Real-time Transport Research Articles

Control of Photocarrier Separation and Recombination at Bismuth Oxyhalide Interface for Nitrogen Fixation.

Developing high-efficiency photocatalysts for clean energy generation is a grand challenge, which requires simultaneously steering photocarrier dynamics and chemical activity for a specific reaction. To this end, here for the first time, we explore the real-time photocarrier transport property and catalytic mechanism of nitrogen reduction reaction (NRR) at the interface of bismuth oxyhalides (BiOX, X = Cl, Br, and I), an inexpensive and green semiconductor. By time-dependent ab initio non-adiabatic molecular dynamics simulations, we show that the separation and recombination processes of excited carriers as well as the catalytic activity can be concurrently optimized by precise band structure engineering. The exact influence of impurity states and heterojunction on the reduction power and lifetime of photogenerated carriers, light absorbance, and NRR activity/selectivity of BiOX are clearly unveiled, to provide essential physical insights for improving the photocatalytic efficiency of semiconductors for practical solar energy conversion and hydrogen fuel storage.

Ultra-Low-Latency, High-Fidelity Analog-to-Digital-Compression Radio-Over-Fiber (ADX-RoF) for MIMO Fronthaul in 5G and Beyond

Wired-wireless convergence technology such as radio-over-fiber (RoF) is regarded as a promising candidate to deliver broadband wireless data from/to edge cloud to/from antenna sites, a notable example being the fronthaul (FH) in centralized/cloud radio access network (C-RAN). In 5G and beyond, the latency, bandwidth and fidelity requirements on FH pose great challenges to the RoF schemes. In this article, we focus on analog-to-digital-compression RoF (ADX-RoF) scheme based on low-latency MIMO data compression, which has the potential to reduce FH data rate by ~90% compared with traditional digital RoF (CPRI) while still maintaining signal fidelity for high-order radio modulation. To verify the practicality of ADX-RoF in the latency-sensitive FH scenario, real-time hardware demonstration with latency evaluation is indispensable. We propose and design a real-time ADX, which achieves low-latency and high-throughput together with high-fidelity. Enabled by the ADX prototyped on a single-chip field-programmable radio platform (i.e., Xilinx RFSoC), we experimentally demonstrate 16-channel MIMO radio reception, real-time ADX processing and RoF transport. With <; 500 ns one-way ADX latency, less than 1.5% average EVM is achieved for 1024QAM, 61.44 MHz NR-class signals at a compression ratio of 13.3%. Negligible fiber-induced performance penalty is observed after 40 km transport. The results highlight the attractiveness of ADX-RoF for future FH.

Structural Properties of the Static Armor during Formation and Reestablishment in Gravel-Bed Rivers

The formation and reestablishment of bed structural properties in the static armor layer is an important research subject. To address this issue, we conducted a series of static armor layer experiments in a laboratory flume that focused on formation and reestablishment. Through an automatic measurement system, we obtained a real-time bed load transport rate. The bed surface elevation at different flow intensities was obtained using a PTS (Photo Terrain Scanning) system. The results show that the formation and reestablishment of the bed load transport rate in the static armor layer increased from zero to its peak before decaying. The bed structure response was found to be highly dependent on the unevenness of the bed surface elevation. The bed surface elevation of a static armor layer in a laboratory flume is considered as a two-dimensional random field. In a two-dimensional random field, the changes in bed elevation are characterized using statistical parameters. Statistical parameters are evaluated from precise digital elevation models (DEMs) of bed surfaces. Experimental results provide the change of probability distribution functions (PDFs) and second-order structure functions of bed elevations between formation and reestablishment after breaking the static armor layer. By quantitatively analyzing the changes in these statistical parameters, we quantified the difference between the bed structure in the static armor layer formation and the new static armor layer formed after being broken. Thus, this finding reveals that the bed structure of the static armor layer formed by different flow intensities is different, and this difference can be quantified using statistical methods.

