Ubiquitous Applications Research Articles

Towards the Future of Ubiquitous Hyperspectral Imaging: Innovations in Sensor Configurations and Cost Reduction for Widespread Applicability

Hyperspectral imaging is currently under active development as a method for remote sensing, environmental monitoring and biomedical diagnostics. The development of hyperspectral sensors is aimed at their miniaturization and reducing the cost of components for the purpose of the widespread use of such devices on unmanned aerial vehicles and satellites. In this review, we present a broad overview of recent work on the development of hyperspectral devices’ configurations, studies aimed at modifying sensors and the possibility of reducing the cost of components of such devices. In addition, we will present the main trends in the development of hyperspectral device configurations for ubiquitous applications.

The role of telecommunications in enabling Internet of Things (IoT) connectivity and applications

This review paper explores the pivotal role of telecommunications in enabling Internet of Things (IoT) connectivity and applications, providing a comprehensive overview of the current landscape and future prospects. The primary objective is to synthesize existing research and industry insights to highlight how advancements in telecommunications infrastructure and technology have catalyzed the adoption and expansion of IoT. The review systematically examines the evolution of telecommunications technologies, including 4G, 5G, and emerging network protocols such as Narrowband IoT (NB-IoT) and Long-Term Evolution for Machines (LTE-M). It assesses how these technologies enhance IoT connectivity by providing the necessary bandwidth, low latency, and reliability for real-time data exchange and device interoperability. Key findings from the literature reveal that the deployment of 5G networks has been a game-changer for IoT, offering unprecedented speeds and connectivity capabilities that support a wide range of applications in smart cities, healthcare, agriculture, and industrial automation. Additionally, the paper discusses the role of telecommunications in addressing challenges related to security, scalability, and energy efficiency in IoT deployments. The review concludes that telecommunications are integral to the growth and sustainability of IoT ecosystems. As telecommunications technology continues to advance, its ability to support increasingly complex and ubiquitous IoT applications will drive innovation and enhance efficiency across various sectors. The paper emphasizes the necessity for ongoing investment in telecommunications infrastructure and research to fully exploit the transformative potential of IoT connectivity and applications.

PhotoSolver: A bidirectional photonic solver for systems of linear equations

Solving systems of linear equations is one of the most fundamental challenges in computing, with ubiquitous applications in science and engineering. Solving large-scale linear equations is particularly challenging, requiring tremendous computing expenses. However, conventional solvers in electronic digital computers face inevitable physical bottlenecks that hinder further advancements in electronic chips. Unlike the electronic architectures, optical computing harnesses the inherent advantages of lights, such as ultimately high speed, negligible energy consumption, and high parallelism, making optical architectures attractive sources for ultra-high speed analog computing. Especially, recent advances in integrated photonic circuits bring exciting new possibilities for high performance computing based on photonic chips. In this work, we propose a bidirectional PhotoSolver using the propagation of optical signals in different directions on a single integrated photonic chip to perform multiple operations in solving systems of linear equations, scalable to solve large-scale systems. Specifically, we propose an in situ approach to iteratively update solutions by performing the forward and backward propagations within a single photonic chip. Furthermore, we propose a partitioning approach to solve large-scale systems using arrays of photonic chips. Numerical experiments demonstrate these capabilities with representative systems of linear equations. By utilizing lights propagating in integrated photonic chips, our PhotoSolvers provide a promising computing architecture to solve large systems of linear equations, with potentials in wide computational applications.

PrivateGaze: Preserving User Privacy in Black-box Mobile Gaze Tracking Services

Eye gaze contains rich information about human attention and cognitive processes. This capability makes the underlying technology, known as gaze tracking, a critical enabler for many ubiquitous applications and has triggered the development of easy-to-use gaze estimation services. Indeed, by utilizing the ubiquitous cameras on tablets and smartphones, users can readily access many gaze estimation services. In using these services, users must provide their full-face images to the gaze estimator, which is often a black box. This poses significant privacy threats to the users, especially when a malicious service provider gathers a large collection of face images to classify sensitive user attributes. In this work, we present PrivateGaze, the first approach that can effectively preserve users' privacy in black-box gaze tracking services without compromising gaze estimation performance. Specifically, we proposed a novel framework to train a privacy preserver that converts full-face images into obfuscated counterparts, which are effective for gaze estimation while containing no privacy information. Evaluation on four datasets shows that the obfuscated image can protect users' private information, such as identity and gender, against unauthorized attribute classification. Meanwhile, when used directly by the black-box gaze estimator as inputs, the obfuscated images lead to comparable tracking performance to the conventional, unprotected full-face images.

