Real-time Properties Research Articles

A Wireless Autonomous Real-Time Underwater Acoustic Positioning System.

Recent acoustic telemetry positioning systems are able to reconstruct the positions and trajectories of organisms at a scale of a few centimeters to a few meters. However, they present several logistical constraints including receiver maintenance, calibration procedures and limited access to real-time data. We present here a novel, easy-to-deploy, energy self-sufficient underwater positioning system based on the time difference of arrival (TDOA) algorithm and the Global System for Mobile (GSM) communication technology, capable of locating tagged marine organisms in real time. We provide an illustration of the application of this system with empirical examples using continuous and coded tags in fish and benthic invertebrates. In situ experimental tests of the operational system demonstrated similar performances to currently available acoustic positioning systems, with a global positioning error of 7.13 ± 5.80 m (mean ± SD) and one-third of the pings can be localized within 278 m of the farthest buoy. Despite some required improvements, this prototype is designed to be autonomous and can be deployed from the surface in various environments (rivers, lakes, and oceans). It was proven to be useful to monitor a wide variety of species (benthic and pelagic) in real time. Its real-time property can be used to rapidly detect system failure, optimize deployment design, or for ecological or conservation applications.

On the use of denoing algorithms for ultrasound beamforming

Beamforming in receive, whose objective is to estimate an image from raw RF data acquired by the probe piezoelectric elements, plays a crucial role in ultrasound imaging. The standard method, called delay and sum (DAS) and implemented in most of the commercial scanners, consists in coherently summing the RF signals, providing the backprojection solution of the inverse problem of beamforming. Despite real-time properties, DAS results into images with limited spatial resolution and contrast. The literature of ultrasound beamforming is rich and mainly consists in alternatives to DAS based on non-adaptive or adaptive (e.g., minimum variance, coherence factor) methods or image reconstruction algorithms in the Fourier domain. Furthermore, inverse problem formulations have been shown to be well-adapted to ultrasound beamforming. They consist in minimizing a cost function formed by two terms: a data fidelity term modelling the acquisition setup, and a regularization term. The choice of the latter is not straightforward in ultrasound imaging, mainly because of the need to conserve statistical properties of the speckle. In this paper, denoising algorithms are shown to be good regularizers for ultrasound beamforming, providing a good performance in spatial resolution and contrast gain, without deteriorating the quality of the speckle texture.

Bidirectional Statistical Feature Extraction Based on Time Window for Tor Flow Classification

The anonymous system Tor uses an asymmetric algorithm to protect the content of communications, allowing criminals to conceal their identities and hide their tracks. This malicious usage brings serious security threats to public security and social stability. Statistical analysis of traffic flows can effectively identify and classify Tor flow. However, few features can be extracted from Tor traffic, which have a weak representational ability, making it challenging to combat cybercrime in real-time effectively. Extracting and utilizing more accurate features is the key point to improving the real-time detection performance of Tor traffic. In this paper, we design an efficient and real-time identification scheme for Tor traffic based on the time window method and bidirectional statistical characteristics. In this paper, we divide the network traffic by sliding the time window and then calculate the relative entropy of the flows in the time window to identify Tor traffic. We adopt a sequential pattern mining method to extract bidirectional statistical features and classify the application types in the Tor traffic. Finally, extensive experiments are carried out on the UNB public dataset (ISCXTor2016) to validate our proposal’s effectiveness and real-time property. The experiment results show that the proposed method can detect Tor flow and classify Tor flow types with an accuracy of 93.5% and 91%, respectively, and the speed of processing and classifying a single flow is 0.05 s, which is superior to the state-of-the-art methods.