Revealing ion transport in supercapacitors with Sub-2 nm two-dimensional graphene channels

Unique ion transport behaviors in two-dimensional (2D) nanochannels have sparked strong interests in exploring 2D nanomaterials for supercapacitor applications, which rely much on the formation of electric double layers (EDLs) at the solid-liquid interface. However, there is still a crucial missing part on understanding how ions with different kinetic properties could affect the formation of EDLs. Here, we examine the real-time ion transport in the formation of EDLs by electrochemical quartz crystal microbalance (EQCM) and observe distinct charge transport behaviors between activated carbon with a tortuous pore structure and graphene films with 2D channel spacing (<2 nm). Using molecular dynamics (MD) simulations, we find that ions with a higher diffusion coefficient predominantly affect the EDLs formation, leading to the anion- or cation-dominated ion exchange process in the sub-2 nm 2D graphene channels. By expanding to different electrolytes, the kinetics-controlled ion transport mechanism is further confirmed by experimental observations and MD simulations. Such findings will bring new insights for improved electrochemical performance of 2D nanomaterials.

Applying a deployment strategy and data analysis model for water quality continuous monitoring and management

In Taiwan, where residential and industrial areas are in close proximity, finding ways to effectively continuous monitor and manage water quality is an essential issue. This study established a total solution for an Internet of things water quality monitoring network that integrates domestic miniaturized water quality monitoring sensors for real-time transport data of pH, temperature, conductivity, chemical oxygen demand, and copper ions. The data will be used to establish an analysis model based on continuous monitoring of the nation’s background concentration. We designed an automatic continuous monitoring and early warning analysis module for automatic analysis of environmental and instrumental anomalies for decision makers, a “pollution source analysis module” utilizing static and dynamic cross-environment data to swiftly trace upstream pollution sources, and a “pollution hotspot analysis module” to evaluate the impact area of pollutants, and immediate response measures to achieve early warning and swift evaluation for the prevention of water pollution. To do this, we installed 100 domestic miniaturized water monitoring devices in Taoyuan City for testing the solution. We found that the establishment of an Internet of things environment analysis and response model integrated with cross-environment analysis can be applied in water quality monitoring and management to assure improved environmental quality.

Evaluation of real-time vehicle energy consumption and related emissions in China: A case study of the Guangdong–Hong Kong–Macao greater Bay Area

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) was recently proposed by the Chinese government for socioeconomic–political–environmental development. As one of the most important parts for the development of the GBA, the transportation sector has become a nonnegligible consumer of energy and source of emission. Therefore, vehicle energy consumption and related emissions in the GBA must be evaluated. However, an effective framework for accurately estimating the real-time transportation performance, vehicle energy consumption and emission in large urban areas is still lacking. This study aims to develop a novel method for examining the regional integration and the spatial connection that affect vehicle emission via crowdsourced traffic data and an emission model. The novelty of this study is that it explores the acquisition of large-scale and real-time data as well as the calculation of corresponding energy consumption and environmental impact. Based on the analysis of transportation in GBA, it is found that the vehicle energy consumption and emission on the expressway are nearly half as that on the other urban roads. Moreover, a significant difference exists in vehicle energy consumption and emission between passenger cars and medium-duty trucks. Regarding transportation performance, vehicle energy consumption and emissions, they are closely related to departure time. It is also found that most of the adjacent cities tend to have high vehicle energy consumption and emission. The contribution of the Hong Kong–Zhuhai–Macao Bridge to transportation from Hong Kong, Macao and Zhuhai is different. Hong Kong benefits most from the bridge in terms of traffic energy efficiency. This study would be valuable to both researchers and practitioners. It helps researchers apply a large dataset in replicating real-world travel, energy consumption and emission pattern at a large scale. Policymakers and practitioners would benefit from developing effective strategies for the sustainable development of the GBA.

Real-Time, Deep Learning Based Wrong Direction Detection

In this paper, we develop a real-time intelligent transportation system (ITS) to detect vehicles traveling the wrong way on the road. The concept of this wrong-way system is to detect such vehicles as soon as they enter an area covered by a single closed-circuit television (CCTV) camera. After detection, the program alerts the monitoring center and triggers a warning signal to the drivers. The developed system is based on video imaging and covers three aspects: detection, tracking, and validation. To locate a car in a video frame, we use a deep learning method known as you only look once version 3 (YOLOv3). Therefore, we use a custom dataset for training to create a deep learning model. After estimating a car’s position, we implement linear quadratic estimation (also known as Kalman filtering) to track the detected vehicle during a certain period. Lastly, we apply an “entry-exit” algorithm to identify the car’s trajectory, achieving 91.98% accuracy in wrong-way driver detection.

