RPM real time performance management

This paper gives an overview of the basic architectural migration steps from current Intelligent Network (IN) based service platforms toward emerging Telecommunications Information Networking Architecture (TINA) based service environments. This evolution can be regarded as a stepwise replacement of IN functional entities by means of a set of appropriate TINA computational objects realised on top of a Distributed Processing Environment (DPE). Hence in the course of evolution, a service platform may comprise two parts; a still IN-conformant part and an already TINA-conformant part. This raises the need for the definition of appropriate adaptation units, allowing for example an IN Service Switching Function to interwork with TINA objects implementing the service control and management logic. Whereas the upper part of the adaptation concentrates on the mapping of concrete IN Application Protocol operations onto appropriate method invocations of TINA computational objects, on the bottom this adaptation has to deal with the interworking between the Signalling System No. 7 network protocols, and inter-DPE-node protocols.

TINA – Architectural basis for future telecommunications services

This paper proposes a model of agricultural product purchase intention based on live broadcast AI and recommendation algorithm (LiveRec). By combining AI behavior analysis in live broadcast with traditional recommendation algorithm, this model can adjust the recommendation strategy in real time according to the user's viewing behavior, interaction, comments and other information, thereby improving the user's purchase intention. This paper makes use of the live broadcast data from the e-commerce platform of agricultural products, including user behavior data and sales records, to evaluate the effectiveness of the model in the forecast of buying intention and the exchange rate. Experiments show that the precision of the prediction is increased by 18 %, and the conversion ratio is 15 % higher than that of conventional logistic regression and SVM. In particular, the model has good adaptability and real time performance in the user's personalized recommendation. The study indicates that the model based on live AI and recommended algorithm can increase the sales efficiency of agriculture products, and it will have a bright future. Future research will further optimize the model's real time performance and processing efficiency, and extend its application to other electronic commerce.

Hubs are critical elements of telecommunication and transportation networks because they play a vital role in mass traffic movement. The design of more reliable networks in hub‐and‐spoke systems is a critical issue because current networks, particularly many commercial Internet backbones, are quite vulnerable. In hub‐and‐spoke‐type topologies, any malfunction at a hub may cause degradation of the entire network's ability to transfer flows. This article presents a new hub location problem, termed thereliable p‐hub location problem, which focuses on maximizing network performance in terms of reliability by locating hubs for delivering flows among city nodes. Two submodels, thep‐hub maximum reliabilitymodel and thep‐hub mandatory dispersionmodel, are formulated. Based on hypothetical and empirical analyses using telecommunication networks in the United States, the relationship between network performance and hub facility locations is explored. The results from these models could give useful insights into telecommunication network design.

In this paper, real time performances of process level networks based on IEC61850 SCSM (Specific Communication Service Mapping) Model are analyzed. IEC61850 SCSM Model which is different from Ethernet OSI (Open System Interconnect) Model of Ethernet is introduced by IEC61850. Firstly, dominating factors of real time performances of process level networks are studied. Then IEC61850 SCSM Model is amplified in detail by comparing OSI Model and how to build a IEC61850 SCSM Model with OPNET Modeler software is interpreted. The paper architects the modeling of IEC61850 SCSM using OPNET Modeler software, studies the real-time performances of process level networks based on IEC61850 SCSM model, and analyses the real time capacity of adopting different network structures and different transmission strategies in the process level networks based on IEC61850 SCSM model.

The paper introduces NAVNEX (Navigation and Neural Explorer), a hybrid system, neural and symbolic/procedural, well suited for real time extraction of navigation situations during incremental explorations of unknown environments performed by an autonomous mobile robot. Real time performances are obtained through the parallelization of the activities necessary to extract relevant information: reactive planning and navigation, environment reconstruction via self-organization, symbolic (topological) analysis of reconstructed maps. The neural part of the system is based on two self-organizing neural networks which learn the obstacle boundaries and the free space maps, while the symbolic/procedural part is composed of a library of heuristic algorithms which analyze the neural maps on a topological bases, playing the role of situation recognizers. An incremental merging algorithm is also presented, which integrates in real time the spatio-temporal information of reconstructed graphs of navigation situations. >

Conducting (big) data analytics in an organization is not just about using a processing framework (e.g. Hadoop/Spark) to learn a model from data currently in a single file system (e.g. HDFS). We frequently need to pipeline real time data from other systems into the processing framework, and continually update the learned model. The processing frameworks need to be easily invokable for different purposes to produce different models. The model and the subsequent model updates need to be integrated with a product that may require a real time prediction using the latest trained model. All these need to be shared among different teams in the organization for different data analytics purposes. In this paper, we propose a real time data-analytics-as-service architecture that uses RESTful web services to wrap and integrate data services, dynamic model training services (supported by big data processing framework), prediction services and the product that uses the models. We discuss the challenges in wrapping big data processing frameworks as services and other architecturally significant factors that affect system reliability, real time performance and prediction accuracy. We evaluate our architecture using a log-driven system operation anomaly detection system where staleness of data used in model training, speed of model update and prediction are critical requirements.