Multi-Channel EEG Emotion Recognition Based on Parallel Transformer and 3D-Convolutional Neural Network

Due to its covert and real-time properties, electroencephalography (EEG) has long been the medium of choice for emotion identification research. Currently, EEG-based emotion recognition focuses on exploiting temporal, spatial, and spatiotemporal EEG data for emotion recognition. Due to the lack of consideration of both spatial and temporal aspects of EEG data, the accuracy of EEG emotion detection algorithms employing solely spatial or temporal variables is low. In addition, approaches that use spatiotemporal properties of EEG for emotion recognition take temporal and spatial characteristics of EEG into account; however, these methods extract temporal and spatial information directly from EEG data. Since there is no reconstruction of the EEG data format, the temporal and spatial properties of the EEG data cannot be extracted efficiently. To address the aforementioned issues, this research proposes a multi-channel EEG emotion identification model based on the parallel transformer and three-dimensional convolutional neural networks (3D-CNN). First, parallel channel EEG data and position reconstruction EEG sequence data are created separately. The temporal and spatial characteristics of EEG are then retrieved using transformer and 3D-CNN models. Finally, the features of the two parallel modules are combined to form the final features for emotion recognition. On the DEAP, Dreamer, and SEED databases, the technique achieved greater accuracy in emotion recognition than other methods. It demonstrates the efficiency of the strategy described in this paper.

A real-time mix-adjusted median property price index enabled by an efficient nearest neighbour approximation data structure

Homeowners, first-time buyers, banks, governments and construction companies are highly interested in following the state of the property market. Currently, property price indexes are published several months out of date and hence do not offer the up-to-date information which housing market stakeholders need in order to make informed decisions. In this article, we present an enhanced version of a mix-adjusted median based property price index which uses geospatial property data and stratification in order to compare similar houses sold in different trading periods. The expansion of the algorithm to include additional parameters, enabled by both a richer dataset and the introduction of a new, efficient data structure for nearest neighbour approximation, allows for the construction of a far smoother and more robust index than the original algorithm produced.

Real-Time Properties of Hydraulic Jump off a Tidal Bore, Its Generation and Transport Mechanisms: A Case Study of the Kampar River Estuary, Indonesia

Since the hydraulic jump off a tidal bore in the Kampar Estuary has never been well-described, real-time measurements of hydraulic jump properties are crucial to understanding the tidal bore characteristics. This study aims to determine the real-time properties of a tidal bore generation, hydraulic jump, and transport mechanism in the Kampar River estuary. Tidal harmonic and range are analyzed using least-square-based tidal modeling. The tidal bore height and turbulent velocity records based on ADCP surveys in the estuary and upstream area are used to determine the hydraulic jump properties. Furthermore, an acoustic-based approach is also employed to quantify the suspended sediment concentration and flux during the passage of the bore. Kampar Estuary is predominated by semidiurnal co-tidal components (M2 and S2), where, based on the phase lag magnitude, it is categorized as an ebb-dominant estuary. This finding is proven by the more intense and prolonged ebb phases, especially during spring tidal conditions where the tidal range reaches 4 m. Of particular concern, the tidal bore height declines by 1.5 m every 20 km upstream with an erratic turbulent velocity. A sudden increase in transverse and vertical velocity during the passage of bore (ranging from −0.9 to 0.2 m/s) reflects the potency of sediment resuspension in the surrounding river edge marked by the significant increase in suspended sediment flux of about 3.7 times larger than at the end of the ebb tide. However, long-term measurement and regular bathymetry surveys are crucial to monitor the tidal bore behavior and morpho-dynamics in the Kampar River estuary.