Vessel intelligent transportation maritime service portfolios in port areas under e-navigation framework

In this study, to satisfy the urgent demand of vessel transportation marine service that are more standardized and intelligent, the vessel intelligent transportation maritime service portfolios in port areas (VITMSPPA) under e-navigation framework are proposed. First, we analyzed the qualitative description of vessel transportation maritime services in the maritime service portfolios. Thereafter, by combining the demand of e-navigation users and the vessel traffic service user’s guide, we propose the logical demand and structure of the VITMSPPA. Second, based on this structure, a data model of VITMSPPA is established, which tally to the common maritime data structure. Third, considering the heterogeneity of VITMSPPA data sources and the characteristics of port traffic data, a data lifecycle management model is constructed to obtain intelligent maintenance of VITMSPPA data. Finally, taking Tianjin port in China as an example, the VITMSPPA is designed based on the electronic chart display and information system using VC++ and MySQL. The simulation experiments are carried out in the different process of vessels entering and leaving port. The results show that the proposed method can provide active, real-time and efficient vessel transportation maritime services for vessels.

Real-time Transportation Prediction Correction using Reconstruction Error in Deep Learning

In online complex systems such as transportation system, an important work is real-time traffic prediction. Due to the data shift, data model inconsistency, and sudden change of traffic patterns (like transportation accident), the prediction result derived from an offline-built model would be unreliable. Retraining the model is usually not time affordable for online prediction, especially when the prediction model is very complex and costs a lot of training time (for example, deep neural networks). A real-time prediction correction strategy would be of great value under this situation. Traditionally, the prediction correction usually relies on the prediction error in several previous time intervals. They assume that the error pattern is similar in the current time interval, so that it is time-delayed to some extent. In this article, we propose the prediction correction strategy using the reconstruction error in the deep neural network. The reconstruction error can reflect the model’s ability on feature representation and then determine the fitness of an input data to the model. We first build the relationship between reconstruction error and prediction error. From the perspective of the prediction interval, we demonstrate that the reconstruction error is in positive relation with the prediction interval. Thus the prediction result is more reliable when the reconstruction error is smaller. Then we propose two mechanisms of real-time prediction correction using the reconstruction error. The data driven prediction correction approach selects several training instances with similar reconstruction errors to the current instance and using their average prediction error in correcting the prediction result. The model-driven approach builds several component deep neural networks in training. The component training set for each network is selected according to the reconstruction error of training instances. For a predicting instance, it first computes the reconstruction error of the sample in each component network and then averages the results by the reconstruction error and prediction interval. The model-driven approach is actually a reconstruction error-based deep neural network ensemble approach. Finally, a series of experiments demonstrated that reconstruction error based prediction correction approaches are effective in several prediction problems in transportation including traffic flow prediction on road, traffic flow prediction in entrance and exit station and travel time prediction. Besides the high overall accuracy, our approach can also provide many observations of using the reconstruction error in transportation prediction.

Sediment transport pre-measurement as revealed by the hydrophone monitoring technique at a volcanic river

Recognizing the real-time sediment transport phenomena of rivers in the Mt. Merapi area has become an important issue, particularly in the efforts to mitigate risks of lahar flow disasters. The development of a lahar-related monitoring system in the Mt. Merapi area has currently become more challenging since contact and direct lahar flow measurement is very risky. Former techniques for lahar flow occurrences such as cable sensor and video shooting are considered good enough in terms of identifying the occurrence, but not the intensity. Furthermore, after the cable is cut, re-installation often meets its practical limit. The hydrophone measurement technique seems to be an acceptable technique subject to sufficient field calibration. This paper reports the performance of a hydrophone equipment, which can be used for monitoring bedload transport with diameters greater than 4 mm.