A shipboard integrated navigation algorithm based on smartphone built-in GNSS/IMU/MAG sensors

The rapid development of built-in sensors in smartphones has inspired a variety of ubiquitous public navigation applications. Nevertheless, previous works have focused on vehicle and pedestrian navigation by smartphones, with limited research on shipboard navigation. In this contribution, we designed a multi-sensors fusion navigation algorithm that integrates the global navigation satellite systems (GNSS), inertial measurement unit (IMU), and magnetometer (MAG) to achieve high-precision horizontal positioning on shipboard for smartphones. Considering the changeable motion state of a ship and the setting of non-holonomic constraint (NHC) noise, a magnetometer-based turning detection method is proposed, and a NHC model with adaptive noise is constructed based on the turning detection results. We conducted a lake experiment in Taku Forts Lake, Tianjin, using sensor data collected from a Xiaomi MI8 through our self-developed ’Sensor Logger’ app. The experimental results demonstrated a high success rate of 94.22 % for magnetometer-based turning detection which can effectively detect turning motions. Compared to the traditional algorithm without constraint information, the roll, pitch and yaw accuracy improve by approximately 34.7 %, 17.9 %, and 57.9 %, respectively. Additionally, the position and velocity accuracy in the east and north directions improve to 0.0926 m, 0.1779 m, 0.0548 m/s, and 0.0589 m/s, respectively, representing enhancements of about 10.7 %, 7.4 %, 10.5 %, and 6.4 %. Overall, the proposed algorithm efficiently improves the shipboard navigation accuracy of smartphones, especially in terms of attitude accuracy.

Effect of phosphate nature on the inhibitory capability of phosphate compositions

Corrosion inhibitors find ubiquitous application across diverse sectors to formulate robust coatings and chemical compounds. Serving as pivotal additives in compositions, they play a vital role in crafting coatings for circulating water systems, water supply networks, oil refining, petrochemical industries, and power plants. Incorporation extends to fuels, oils, lubricants, and construction materials. In industries oxidation inhibitors is particularly high as chemical, electrochemical, petrochemical, and gas, as well as in water supply networks and circulating water systems. The utilization of inhibitors obviates the necessity for treating metal surfaces with protective enamels and mastics. Effectively preventing oxidation involves the introduction of chemical contributions into aggressive ecology, thereby neutralizing or retarding the oxidation processes. This article explores the anti-oxidation properties of inhibitors in compositions related to St-3 steel. The study includes factors such as the pH of the medium, the nature and concentration of phosphate in compositions simulating reservoir water under stationary and aerated conditions. Investigation methods as potentiometry and photocolorimetry used in research. Experimental data, quantitative parameters of the oxidation process, oxidation rate, and degree of protection, depth index, and stability of the film formed were determined. These indicators were assessed on the oxidation resistance scale relative to steel. Analysis of the data shows that out of 63 compositions, seven systems demonstrate the most significant protective effects, with five containing sodium orthophosphate and two containing sodium hydroorthophosphate. The effective, surrounding friendly and cost-effective steel oxidation inhibitors using materials from Kazakhstan will contribute to the development of industry,

Student's Behavior Clustering based on Ubiquitous Learning Log Data using Unsupervised Machine Learning

Online learning is the source of data generation related to learner's learning behaviors, which is valuable for knowledge discovery. Existing research emphasized more on an understanding of student's performance and achievement from learning log data. In this study, we presented data-driven learning behavior clustering in authentic learning context to understand students' behavior while participating in the learning process. The objective of the study is to distinguish students according to their learning behavior characteristics and identify clusters of students at risk of unsuccessful learning achievement. Learning log data were collected from ubiquitous learning applications before conducting Exploratory Data Analysis (EDA) and cluster analysis. We used partitional clustering using K-means algorithm and hierarchical clustering based on the agglomerative method to improve clustering strategies. The result of this study revealed three different clusters of students supported by data visualization techniques. Cluster 1 comprised more students with active learning behavior based on the total logs, total problems posed, and the total attempts in fraction operation and simplification. Students in clusters 2 and 3 had a higher attempt at problem-solving instead of problem-posing. Both clusters also focused on fraction's conceptual understanding. Knowledge discovery of this study used real data generated from ubiquitous learning application namely U-Fraction. We combined two different types of clustering method for delivering more accurate portrait of a student's hidden learning behaviors. The outcome of this study can be a basis for educational stakeholders to provide preventive learning strategies tailored to a different cluster of students.