Uncertainty in the seismic performance of semi-active base isolation systems

In a conventional base isolation system, minimizing the seismic responses of the superstructure is always at the cost of increasing the isolator's response. The semi-active control of the isolator has been considered an effective solution to such a dilemma. It tunes the real-time properties of the isolator according to preset rules to further reduce the superstructure's seismic responses without increasing that of the isolator or vice versa. However, the number of ground motion records used to design and validate the controller, i.e., the preset rules, in existing studies is usually very small and therefore is suspectable if it is adequate to address the significant uncertainty in the shaking of future earthquakes. This paper critically reviews the performance of the proportional-integral-derivative (PID), linear-quadratic regulator (LQR), and fuzzy controllers in semi-active base isolation systems with magnetorheological (MR) dampers subjected to highly uncertain ground motion inputs through numerical simulations. The results show that the control performance of the controllers varies significantly with the increasing number of input records, suggesting the necessity of using at least 50 ground motion records to appropriately assess the performance uncertainty of semi-active base isolation systems. More importantly, the superior performance of the optimized controllers is not guaranteed if the system is subjected to ground motions that are new to the controller, even if the controller has been optimized for thousands of existing ground motions. It highlights the need of improving the adaptability of the semi-active systems for uncertain ground motion inputs.

In Situ Measurement of Instantaneous Viscosity Curve of Fluids in a Reserve Tank

A novel method enabling in situ measurement in a reserve tank for evaluating an instantaneous viscosity curve is presented. This aims to satisfy the practical demand of industrial production process for monitoring real-time rheological properties without taking samples. With applicable ranges of shear rate and viscosity, O(10–1–102 s–1) and O(10–2–100 Pa·s), it enables evaluation of fluid properties having shear strain rate dependence, based on ultrasonic spinning rheometry. Since this method evaluates the characteristic of shear thinning viscosity while changing the fluid temperature in a reserve tank, some useful examples are expected as further applications for measuring viscosity during chemical reaction processes, such as gelation, hydrolysis, and polymerization. Efficacy of the method evaluating rheological properties is validated by numerical and experimental results.

Cache Interference-aware Task Partitioning for Non-preemptive Real-time Multi-core Systems

Shared caches in multi-core processors introduce serious difficulties in providing guarantees on the real-time properties of embedded software due to the interaction and the resulting contention in the shared caches. Prior work has studied the schedulability analysis of global scheduling for real-time multi-core systems with shared caches. This article considers another common scheduling paradigm: partitioned scheduling in the presence of shared cache interference. To achieve this, we propose CITTA, a cache interference-aware task partitioning algorithm. We first analyze the shared cache interference between two programs for set-associative instruction and data caches. Then, an integer programming formulation is constructed to calculate the upper bound on cache interference exhibited by a task, which is required by CITTA. We conduct schedulability analysis of CITTA and formally prove its correctness. A set of experiments is performed to evaluate the schedulability performance of CITTA against global EDF scheduling and other greedy partition approaches such as First-fit and Worst-fit over randomly generated tasksets and realistic workloads in embedded systems. Our empirical evaluations show that CITTA outperforms global EDF scheduling and greedy partition approaches in terms of task sets deemed schedulable.

Bringing chemical structures to life with augmented reality, machine learning, and quantum chemistry.

Visualizing 3D molecular structures is crucial to understanding and predicting their chemical behavior. However, static 2D hand-drawn skeletal structures remain the preferred method of chemical communication. Here, we combine cutting-edge technologies in augmented reality (AR), machine learning, and computational chemistry to develop MolAR, an open-source mobile application for visualizing molecules in AR directly from their hand-drawn chemical structures. Users can also visualize any molecule or protein directly from its name or protein data bank ID and compute chemical properties in real time via quantum chemistry cloud computing. MolAR provides an easily accessible platform for the scientific community to visualize and interact with 3D molecular structures in an immersive and engaging way.

Liposome-Loaded Targeted Theranostic Fluorescent Nano-Probes for Diagnosis and Treatment of Cervix Carcinoma