Integrating IoT with LQR-PID controller for online surveillance and control of flow and pressure in fluid transportation system

Abstract In the modern upgradation era, monitoring and control system is essential to ensure the smooth operation of the long-distance pipeline in the fluid transportation system. Now-a-days, to design an accurate online monitoring and control system represents a critical task that requires proactive planning in fluid transportation system. In the fluid pipeline system, the huge amount of fluid loss caused by leakages, cracks and blockages due to extreme pressure changes in the pipelines during transportation. The present research work focuses on developing Integrated IoT based intelligent architecture to perform online monitoring and control of pressure and flow rate in the fluid transportation system. The proposed Integrtaed IoT based architecture holds SCADA with LQR-PID controller as local control unit or local intelliegnce. During crack and leak occurrences in the fluid pipeline, SCADA with PID controller cannot afford desired control action due to drastic change in the pressure and the flow rate. Hence the entire architecture is monitored and accessed through high-level online server IoT interface to identify leaks and cracks in the pipeline at the initial stage before it leads to any catastropic situations. In order to attain better data communication between cloud server and pipeline hardware setup, smart IoT module is designed and fabricated. Once the crack or leaks is identified in the IoT front end, immediately emergency shut off is activated by the cloud server through smart IoT module by stopping the pump. The developed Integrated IoT architecture is experimentally validated in real-time lab-scale fluid transportation pipeline system. Further in this present work, the performance of Linear Quadratic Regulator-PID controller to regulate pressure and flow rate of the fluid being tansported is analyzed by comparing with convnetional controllers like Internal-Mode controller and Zigler–Nichols controller.

Demand-Oriented Approach to Estimate the Operational Performance of Urban Networks With and Without Traffic Information Provision

This paper addresses the problem of estimating the operational performance of extended urban transport networks under different conditions of traffic information availability. The suggested approach employs a simulation procedure for the real-time estimation of the network state, based on the dynamic assignment of an O-D matrix, which is adjusted by the prevailing traffic conditions. Next, a different procedure is used for the online simulation of the route diversion behavior of users under the influence of route guidance information. Moreover, the approach provides the prediction of the network state by hypothesizing about the adjustment of the travel behavior of users to the new traffic conditions generated by the routing information. The implementation of the approach on a part of a real urban network led to the determination of suitable information updating frequencies for providing the largest improvements in the various performance measures. The results signify the important role of taking into account dynamic changes in the O-D demand size for the plausible evaluation of the impact of information provision on the network performance for real-time traffic operations and transport planning purposes.

The Design of Intelligent Transportation Video Processing System in Big Data Environment

The intelligent transportation system in big data environment is the development trend of future transportation system, which effectively integrates advanced information technology, data communication transmission technology, electronic sensor technology, control technology and computer technology and is applied to overall ground transportation management. Hence, it establishes a real-time, accurate, efficient and comprehensive transportation management system that functions in a wide range and all-round aspects. In order to meet the demands of the intelligent transportation big data processing, this paper puts forward a high performance computing architecture of large-scale transportation video data management based on cloud computing, designs a parallel computing model containing the distributed file system and distributed computing system to solve the problems such as flexible server increase or decrease, load balancing and flexible dynamic storage increase or decrease, computing power and great improvement of storage efficiency. On the basis of this technical architecture, the system adopts BP neural network-related algorithms to extract the static transportation signs in road videos, and uses interframe difference algorithm and Gaussian mixture model (GMM) fusion algorithm to extract the moving targets in road transportation videos. In this way, they are taken as important integral parts and data sources of key frames of intelligent video image recognition to improve the recognition ability of key frames and eventually utilize semantic recognition model based on CNN (Convolutional Neural Network) to complete the intelligent recognition of whole transportation videos. Through network pressure test, computing ability test, recognition ability test and other tests, it has been proved that the intelligent transportation video processing system based on big data environment is successful and the design scheme of this system has strong practical application value.