WEB, the Wind Evaluation Breadboard, is an Extremely Large Telescope Primary Mirror simulator, developed with the aim of quantifying the ability of a segmented primary mirror to cope with wind disturbances. This instrument supported by the European Community (Framework Programme 6, ELT Design Study), is developed by ESO, IAC, MEDIA-ALTRAN, JUPASA and FOGALE. The WEB is a bench of about 20 tons and 7 meter diameter emulating a segmented primary mirror and its cell, with 7 hexagonal segments simulators, including electromechanical support systems. In this paper we present the WEB central control electronics and the software development which has to interface with: position actuators, auxiliary slave actuators, edge sensors, azimuth ring, elevation actuator, meteorological station and air balloons enclosure. The set of subsystems to control is a reduced version of a real telescope segmented primary mirror control system with high real time performance but emphasizing on development time efficiency and flexibility, because WEB is a test bench. The paper includes a detailed description of hardware and software, paying special attention to real time performance. The Hardware is composed of three computers and the Software architecture has been divided in three intercommunicated applications and they have been implemented using Labview over Windows XP and Pharlap ETS real time operating system. The edge sensors and position actuators close loop has a sampling and commanding frequency of 1KHz.

Numerous industries, including defense, logistics, surveillance, agriculture, and entertainment, have undergone radical change as a result of the quick development of Unmanned Aerial Vehicles (UAVs), also referred to as drones. However, the increasing deployment of drones has introduced significant challenges in ensuring airspace security, privacy, and safety, particularly due to their resemblance to birds and other aerial objects when viewed from a distance. Accurate and real time detection and classification of drones have thus become critical to mitigating threats such as unauthorized surveillance, espionage, and potential attacks. Using cutting edge deep learning techniques, this study suggests a reliable drone identification and classification system that makes use of the YOLOv8 (You Only Look Once version 8) object detection algorithm. YOLOv8 is notable for striking a compromise between accuracy and real time performance, which makes it ideal for dynamic settings. An extensive and varied collection of drone photos including fixed wing, single rotor, and multi rotor drones was cited, and the PASCAL VOC format was used for annotation. To guarantee the best possible model performance, the dataset was preprocessed, enhanced, and divided into training, validation, and test sets. To enable users to upload photos or stream videos for real time drone identification, the trained YOLOv8 model was implemented in a Flask based web application.

In this paper, we propose a new lossy image compression technique suitable for computationally challenged platforms. Extensive development in moving platforms create need for encoding images in real time with less computational resources. Conventional compression algorithms have potential to address this problem. However, the reconstruction accuracy of conventional encoders does not match that of deep learning based compression algorithms. In this paper, we have utilized best of both worlds by proposing a new compression method which combines conventional and deep learning based methods to sustain real time transmission and as well good reconstruction quality. We have validated our algorithm across a varied set of test images from EPFL mini drone dataset and Stanford drone dataset. The proposed algorithm exhibits better rate-distortion performance than conventional method. More importantly, our algorithm gives real time performance which has been substantiated by displaying a dramatic improvement in speed as against state-of-the-art deep learning compression method.

SummaryWith the rapid development of artificial intelligence, the intelligent distribution network can effectively improve the overall power supply quality of the distribution system. The designed distributed power has been able to achieve an optimal operation of grid connection, while the high‐speed operation of distribution equipment can achieve effective interaction with users. At present, intelligent distribution network is widely studied and applied in power industry because of its advantages of high efficiency energy and environmental protection. Because of its unique characteristics, such as low cost and fast speed, wireless sensor network is cleverly applied in intelligent distribution network. However, wireless sensor network has some defects in energy utilization due to its large range and too many nodes. This paper presents a cross‐layer cooperative control model, which can control the real time and reliability of network power data communication through stack protocol layer. This method can learn the weight value adaptively by training the influence relationship between the parameters of each protocol layer and achieve real‐time and reliable data communication by adjusting the policy and parameters of each layer in real time. This method takes the parameters of each wireless sensor node as the unit to establish the influence relationship between each other so as to realize the connection between each node of wireless sensor network and then to control the wireless sensor network as a whole to ensure the real‐time reliability of data communication in intelligent distribution network. In this paper, a lot of simulation experiments have been done, and the results show that the proposed cross‐layer cooperative control model can control the whole wireless sensor network to achieve efficient data transmission capacity of intelligent distribution wireless sensor network.