Lightweight polyimide/silica aerogel composite foams and their derived carbon foams for excellent flame retardancy, thermal insulation and EMI shielding performance

Multifunctional porous materials demonstrate ubiquitous applications in the fields of flame retardancy, thermal insulation and electromagnetic interference (EMI) shielding. In this work, a series of lightweight polyimide/silica aerogel (PIF/SiA) foams with exceptional thermal stability, flame retardancy and thermal insulation performance were prepared by employing microwave-assisted foaming and thermal imidization treatment, using polyester ammonium salt/silica aerogel (PEAS/SiA) powders as the derivative precursors. The limiting oxygen index and fire growth rate reached as high as 58.2 % and as low as 0.1 kW/(m2·s) for PIF/SiA composite foams, respectively, which was a result of the formation of micro/nano multi-scale pore structure of PIF skeleton and the “pearl-necklace-like” skeletal network of nano SiA. In addition, the PIF/SiA composite foams exhibited excellent thermal insulation and infrared stealth performance, which demonstrate a potential for long service life in fire protection use. Furthermore, the carbonized PIF/SiA (i.e., PICF/SiA) foams possessed enhanced mechanical (improvement of 117.6 %) and EMI shielding performance (specific EMI shielding effectiveness: 8541.9 dB/(g/cm2)) when compared with the original PIF/SiA composite foams. To sum up, a facile method was proposed to fabricate multifunctional PIF-based composite foams and their derived carbon foams for flame retardancy, thermal insulation and EMI shielding applications in high-end engineering sectors.

Essay: Quantum Sensing with Atomic, Molecular, and Optical Platforms for Fundamental Physics.

Atomic, molecular, and optical (AMO) physics has been at the forefront of the development of quantum science while laying the foundation for modern technology. With the growing capabilities of quantum control of many atoms for engineered many-body states and quantum entanglement, a key question emerges: what critical impact will the second quantum revolution with ubiquitous applications of entanglement bring to bear on fundamental physics? In this Essay, we argue that a compelling long-term vision for fundamental physics and novel applications is to harness the rapid development of quantum information science to define and advance the frontiers of measurement physics, with strong potential for fundamental discoveries. As quantum technologies, such as fault-tolerant quantum computing and entangled quantum sensor networks, become much more advanced than today's realization, we wonder what doors of basic science can these tools unlock. We anticipate that some of the most intriguing and challenging problems, such as quantum aspects of gravity, fundamental symmetries, or new physics beyond the minimal standard model, will be tackled at the emerging quantum measurement frontier. Part of a series of Essays which concisely present author visions for the future of their field.

Insights into pollution characteristics and human health risks of plasticizer phthalate esters in shellfish species

The ubiquitous application of phthalate esters (PAEs) as plasticizers contributes to high levels of marine pollution, yet the contamination patterns of PAEs in various shellfish species remain unknown. The objective of this research is to provide the first information on the pollution characteristics of 16 PAEs in different shellfish species from the Pearl River Delta (PRD), South China, and associated health risks. Among the 16 analyzed PAEs, 13 were identified in the shellfish, with total PAE concentrations ranging from 23.07 to 3794.08 ng/g dw (mean = 514.35 ng/g dw). The PAE pollution levels in the five shellfish species were as follows: Ostreidae (mean = 1064.12 ng/g dw) > Mytilus edulis (mean = 509.88 ng/g dw) > Babylonia areolate (mean = 458.14 ng/g dw) > Mactra chinensis (mean = 378.90 ng/g dw) > Haliotis diversicolor (mean = 335.28 ng/g dw). Dimethyl phthalate (DMP, mean = 69.85 ng/g dw), diisobutyl phthalate (DIBP, mean = 41.39 ng/g dw), dibutyl phthalate (DBP, mean = 130.91 ng/g dw), and di(2-ethylhexyl) phthalate (DEHP, mean = 226.23 ng/g dw) were the most abundant congeners. Notably, DEHP constituted the most predominant fraction (43.98 %) of the 13 PAEs detected in all shellfish from the PRD. Principal component analysis indicated that industrial and domestic emissions served as main sources for the PAE pollution in shellfish from the PRD. It was estimated that the daily intake of PAEs via shellfish consumption among adults and children ranged from 0.004 to 1.27 μg/kgbw/day, without obvious non-cancer risks (< 0.034), but the cancer risks raised some alarm (2.0 × 10−9–1.4 × 10−5). These findings highlight the necessity of focusing on marine environmental pollutants and emphasize the importance of ongoing monitoring of PAE contamination in seafood.