Real-Time Wire EDM Tool Simulation Enabled by Discharge Location Tracker

In Wire Electrical Discharge Machining (WEDM), one of the most limiting factors for productivity, especially during rough cutting, is the wire breakage. Root causes of wire breakage are widely investigated in the literature and three main causes have been identified: mechanical rupture, excessive thermal load on wire and discharges concentration. Since the wire breakage is essentially the limiting factor for WEDM performance, the capability to safely approach the wire electrode exploitation limits is crucial for high performance and high efficiency WEDM. This paper describes a real-time, in-process, Finite Elements Model (FEM) simulation of the tool electrode in WEDM. Using the Discharge Location Tracker (DLT), position and energy of each discharge is known; allowing real-time realistic simulation of the wire electrode thermo-mechanical status. Based on the single discharges crater volume and on the wire unwinding speed, the wire electrode wear is simulated and its radius as a function of the vertical coordinate r(z) is calculated. Then, the thermal equilibrium of the wire is computed using a FEM algorithm, taking in consideration the real-time wear and transport of the wire for Joule dissipation, thermal convection and thermal diffusion. Moreover, the wire heating is also calculated in real-time based on the single discharge position and energy. The numerical model, coupled with the DLT sensor, allows real-time monitoring of the wire electrode thermo-mechanical state during machining.

Transmembrane Cu(I) P-type ATPase pumps are electrogenic uniporters.

Cu(i) P-type ATPases are transmembrane primary active ion pumps that catalyze the extrusion of copper ions across cellular membranes. Their activity is critical in controlling copper levels in all kingdoms of life. Biochemical and structural characterization established the structural framework by which Cu-pumps perform their function. However, the details of the overall mechanism of transport (uniporter vs. cotransporter) and electrogenicity still remain elusive. In this work, we developed a platform to reconstitute the model Cu(i)-pump from E. coli (EcCopA) in artificial lipid bilayer small unilamellar vesicles (SUVs) to quantitatively characterize the metal substrate, putative counter-ions and charge translocation. By encapsulating in the liposome lumen fluorescence detector probes (CTAP-3, pyranine and oxonol VI) responsive to diverse stimuli (Cu(i), pH and membrane potential), we correlated substrate, secondary-ion translocation and charge movement events in EcCopA proteoliposomes. This platform centered on multiple fluorescence reporters allowed study of the mechanism and translocation kinetic parameters in real-time for wild-type EcCopA and inactive mutants. The maximal initial Cu(i) transport rate of 165 nmol Cu(i) mg-1 min-1 and KM, Cu(I) = 0.15 ± 0.07 μM was determined with this analysis. We reveal that Cu(i) pumps are primary-active uniporters and electrogenic. The Cu(i) translocation cycle does not require proton counter-transport resulting in electrogenic generation of transmembrane potential upon translocation of one Cu(i) per ATP hydrolysis cycle. Thus, mechanistic differences between Cu(i) pumps and other better characterized P-type ATPases are discussed. The platform opens the venue to study translocation events and mechanisms of transport in other transition metal P-type ATPase pumps.

Accelerating Information Security in Cloud Computing using a Novel Holomorphic Scheme

In the past decades, many security algorithms are invented and used in many applications for encryption and decryption of information or data. All the secure algorithms were uses the security key of size 8bit, 16bit, 32bit, 64bit and 128bits for encryption and decryption but literature survey says that higher the key size, higher the security. But the higher key size has number more bits and its requires more memory for storage and also to perform each and every bit through computational operations leads to more delay. To address these issues, the key and plain text are of 128bits and converted into integer. The converted integer values of key and plaintext are encrypted and decrypted using Holomorphic through Advanced Encryption Standard (AES). The research method is sophisticated and more protected through meaning fully lesser key in size and is accomplished for encryption in terms integer key and plaintext moderately than binary bits, therefore larger size of key and plaintext can be minimized and reduced the computational complexities. Finally the cipher text is uploaded into cloud through Real Time Transport Protocol (RTP) and Real Time Transport Control Protocol (RTCP) for storage. The main problem with continuous sharing of information into cloud is security attacks so in this research work, three different multimedia signals such as ECG, EEG and biomedical images are converted into integer and encrypted using AES. The stored data can access by the authorized users and can decode the information after decryption using AES.

