, especially in presence of renewables, electric vehicles, and storage systems. The method uses a moving block bootstrap technique applied on time series residuals, after Box-Cox transformation and Seasonal-Trend decomposition based on LOESS (STL). The adopted techniques, already known in statistics for improving time series forecasting, can generate new observations of system variables, such as load, generation, or transmission line power flows, by resampling from the STL model residuals of observed time series data. The proposed approach has the advantage of avoiding a full probabilistic analysis, which can be computationally expensive and data-intensive. It can generate distribution functions of electrical parameters at any time point within the observation period, calculate confidence intervals of relevant statistics, and assess probabilities of critical system anomalies (such as overload or voltage violations). These are all essential for strategic planning decisions. The suggested methodology, despite its inherent limitations, can efficiently deal with uncertainties in the context of power system analysis. It proves particularly valuable in cases where traditional probabilistic analysis becomes unfeasible due to inadequate data or computational constraints.

A crucial security feature called multifactor authentication (MFA) is intended to improve the security of sensitive data and digital identities. By employing many levels of authentication to confirm a user's identity, it greatly increases the difficulty for unauthorized users to access systems and data. Recent advancements in machine learning (ML) and artificial intelligence (AI) have strengthened MFA systems and improved their usability and security.

Reviewing software changes is crucial, as it mitigates the introduction of defects and thereby saves time and reduces costs. Just-in-time (JIT) defect prediction has emerged as an approach to support the review process by predicting the likelihood of defects in new commits. Effort-aware evaluations were proposed to better manage developers' limited time to review changes and analyze the applicability of JIT defect prediction approaches. However, current effort-aware approaches neglect the timedependent nature of software engineering, thus overstating the performance and applicability of such approaches. Further, they do not reflect state-of-the-art software development practices. In this work, we discuss these limitations and propose a paradigm shift to evaluate JIT defect prediction more realistically and redirect the focus to saving effort under the condition that defective commits are still reviewed. Thus, we propose performance metrics that better represent applicable JIT defect prediction. We further analyze reliability techniques that adapt the prediction results and allow for a risk-based application of JIT defect prediction models. Taking this new perspective, we find that while still reviewing 95% of defective commits, on average, 46% of the non-defective commits are correctly identified by JIT defect prediction models and can be skipped; therefore, 20% of the total avoidable effort can be saved by employing JIT defect prediction models.

Traditional CAD systems have expanded human capabilities in numerical calculations and graphics processing, but they still have the following shortcomings without intelligent support: Failure to provide effective computer support throughout the design process; Inability to handle fuzzy knowledge or design problems described by insufficient conditions; lack of macroscopic knowledge structure analysis, design thinking principles and specific methods and skills to implement design, processing algorithms are mixed with the environment representing the processing object, which makes it difficult to change with the change of the environment or the replacement of processing technology and make necessary modifications and expansions. Based on the idea of intelligent engineering, this article uses simulated annealing method and genetic algorithm to solve the complex clock and watch movement CAD layout problem. Layout problems widely exist in production fields such as machinery manufacturing, clothing, leather, packaging and printing, glass, urban planning, factory layout, etc. The main manifestations are: Cutting one-dimensional bars into pieces of different lengths workpiece to maximize material utilization, such as the problem of cutting steel pipes according to customer orders.

Medical Visual Question Answering (VQA-Med) is a challenging task that involves answering clinical questions related to medical images. However, most current VQA-Med methods ignore the causal correlation between specific lesion or abnormality features and answers, while also failing to provide accurate explanations for their decisions. Moreover, VQA-Med methods suffer from the common language bias problem in generic VQA. To explore the interpretability and language bias of VQA-Med, this paper proposes a novel CCIS-MVQA model for VQA-Med based on a counterfactual causal-effect intervention strategy. This model consists of the modified ResNet for image feature extraction, a GloVe decoder for question feature extraction, a bilinear attention network for vision and language feature fusion, and an interpretability generator for producing the interpretability and prediction results. The proposed CCIS-MVQA introduces a layer-wise relevance propagation method to automatically generate counterfactual samples for improving interpretability and alleviating language bias. Additionally, CCIS-MVQA applies counterfactual causal reasoning throughout the training phase to enhance interpretability and generalization. Extensive experiments on three benchmark datasets show that the proposed CCIS-MVQA model outperforms the state-of-the-art methods. Enough visualization results are produced to analyze the interpretability and debasing performance of CCIS-MVQA.