Near-infrared fluorescence imaging, with its high sensitivity, non-invasiveness, and superior real-time feedback properties, has become a powerful skill for accurate diagnosis in the clinic. Nanoparticle-assisted chemotherapy is an effective cure for cancer. Specifically, the combination of near-infrared fluorescence imaging with chemotherapy represents a promising method for precise diagnosis and treatment of cervical cancer. To realize this approach, it is necessary to design and synthesize therapeutic nano-probes with detection abilities. In this work, an organic NIRF emissive heptamethine cyanine dye, IR783, was utilized and encapsulated in biocompatible drug-carrier liposomes). Then, the anticancer drug doxorubicin was loaded, to form LP-IR783-DOX nanoparticles. The LP-IR783-DOX nanoparticles had spherical shapes and were smoothly dispersed in aqueous solutions. Favorable absorption (a peak of 800 nm) and fluorescence (a peak of 896 nm) features were obtained from LP-IR783-DOX nanoparticles in the near-infrared region. Moreover, the specific detection abilities of nanoparticles were confirmed in different cell lines, and nanoparticles exhibited strong detection abilities in human cervix carcinoma cells in particular. To analyze the chemotherapeutic properties of LP-IR783-DOX nanoparticles, live HeLa cells were studied in detail, and the application of these NPs resulted in a chemotherapeutic efficiency of 56.75% based on fluorescein isothiocyanate staining and flow cytometry. The results indicate that nanoparticles have great potential for theranostic application of fluorescence imaging and chemotherapy in cases of cervical cancer.

Formal Verification of Real-Time Autonomous Robots: An Interdisciplinary Approach.

Due to the severe consequences of their possible failure, robotic systems must be rigorously verified as to guarantee that their behavior is correct and safe. Such verification, carried out on a model, needs to cover various behavioral properties (e.g., safety and liveness), but also, given the timing constraints of robotic missions, real-time properties (e.g., schedulability and bounded response). In addition, in order to obtain valid and useful verification results, the model must faithfully represent the underlying robotic system and should therefore take into account all possible behaviors of the robotic software under the actual hardware and OS constraints (e.g., the scheduling policy and the number of cores). These requirements put the rigorous verification of robotic systems at the intersection of at least three communities: the robotic community, the formal methods community, and the real-time systems community. Verifying robotic systems is thus a complex, interdisciplinary task that involves a number of disciplines/techniques (e.g., model checking, schedulability analysis, component-based design) and faces a number of challenges (e.g., formalization, automation, scalability). For instance, the use of formal verification (formal methods community) is hindered by the state-space explosion problem, whereas schedulability analysis (real-time systems) is not suitable for behavioral properties. Moreover, current real-time implementations of robotic software are limited in terms of predictability and efficiency, leading to, e.g., unnecessary latencies. This is flagrant, in particular, at the level of locking protocols in robotic software. Such situation may benefit from major theoretical and practical findings of the real-time systems community. In this paper, we propose an interdisciplinary approach that, by joining forces of the different communities, provides a scalable and unified means to efficiently implement and rigorously verify real-time robots. First, we propose a scalable two-step verification solution that combines formal methods and schedulability analysis to verify both behavioral and real-time properties. Second, we devise a new multi-resource locking mechanism that is efficient, predictable, and suitable for real-time robots and show how it improves the latter’s real-time behavior. In both cases, we show, using a real drone example, how our approach compares favorably to that in the literature. This paper is a major extension of the RTCSA 2020 publication “A Two-Step Hybrid Approach for Verifying Real-Time Robotic Systems.”

A Low Profile Microstrip Patch Antenna using a Polygon-Shaped Patch for Wireless Applications.

In this paper. a low profile microstrip patch antenna is presented. The FR4 substrate is chosen for the dielectric substrate with a height of 1.6 mm. The presented antenna employs a polygon-shaped patch as the radiator for operating at 2.4 GHz. Co-axial feed is selected to excite the resented antenna to retain the low profile property of the antenna. The simulated results of the proposed antenna were analyzed and the structure of the proposed antenna was validated through fabrication results. The vector network analyzer is used to measure the real-time return loss property of the presented antenna.