A reliable and efficient micro-protocol for data transmission over an RTP-based covert channel

As the VoIP steganographic methods provide a low capacity covert channel for data transmission, an efficient and real-time data transmission protocol over this channel is required which provides reliability with minimum bandwidth usage. This paper proposes a micro-protocol for data embedding over covert storage channels or covert hybrid channels developed by steganographic methods where real-time transport protocol (RTP) is their underlying protocol. This micro-protocol applies an improved Go-Back-N mechanism which exploits some RTP header fields and error correction codes to retain maximum covert channel capacity while providing reliability. The bandwidth usage and the performance of the proposed micro-protocol are analyzed. The analyses indicate that the performance depends on the network conditions, the underlying steganographic method, the error correction code and the adjustable parameters of the micro-protocol. Therefore, a genetic algorithm is devised to obtain the optimal values of the adjustable micro-protocol parameters. The impact of network conditions, the underlying steganographic method and the error correction code on the performance are assessed through simulations. The performance of this micro-protocol is compared to an existing method named ReLACK where this micro-protocol outperforms its counterpart.

Simulation and Formal Verification of SIP/ZRTP Protocol using UPPAAL

The security of voice communication over the Internet Protocol is a continuously growing research area due to the rapid rise in its usage among consumers. With the advent of Voice-over-IP Protocols, the Real Time Protocol (RTP) was used to facilitate VoIP communications. To secure this communication, Secure Real Time Protocol (SRTP) was implemented to encrypt these voice packets. The SRTP requires a session key to be shared between the communicating entities. The challenging task of establishing a new, unused session key to secure each SRTP session was overcome by the key agreement protocol, Zimmermann Real-time Transport Protocol (ZRTP) which ensures confidentiality as well as a shield against Man-in-the-Middle attack. We firstly analyze the security properties of this protocol. Formal analysis is a mathematical technique that can be used to verify the correctness of the system. We simulated the complete ZRTP Protocol with the well-known formal analysis tool, Uppaal, and verified the existing security properties such as Deadlock Prevention, Liveliness, Safety and other protocol parameters mismatch detection using the Uppaal model checker engine. Temporal logic was used to design the queries to verify the properties.

Modified Differential Evolution Algorithm for a Transportation Software Application

This research developed a solution approach that is a combination of a web application and the modified differential evolution (MDE) algorithm, aimed at solving a real-time transportation problem. A case study involving an inbound transportation problem in a company that has to plan the direct shipping of a finished product to be collected at the depot where the vehicles are located is presented. In the newly designed transportation plan, a vehicle will go to pick up the raw material required by a certain production plant from the supplier to deliver to the production plant in a manner that aims to reduce the transportation costs for the whole system. The reoptimized routing is executed when new information is found. The information that is updated is obtained from the web application and the reoptimization process is executed using the MDE algorithm developed to provide the solution to the problem. Generally, the original DE comprises of four steps: (1) randomly building the initial set of the solution, (2) executing the mutation process, (3) executing the recombination process, and (4) executing the selection process. Originally, for the selection process in DE, the algorithm accepted only the better solution, but in this paper, four new selection formulas are presented that can accept a solution that is worse than the current best solution. The formula is used to increase the possibility of escaping from the local optimal solution. The computational results show that the MDE outperformed the original DE in all tested instances. The benefit of using real-time decision-making is that it can increase the company’s profit by 5.90% to 6.42%.

IroT/MavN Is a Legionella Transmembrane Fe(II) Transporter: Metal Selectivity and Translocation Kinetics Revealed by in Vitro Real-Time Transport.

In intravacuolar pathogens, iron is essential for growth and virulence. In Legionella pneumophila, a putative transmembrane protein inserted on the surface of the host pathogen-containing vacuole, IroT/MavN, facilitates intravacuolar iron acquisition from the host by an unknown mechanism, bypassing the problem of Fe(III) insolubility and mobilization. We developed a platform for purification and reconstitution of IroT in artificial lipid bilayer vesicles (proteoliposomes). By encapsulating the fluorescent reporter probe Fluozin-3, we reveal, by real-time metal transport assays, that IroT is a high-affinity iron transporter selective for Fe(II) over other essential transition metals. Mutational analysis reveals important residues in the transmembrane helices, soluble domains, and loops important for substrate recognition and translocation. The work establishes the substrate transport properties in a novel transporter family important for iron acquisition at the host-pathogen intravacuolar interface and provides chemical tools for a comparative investigation of the translocation properties in other iron transporter families.