Mass Spectrometry-Based Analyses of Carbon Nanodots: Structural Elucidation.

Carbon nanodots (CNDs) are nanomaterials with ubiquitous applications in health for diagnosis and treatments. The key to enhancing the applications of carbon nanodots in various fields lies on how deep its structure is understood. Here, we review the mass spectroscopy (MS) techniques employed for carbon nanodot analysis. We aimed to revive the use of MS to support the structural elucidation of carbon nanodots. General techniques used in nanomaterials characterization include laser desorption/ionization (LDI), matrix-assisted LDI (MALDI), inductively coupled plasma (ICP), and electrospray ionization (ESI) MS. For CNDs characterization, LDI-MS, MALDI-MS, and ESI-MS were employed. The techniques required further instrumentations of time-of-flight (TOF), for MALDI, and TOF, quadrupole (Q), and tandem (MS/MS) for ESI. LDI-MS could be applied to prove the surface and core structural composition of carbon nanodots. Meanwhile, MALDI-MS was used to elucidate the surface structures of CNDs. Finally, ESI-MS could provide significant insight into the carbon nanodots' structural composition and bonding patterns. In summary, MS could be combined with other techniques to unambiguously elucidate the structure of carbon nanodots.

From programming-to-modeling-to-prompts smart ubiquitous applications

Since its introduction by Mark Weiser, ubiquitous computing has received increased interest in the dawn of technological advancement. Supported by wireless technology advancement, embedded systems, miniaturization, and the integration of various intelligent and communicative devise, context-aware ubiquitous applications actively and intelligently use rich contextual information to assist their users. However, their designs are subject to continuous changes imposed by external factors. Nowadays, software engineering, particularly in the fields of Model-Driven Engineering, displays a strong tendency towards developing applications for pervasive computing. This trend is also fueled by the rise of generative artificial intelligence, paving the way for a new generation of no-code development tools and models specifically trained on open-source code repositories to generate applications from their descriptions. The specificities of our approach lies in starting with a graphical model expressed using a domain-specific language (DSL) composed of symbols and formal notations. This allows for graphically instantiating and editing applications, guiding and assisting experts from various engineering fields in defining ubiquitous applications that are eventually transformed into peculiar models. We believe that creating intelligent models is the best way to promote software development efficiency. We have used and evaluated recurrent neural networks, leveraging the recurrence of processing the same contextual information collected within this model, and enabling iterative adaptation to future evolutions in ubiquitous systems. We propose a prototype instantiated by our meta-model which tracks the movements of individuals who were positive for COVID-19 and confirmed to be contagious. Different deep learning models and classical machine learning techniques are considered and compared for the task of detection/classification of COVID-19. Results obtained from all techniques were evaluated with confusion matrices, accuracy, precision, recall and F1-score. In summary, most of the results are very impressive. Our deep learning approach used a RNN architecture produced up to 92.1% accuracy. With the recent development of OpenAI Codex, optimized for programming languages, we provided the same requirements to the Codex model and asked it to generate the source code for the COVID-19 application, comparing it with the application generated by our workshop.

Tissue mimicking materials and finger phantom design for pulse oximetry.