Performance comparison between fixed tilt angle and solar tracking systems at Basra governorate: A case study

Theoretical <span lang="EN-US">calculations and online system simulations using PVWatts and global solar atlas simulators, were conducted in this study to find the difference in solar power production between fixed solar power systems and tracking systems at Basra Governorate. The research included the analysis of geographical location and weather conditions and their effect on output power. The reliant power resource types and power generation of the southern region of Iraq as well as load demands were demonstrated and discussed in this research. Furthermore, the sun path, solar angles and solar radiations were considered in this study, in addition to the mathematical calculations of optimum tilt angles. The methodology used in this study was based upon theoretical and online measures of real-time weather factors, solar angles, solar radiations and model properties of the examined system. The results and factors of different systems including: peak sun hours per day, dc to ac derate factors, tilt angles, solar radiations and power production were compared to multiple similar research elements that were accomplished around the same region and some other countries. The study concluded that solar tracking system absorbs more radiations and produces an annual production of 15–30% higher than fixed tilt angle system.</span>

Real-time nanomechanical property modulation as a framework for tunable NEMS

Phase-change materials (PCMs) can switch between amorphous and crystalline states permanently yet reversibly. However, the change in their mechanical properties has largely gone unexploited. The most practical configuration using suspended thin-films suffer from filamentation and melt-quenching. Here, we overcome these limitations using nanowires as active nanoelectromechanical systems (NEMS). We achieve active modulation of the Young’s modulus in GeTe nanowires by exploiting a unique dislocation-based route for amorphization. These nanowire NEMS enable power-free tuning of the resonance frequency over a range of 30%. Furthermore, their high quality factors (Q > 104) are retained after phase transformation. We utilize their intrinsic piezoresistivity with unprecedented gauge factors (up to 1100) to facilitate monolithic integration. Our NEMS demonstrate real-time frequency tuning in a frequency-hopping spread spectrum radio prototype. This work not only opens up an entirely new area of phase-change NEMS but also provides a novel framework for utilizing functional nanowires in active mechanical systems.

DoSRA: A Decentralized Approach to Online Edge Task Scheduling and Resource Allocation

With the proliferation of novel Internet of Things (IoT) mobile applications and advanced communication technologies, nowadays we are surrounded by ubiquitous sensors and smart devices. These smart IoT devices generate a large volume of data day and night at the edge of the network, create a huge demand for edge computing resources, and thus, promote the emergence of the multiaccess edge computing (MEC) paradigm. In MEC environments, IoT devices or mobile users are allowed to offload their computational tasks to nearby edge servers to overcome the limitation of local computing resources. Though edge servers could provide low-latency service with high-responsible computing capabilities, they are still facing many challenges posed by the limited hardware resources and diverse offloading requests. However, traditional approaches are usually based on the centralized architecture and batch-processing scheduling mode, which might lead to low efficiency and high communication overhead. Besides, they also lack the consideration of task diversity and priorities, which are crucial in real-world application scenarios. Thus, smart task scheduling and resource provision strategies with a high real-time property are urgently needed for better user experience and higher resource utilization. In this article, we target the online edge IoT task scheduling and resource allocation problem and propose a decentralized approach (DoSRA). The experiments based on real-world edge environments have demonstrated that the proposed approach could achieve at most a 35.34% reduction on the average weighted offloading response time.

A Model-Based Approach for Common Representation and Description of Robotics Software Architectures

Unlike conventional software, robotic software suffers from a lack of methods and processes that could systematize and facilitate development. Thus, the application of software engineering techniques is at the heart of current issues in robotics. The work presented in this paper aims to facilitate the development of robotic software and to facilitate communication between experts in the field through the use of software engineering techniques and methods. It proposes RsaML (Robotic Software Architecture Modeling Language), a Domain Specific Modeling Language (DSML) dedicated to robotics, which takes into account the different categories of robotic software architectures and makes it possible to describe the latter independently from the implementation platform. The conceptual model defining the terminology and the hierarchy of concepts used for the description and representation of robotic software architectures in RsaML are presented in this article. RsaML is defined through a meta-model which represents the abstract syntax of the language. The real-time properties of robotic software architectures are identified and included in the meta-model. The use of RsaML is illustrated through several experimental scenarios of the language: the definition of a robotic system and the description of its software architecture, the verification of the semantics of a robotic software architecture, and the modeling of a robotic system whose software architecture does not belong to the usual categories. The support tool used for implementations and experimentation is Eclipse Modeling Framework (EMF). The results of experimentation showed good working of the proposed solution and made it possible to validate the main concepts of the RsaML language.