This paper presents the tunable and switchable Perfect Absorbers (PAs), operating within the mid-infrared (mid-IR) spectrum, specifically targeting the 3 to 5 µm range with precise 0.25 µm intervals. This spectrum is particularly engineered for its minimal atmospheric absorption and unique atmospheric transmission characteristics. Our approach utilizes graphene-based nanophotonic aperiodic multilayer structures optimized through the synergy of micro-genetic optimization algorithm (GOA) within an inverse design framework. This strategic combination enables a predictive model-based strategy that broadens the design of space exploration, facilitating the discovery of PAs with highly accurate absorption control. Employing the Transfer-Matrix-Method (TMM) method for simulations, we manipulate the absorption characteristics, allowing for the precise tailoring of the desired spectral response while maintaining the multilayer structures' thickness under 2 µm. Our results demonstrate the tunability and switchability of PAs by adjusting graphene layers' chemical potentials, highlighting their dynamic optical behavior. For instance, a PA optimized for a 4 µm absorption peak can shift its absorption peak to 4.22 µm by merely changing the graphene layer's chemical potential from 0 eV to 1 eV, without compromising absorption efficiency. Additionally, our research uncovers the proposed absorbers' remarkable adaptability to various incident angles, maintaining 90% absorption up to 52 degrees. This adaptability demonstrates the versatility and robustness of our design across a broad spectrum of real-world applications, including thermal photovoltaics, sensors, and stealth technology, where angular independence significantly enhances device performance and efficiency. This research not only deepens the understanding of nanophotonic materials' capabilities but also paves the way for the design and development of highly efficient optical devices tailored for the mid-IR range.

Outphasing amplifiers (OPAs) have attracted attention as amplifiers that achieve high efficiency from saturated output to high backoff output. Mixed-mode Symmetric Outphasing Amplifiers (MSOPA), which change amplitude as well as phase below high backoff, are also attracting attention. Conventionally, an outphasing amplifier consists of two amplifiers with the same saturated output power and a Chireix power combiner. In this paper, we propose a mixed-mode asymmetric outphasing amplifier (MAOPA) using two amplifiers with different saturated output power to achieve higher efficiency at backoff output. The design of the output matching circuit for MAOPA requires an output port for each amplifier at two power conditions, i.e., four matching conditions must be realized simultaneously. However, the four matching conditions interfere with each other, making circuit design and evaluation difficult. Therefore, this paper proposes a design method for MAOPA design in which the four matching conditions can be adjusted independently. The proposed method first derives an analytical solution to match optimal loads under two power conditions using the invariants of a lossless matching circuit. Based on this analytical solution, impedance transformation to resistive loads done by matching circuits using offset lines enables the design of each amplifier and power combiner, to be done independently. MSOPA and MAOPA show that the dynamic ranges of the drain efficiency above 50% are 9dB, 12dB, respectively.

Unlike the universal approximation theorems for functions mapping from a real-valued (RV) vector to a RV number or from a complex-valued (CV) vector to a CV number, in the field of electromagnetism, we need to approximate functions mapping from a RV vector to a CV number when we consider the electric field as a function of the spatial coordinate in the frequency domain. Typically, CV numbers contain phase information. When such phase information is handled properly, the performance of the neural networks (NNs) can be improved. This work derives a universal approximation theorem for functions mapping from a RV vector to a CV number. A deep NN, named as HV-DL, is designed accordingly, which consists of a RV input layer, a RV module containing two branches, a CV module and a CV output layer. The proposed universal approximation theorem is verified by numerical experiments on the HV-DL solution of the two-dimensional (2-D) electric field integral equation (EFIE). To integrate the underlying physics of electromagnetic scattering into the proposed HV-DL, the loss function is computed according to the EFIE.