Advance Traveller Information Systems (ATIS) are considered a promising tool to alleviate traffic congestion and improve road network performance. They provide real time traffic information and route recommendation to road users, in order to increase their ability to choose the best alternative path. Though such systems have reached a high technical standard, their actual impact in traffic pattern and network performance is controversial. The methodology used is based on a Multi Agent Simulation to model how the presence of information influences the driver's reactive behavior and the network efficiency. The case study is the well known network of the Braess' paradox and the specific aim is to find the proper route recommendation strategy to avoid that adding a new road to traffic network may result in increasing the total travel time. Through a software platform able to simulate a virtual road network, where single drivers interact with each other and with the spatial environment according to a defined behavior, that is their reaction to external outputs, two behavioral patterns will be simultaneously considered. The first refers to the driver's path choice among those available for a fixed origin-destination pair; the second refers, once the path is chosen, to the microscopic motion of each vehicle as a function of the leader vehicle along each link of the network. To simulate the presence of drivers equipped with ATIS system and drivers who are not, or equivalently to simulate different reactive behavior to the information provided, it has been used a variable “probability of feedback”. Pattern arrival vehicle flow can be varied together with speed and acceleration of the vehicles. The general purpose of the paper is to contribute to the analysis of the impact of ITS (Intelligent Transport Systems) technology in traffic pattern and network performance. The specific objective is modelling driver's behavior in road networks when real time traffic information is provided. The results show that a proper rate of provided information is able to reduce the effect of the Braess' paradox and that network performance increases when drivers' behavior is affected by their ability to see local traffic conditions.

In this paper, the implementation of a DSP-based for DVB-H is presented. The gateway allows to extract a program (audio and video) included in a DVB-H compliant stream and transmit it in RTP/UDP packets through an Ethernet network. Moreover, to supervise the system operation, the audio and video streams of the selected program are decoded and presented in an LCD monitor. Real time performance has been achieved using about 20% of CPU for retransmit and decode one video service.

A frame store display station capable of generating real time animation effects using filled polygonal shapes is described. The system consists of a host microcomputer, a high performance frame store display controller and a microprogrammed graphic command interpreter. Real time performance is attained using a combination of software, hardware and a systems approach. The display station can be used to interactively test and view simple animation sequences. Some proposals for further increasing performance are discussed.

Infrared and optical remote sensing imagery plays a critical role in environmental applications such as wildfire detection, urban thermal profiling, climate monitoring, and sustainable resource management. Nevertheless, sensor noise, low contrast, motion blur, and atmospheric clutter usually affect the utility of such data, reducing its usefulness in real-time environmental monitoring and intelligent visual analytics. To address these challenges, the paper suggests a new adaptive statistical quality profiling and refinement architecture with future functionality in real time work of infrared and optical satellite images. The proposed framework incorporates the modeling of the temporal-spatial consistency with adaptive histogram-based statistical assessments and edge-preserving denoising techniques to reconstruct degraded scenes and deliver high-fidelity outcomes. The system can be used as a diagnostic layer in intelligent visual analytics pipelines as well as an enhancement module by incorporating both perceptual quality measures and statistical measures. Experiments on Sentinel and Landsat images indicate that the quantitative metrics are significantly enhanced: PSNR was improved to 29.8 dB and 32.3 dB when using an IR data and 24.1 dB and 26.5 dB when using an RGB optical data respectively. Similarly, SSIM, BRISque and NIQE increased by over 30 meaning that there was an improvement in perceptions. The framework not only is rapid in image fidelity, but also satisfies near real time performance, and can sustain throughput of up to 25 fps with GPU-accelerated inference and maintains the mean latency low, at less than 50 ms. These findings support the scalability of the given approach to the edge deployment within the process of satellite and ground-station. In addition to technical efficiency, the enhancements have a direct positive impact on environmental monitoring, making it possible to track the location of the hotspots of wildfires much better, haze less effectively to analyze air quality, and profile urban heat islands. In general, the research paper reflects a viable and scalable quality diagnostics and improvement system that facilitates sustainable environmental monitoring, smart visual analytics, and real-time Earth observation decision-making.