Pulse oximetry represents a ubiquitous clinical application of optics in modern medicine. Recent studies have raised concerns regarding the potential impact of confounders, such as variable skin pigmentation and perfusion, on blood oxygen saturation measurement accuracy in pulse oximeters. Tissue-mimicking phantom testing offers a low-cost, well-controlled solution for characterizing device performance and studying potential error sources, which may thus reduce the need for costly in vivo trials. The purpose of this study was to develop realistic phantom-based test methods for pulse oximetry. Material optical and mechanical properties were reviewed, selected, and tuned for optimal biological relevance, e.g., oxygenated tissue absorption and scattering, strength, elasticity, hardness, and other parameters representing the human finger's geometry and composition, such as blood vessel size and distribution, and perfusion. Relevant anatomical and physiological properties are summarized and implemented toward the creation of a preliminary finger phantom. To create a preliminary finger phantom, we synthesized a high-compliance silicone matrix with scatterers for embedding flexible tubing and investigated the addition of these scatterers to novel 3D printing resins for optical property control without altering mechanical stability, streamlining the production of phantoms with biologically relevant characteristics. Phantom utility was demonstrated by applying dynamic, pressure waveforms to produce tube volume change and resultant photoplethysmography (PPG) signals. 3D printed phantoms achieved more biologically relevant conditions compared to molded phantoms. These preliminary results indicate that the phantoms show strong potential to be developed into tools for evaluating pulse oximetry performance. Gaps, recommendations, and strategies are presented for continued phantom development.

Rhodium-Catalyzed Asymmetric Macrocyclization towards Crown Ethers Using Hydroamination of Bis(allenes).

Enantiomerically enriched crown ethers (CE) exhibit strong asymmetric induction in phase transfer catalysis, supramolecular catalysis and molecular recognition processes. Traditional methods have often been used to access these valuable compounds, which limit their diversity and consequently their applicability. Herein, a practical catalytic method is described for the gram scale synthesis of a class of chiral CEs (aza-crown ethers; ACEs) using Rh-catalyzed hydroamination of bis(allenes) with diamines. Using this approach, a wide range of chiral vinyl functionalized CEs with ring sizes ranging from 12 to 36 have been successfully prepared in high yields of up to 92 %, dr of up to >20 : 1 and er of up to >99 : 1. These vinyl substituted CEs allow for further diversification giving facile access to various CE derivatives as well as to their three-dimensional analogues using ring-closing metathesis. Some of these chiral CEs themselves display high potential for use in asymmetric catalysis.

Rhodium‐Catalyzed Asymmetric Macrocyclization towards Crown Ethers Using Hydroamination of Bis(allenes)

AbstractEnantiomerically enriched crown ethers (CE) exhibit strong asymmetric induction in phase transfer catalysis, supramolecular catalysis and molecular recognition processes. Traditional methods have often been used to access these valuable compounds, which limit their diversity and consequently their applicability. Herein, a practical catalytic method is described for the gram scale synthesis of a class of chiral CEs (aza‐crown ethers; ACEs) using Rh‐catalyzed hydroamination of bis(allenes) with diamines. Using this approach, a wide range of chiral vinyl functionalized CEs with ring sizes ranging from 12 to 36 have been successfully prepared in high yields of up to 92 %, dr of up to &gt;20 : 1 and er of up to &gt;99 : 1. These vinyl substituted CEs allow for further diversification giving facile access to various CE derivatives as well as to their three‐dimensional analogues using ring‐closing metathesis. Some of these chiral CEs themselves display high potential for use in asymmetric catalysis.

Buried InGaAs/InP quantum wells selectively grown on SOI for lateral membrane laser diodes

The monolithic integration of energy-efficient and high-speed III–V lasers on silicon-on-insulator (SOI) platform in a cost-effective and scalable manner is the crux for the ubiquitous application of Si photonics in various applications. Here, aiming for lateral p-i-n membrane laser diodes, we report the growth of InGaAs/InP multi-quantum wells (MQWs) buried inside InP membranes on (001) SOI wafers using the lateral aspect ratio trapping method. We first obtain uniform InP membranes through careful tuning of a low-temperature nucleation layer, effectively trapping crystalline defects at the InP/Si heterogeneous interface and obtaining dislocation-free InP crystals away from the interface. We then construct buried (110)-oriented InGaAs/InP MQWs emitting in the telecom wavelengths by engineering the faceting of the InP membrane to enable the epitaxy of InGaAs alloy on (110) planes. These as-grown InGaAs/InP MQWs are fully embedded inside the InP membrane and provide effective confinement of both light and charged carriers. Our results demonstrate an elegant solution for future lateral membrane laser diodes directly grown on SOI wafers.

Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems.

Fog computing is today considered a promising candidate to improve the user experience in dynamic on-demand computing services. However, its ubiquitous application would require support for this service in wireless multi-hop mesh systems, where the use of conventional IP-based solutions is challenging. As a complementary solution, in this paper, we consider a Named-Data Networking (NDN) approach to enable fog computing services in autonomous dynamic mesh formations. In particular, we jointly implement two critical mechanisms required to extend the NDN-based fog computing architecture to wireless mesh systems. These are (i) dynamic face management systems and (ii) a learning-based route discovery strategy. The former makes it possible to solve NDN issues related to an inability to operate over a broadcast medium. Also, it improves the data-link layer reliability by enabling unicast communications between mesh nodes. The learning-based forwarding strategy, on the other hand, efficiently reduces the amount of overhead needed to find routes in the dynamically changing mesh networks. Our numerical results show that, for static wireless meshes, our proposal makes it possible to fully benefit from the computing resources sporadically available up to several hops away from the consumer. Additionally, we investigate the impacts of various traffic types and NDN caching capabilities, revealing that the latter result in much better system performance while the popularity of the compute service contributes to additional performance gains.

Financial Technology (Fintech) and Banking Industry Transformation: A Symbiotic Evolution into the Digital Era

This study navigates the dynamic landscape of Financial Technology (Fintech) and its transformative impact on the banking industry, elucidating the symbiotic evolution that characterizes this intersection. Fintech, as the fusion of cutting-edge technologies with financial services, signifies a revolutionary leap, fundamentally reshaping the financial landscape. From ubiquitous mobile banking applications to the advent of blockchain, AI-driven analytics, and cryptocurrencies, Fintech embodies a spectrum of innovations redefining traditional banking norms.The symbiosis between Fintech and banking drives a profound transformation, compelling traditional institutions to embrace digital strategies for competitiveness. Open Banking initiatives foster collaboration, blurring the lines between established banks and nimble Fintech startups. This exploration delves into the intricate layers of this relationship, unraveling the evolution, challenges, and future trends shaping the financial ecosystem. Keywords: Fintech Integration, Banking Transformation, Digital Financial Services, Open Banking Initiatives, Symbiotic Evolution.

Multiview Deep Anomaly Detection: A Systematic Exploration.

Anomaly detection (AD), which models a given normal class and distinguishes it from the rest of abnormal classes, has been a long-standing topic with ubiquitous applications. As modern scenarios often deal with massive high-dimensional complex data spawned by multiple sources, it is natural to consider AD from the perspective of multiview deep learning. However, it has not been formally discussed by the literature and remains underexplored. Motivated by this blank, this article makes fourfold contributions: First, to the best of our knowledge, this is the first work that formally identifies and formulates the multiview deep AD problem. Second, we take recent advances in relevant areas into account and systematically devise various baseline solutions, which lays the foundation for multiview deep AD research. Third, to remedy the problem that limited benchmark datasets are available for multiview deep AD, we extensively collect the existing public data and process them into more than 30 multiview benchmark datasets via multiple means, so as to provide a better evaluation platform for multiview deep AD. Finally, by comprehensively evaluating the devised solutions on different types of multiview deep AD benchmark datasets, we conduct a thorough analysis on the effectiveness of the designed baselines and hopefully provide other researchers with beneficial guidance and insight into the new multiview deep AD topic.

Robust Optimization and Inference on Manifolds

We propose a robust and scalable procedure for general optimization and inference problems on manifolds leveraging the classical idea of `median-of-means' estimation. This is motivated by ubiquitous examples and applications in modern data science in which a statistical learning problem can be cast as an optimization problem over manifolds. Being able to incorporate the underlying geometry for inference while addressing the need for robustness and scalability presents great challenges. We address these challenges by first proving a key lemma that characterizes some crucial properties of geometric medians on manifolds. In turn, this allows us to prove robustness and tighter concentration of our proposed final estimator in a subsequent theorem. This estimator aggregates a collection of subset estimators by taking their geometric median over the manifold. We illustrate bounds on this estimator via calculations in explicit examples. The robustness and scalability of the procedure is illustrated in numerical examples on both simulated and real data sets.