This article combines a holistic look at needed developments toward autonomous robotic-assisted surgeries combined with a presentation on a novel sensor and analysis technology using vibroacoustic audio signals as a tool for guiding, sensing, and supporting technology.Healthcare will experience many changes in the next 10 years. Intentional technology-based disruptions will have an effect on actual delivery but also come with novel health business models. This article will initially present some of the upcoming developments and subsequently focus on the use of artificial intelligence (AI) and intelligent sensing for application in robotic assisted surgery (RAS).The combination of these technologies will lead to dedicated RASs in the near future with reduced cost and complexity and higher precision and prediction capabilities.While the procedures have significant advantages for the patient, they also have problems, as the navigation/guidance of the devices to a target location is based on either preoperatively acquired images and then performed freehand or accompanied by intraoperative imaging, such as MRI or CT, which is expensive and complicated and can produce artifacts or video-based technologies that only allow direct visualization. Using robotic systems for moving and guiding these interventional and therapeutic devices adds additional issues, such as a lack of palpation sensation and/or tissue feedback. While it is possible to add sensors to the distal tip, this creates other obstacles concerning reduced functionality, need for cables, sterility issues and added complexity and cost.We propose the use of a proximally attached audio sensor to record tissue tool interactions and provide real-time feedback to clinicians. This paper reports initial attempts to use this technology with robotic arms for surface characterization and interventional vascular procedures, which have recently gained increased attention in combination with robotic devices.Advanced sensors and AI will be powerful combinations not only for guiding and supporting robotic procedures but also for enabling surgical outcome prediction, which could theoretically increase the safety of RASs.

Lunar exploration has attracted considerable attention, with the lunar poles emerging as the next exploration hot spot for the cold trapping of volatiles in the permanently shadowed regions (PSRs) at these poles. Remote sensing via the satellite’s optical load is one of the most important ways to get the scientific data of PSRs. However, the illumination conditions at the lunar poles are quite different from the low latitude areas and how to get appropriate optical signal remains challenging. Thus, simulation of the optical remote sensing process, which provides reference for the choice of satellites’ imaging parameters to ensure the implementation of lunar exploration project, is of great value. In this paper, an optical imaging chain modeling for the PSRs at the lunar south pole, which includes lunar three dimensional topography, observing satellite’s orbit, instrument’s parameters and other environmental parameters, has been built. To demonstrate the physical accuracy, some PSRs’ observations acquired by narrow angle cameras (NACs) equipped on the Lunar Reconnaissance Orbiter (LRO) are compared with the corresponding images simulated by the proposed imaging chain model. The digital value’s difference between the simulated images and real captured images is generally less than 50 for 12-bit images ranging from 0 to 4095, indicating a good fit considering the uncertainty of soil’s absolute reflectance and the noise in the real captured images. In addition, the impact of the imaging chain’s parameters is revealed with the proposed algorithm. The simulation method will provide reference and assist future optical imaging of PSRs.  

A document by Jeongjin Shin. Click on the document to view its contents.

Falls can lead to serious consequences such as bone fractures or even death. Various fall protection devices such as mobile walkers, wearable airbags and lower-limb wearable robots have been developed to provide balance support or mitigate the damage from a fall. While these devices have shown promising results, critical drawbacks still exist. Most importantly, current devices can only mitigate the damage to a fall upon occurrence, but cannot take proactive action to prevent it. In this paper, we report on the design and evaluation of a novel fall prevention device using two compliant continuum robots. These two robots, which are pneumatically actuated, will be mounted to the back of a human user and deployed to grasp nearby static objects in the early stages of a fall, detected using onboard sensors. Preliminary fall experiments on a mannequin in forward, backward, and lateral directions demonstrate the efficacy of the proposed design.

Geodetic analysis of postseismic responses to major earthquakes offers insight into potential subsequent seismic activities and aseismic strain release. Interferometric Synthetic Aperture Radar (InSAR) provides a particularly high-resolution imaging capability for such transient events, particularly in regions without dense Global Navigation Satellite Systems (GNSS) networks. However, InSAR suffers from decorrelation errors and atmospheric noise, which can distort the interpretation of deformation patterns. To take a step towards addressing these challenges, this study introduces an advanced InSAR processing workflow and applies it to Sentinel-1 observations of postseismic deformation following the 2021 Haiti earthquake. We address decorrelation errors by employing phase linking through the Fine Resolution InSAR with Generalized Eigenvectors (FRInGE) method. We compare three methods for mitigating atmospheric effects, including a grouped Independent Component Analysis (ICA) method, and find that ICA performs best in removing atmosphere. Without applying these methods most of the signal is lost or hidden in the noise; after processing transient postseismic deformation, likely related to shallow fault creep, can be observed over ~3 months following the earthquake on the eastern EPGFZ. We compare the estimated cumulative slip to that obtained from ALOS-2 observations and find a good match, with ~3 cm of differential displacement on either side of the EPGFZ east of the rupture area. Our workflow provides a method for more precise characterization of localized transient deformation signals using C-band InSAR.