Reconstructing QCD spectral functions with Gaussian processes

We reconstruct ghost and gluon spectral functions in 2+1 flavor QCD with Gaussian process regression. This framework allows us to largely suppress spurious oscillations and other common reconstruction artifacts by specifying generic magnitude and length scale parameters in the kernel function. The Euclidean propagator data are taken from lattice simulations with domain wall fermions at the physical point. For the infrared and ultraviolet extensions of the lattice propagators as well as the low-frequency asymptotics of the ghost spectral function, we utilize results from functional computations in Yang-Mills theory and QCD. This further reduces the systematic error significantly. Our numerical results are compared against a direct real-time functional computation of the ghost and an earlier reconstruction of the gluon in Yang-Mills theory. The systematic approach presented in this work offers a promising route towards unveiling real-time properties of QCD.

DNA-Templated ultrasmall bismuth sulfide nanoparticles for photoacoustic imaging of myocardial infarction

Photoacoustic imaging (PAI) has shown great clinical potential in diagnosing various diseases due to its noninvasive, cost-effective, and real-time imaging properties but is limited by the lack of contrast agents with high sensitivity for deep tissue imaging. Here, DNA-templated ultrasmall bismuth sulfide (Bi2S3) nanoparticles (NPs) were reported as a photoacoustic (PA) probe for imaging myocardial infarction. We present a simple synthesis strategy of ultrasmall NPs via self-assembly of single-stranded DNA (ssDNA)/metal ion complexes. The in vivo imaging results showed a dramatically enhanced PA signal in the region of myocardial infarction after intravenous injection of DNA-Bi2S3 NPs in the myocardial ischaemia/reperfusion (I/R) mouse model. Further near infrared fluorescence imaging indicated that Bi2S3 NPs mainly accumulated in the infarcted area, leading to enhancement of PA signals. Moreover, such hybrid NPs possess a well-defined nanostructure, superior photobleaching resistance, excellent water dispersibility and negligible acute toxicity. These results not only demonstrate that ultrasmall DNA-Bi2S3 NPs are a potent PA probe for imaging the infarcted region but also provide a new avenue for preparing ultrasmall-sized PA probes by using ssDNA as a template.

Anomalous magnetic noise in an imperfectly flat landscape in the topological magnet Dy2Ti2O7

Noise generated by motion of charge and spin provides a unique window into materials at the atomic scale. From temperature of resistors to electrons breaking into fractional quasiparticles, "listening" to the noise spectrum is a powerful way to decode underlying dynamics. Here, we use ultrasensitive superconducting quantum interference device (SQUIDs) to probe the puzzling noise in a frustrated magnet, the spin-ice compound Dy2Ti2O7 (DTO), revealing cooperative and memory effects. DTO is a topological magnet in three dimensions-characterized by emergent magnetostatics and telltale fractionalized magnetic monopole quasiparticles-whose real-time dynamical properties have been an enigma from the very beginning. We show that DTO exhibits highly anomalous noise spectra, differing significantly from the expected Brownian noise of monopole random walks, in three qualitatively different regimes: equilibrium spin ice, a "frozen" regime extending to ultralow temperatures, and a high-temperature "anomalous" paramagnet. We present several distinct mechanisms that give rise to varied colored noise spectra. In addition, we identify the structure of the local spin-flip dynamics as a crucial ingredient for any modeling. Thus, the dynamics of spin ice reflects the interplay of local dynamics with emergent topological degrees of freedom and a frustration-generated imperfectly flat energy landscape, and as such, it points to intriguing cooperative and memory effects for a broad class of magnetic materials.