In photonic circuits and optical communications links, the electro-optic modulator (EOM) can be considered the heart of the entire system, allowing analog and digital data in the RF or microwave domain to be modulated onto an optical carrier for transmission over low-loss, high-bandwidth optical links. EOMs can be fabricated in a number of material systems and operated in various modes that optimize some measure of performance depending on the application. One common configuration is the push-pull configuration, where a phase modulator in each arm of a Mach-Zehnder interferometer is modulated with equal amplitude but opposite phase. The resulting optical fields of the EOM, and most importantly, the photodetected currents generated from those fields, are well-characterized in the literature. Here, we explicitly demonstrate how the complex optical fields, represented by a sinusoidal series with Bessel function amplitudes, are combined by the detector nonlinearity into RF and microwave currents, also represented by a sinusoidal series with Bessel function amplitudes, and extracted at the end of the optical link.

This article proposes a 13.5-volt, 450-watt outerrotor brushless DC (BLDC) motor for automotive radiator cooling fan applications. The design and modeling of the proposed BLDC motor are presented in some detail. Dynamic-transient analysis of the proposed motor is performed through finite element method (FEM) simulations using the ANSYS MAXWELL software. An integrated sensorless drive system is designed for the proposed motor which uses the phase voltage zero-crossing to detect the rotor position using the virtual neutral point scheme. The motor drive system simulation is performed through the MULTISIM software and the system's performance is evaluated. Thermal analysis of the drive system is essential due to the strict automotive considerations. In this regard, the thermal analysis of the integrated drive system is performed through the 3D FEM simulations using the SIEMENS FLOTHERM software. In order to validate the simulations, a prototype of the proposed motor in compliance with all automotive considerations is manufactured and some experimental tests including the functional and windtunnel tests are applied to evaluate the design accuracy. The results show that the proposed motor can be a good option for automotive fan applications.

5G positioning is an essential part of many applications, such as driverless vehicles, drone tracking, emergency services, location-based services, etc. Such positioning can attain an accuracy on the order of 1 meter or even lower in both indoor and outdoor settings due to its several advantageous aspects, which include millimeter wave signal, multiple input multiple output antennas, device-to-device communication, etc. Hence, many researchers have paid attention to the development of 5G positioning systems over the past decade. This paper, therefore, presents a state-of-the-art review of the existing 5G positioning methods along with their taxonomy and comparative analysis in several metrics including their merits and demerits. Moreover, the architecture and advantageous aspects of 5G positioning are also presented in this paper. Similar to the satellite signals, 5G signals, however, can be impeded by various obstructions, further declining the accuracy of such positioning. Moreover, many security concerns and privacy issues are also associated with 5G positioning. Thus, the development of a precise and secure 5G positioning system faces a number of challenges. Therefore, various ways to improve the 5G positioning accuracy and several solutions to address the security concerns and privacy issues of such positioning are pointed out in this study.

In today’s fast-paced business environment, the abundance of data is both a boon and a challenge for companies. The sheer volume of information generated internally and acquired from vendors is immense, and technologies like Tableau have revolutionized data analytics and visualizations. However, the key to success lies in delivering the right information to the sales team promptly, enabling them to make informed decisions in real time. This article explores how harnessing Tableau subscriptions, user filters, and machine learning-driven recommendations can efficiently provide real-time, personalized insights directly to sales teams via email and phone. The goal is to transform the conventional approach, where end-users actively seek out information, into a proactive model where insights are delivered seamlessly, optimizing the efficiency of the sales team. The traditional paradigm often sees sales professionals spending valuable time searching for relevant insights instead of focusing on selling. By proactively delivering hidden trends and actionable insights, businesses can enhance the productivity of their sales teams, allowing them to allocate more time to revenue-generating activities. One of the key features of this approach is its emphasis on user-friendly tools like Tableau, demonstrating that Next Best Action can be seamlessly integrated within the Tableau ecosystem without the need for additional tools such as CRM systems. This not only streamlines the workflow for sales teams but also highlights the versatility and power of Tableau as an all-encompassing analytics platform. Furthermore, the solution presented in this article leverages the convenience of delivering insights directly to sales representatives through email and text messages. This eliminates the need for complex system logins, making the process more accessible and user-friendly. By providing insights directly to the fingertips of sales teams, this approach ensures that critical information is readily available, facilitating quick decision-making and boosting overall engagement.