Video game consoles share many of the characteristics of an ideal device for use in enterprise deployments. In comparison to many desktop and notebook PCs available in the market, modern video game consoles are actually quite powerful and capable. They provide an excellent user experience with simple and intuitive setup and operation. At the heart of the design of many modern video game consoles is security; they are remarkably resilient against very sophisticated hardware and software attacks. They are also rather cost-effective in comparison to modern PCs.

Increasing share of renewables poses new challenges towards planning and operation of power systems all over the world, thereby, requiring new methods and tools. This article explains the value of balancing tool chain which encompasses operation of market and power system from day-ahead planning until the real-time dynamic simulation. The salient features for such a balancing tool chain is discussed in this article. A Balancing Tool Chain has been developed and validated based on balancing principles of Denmark and Europe. The efficacy of the balancing tool chain is demonstrated through very detailed case studies of massive penetration of offshore wind power in North Sea countries towards 2050. Using detailed case studies, recommendations are provided towards balancing of North Sea countries in terms of operational and balancing practices as well as dimensioning of reserves for future scenarios.

In this paper, we investigate the transmission probabilities in three cases (depending only on the legitimate receiver, depending only the eavesdropper, and depending on both legitimate receiver and eavesdropper) in quantum key distribution (QKD) systems over free-space optical links. To be more realistic, we consider a generalized pointing error scenario, where the azimuth and elevation pointing error angles caused by stochastic jitters and vibrations in the legitimate receiver platform are independently distributed according to a non-identical normal distribution. Taking these assumptions into account, we derive approximate expressions of transmission probabilities by using the Gaussian quadrature method. To simplify the expressions and get some physical insights, some asymptotic analysis on the transmission probabilities is presented based on asymptotic expression for the generalized Marcum Q-function when the telescope gain at the legitimate receiver approaches to infinity. Moreover, from the asymptotic expression for the generalized Marcum Q-function, the asymptotic outage probability over Beckmann fading channels (a general channel model including Rayleigh, Rice, and Hoyt fading channels) can be also easily derived when the average signal-to-noise ratio is sufficiently large, which shows the diversity order and array gain.

Satellite image time series (SITS) classification is a major research topic in remote sensing and is relevant for a wide range of applications. Deep learning approaches have been commonly employed for SITS classification and have provided state-of-the-art performance. However, deep learning methods suffer from overfitting when labeled data is scarce. To address this problem, we propose a novel self-supervised pre-training scheme to initialize a Transformer-based network by utilizing large-scale unlabeled data. In detail, the model is asked to predict randomly contaminated observations given an entire time series of a pixel. The main idea of our proposal is to leverage the inherent temporal structure of satellite time series to learn general-purpose spectral-temporal representations related to land cover semantics. Once pre-training is completed, the pre-trained network can be further adapted to various SITS classification tasks by fine-tuning all the model parameters on small-scale task-related labeled data. In this way, the general knowledge and representations about SITS can be transferred to a label-scarce task, thereby improving the generalization performance of the model as well as reducing the risk of overfitting. Comprehensive experiments have been carried out on three benchmark datasets over large study areas. Experimental results demonstrate the effectiveness of the proposed method, leading to a classification accuracy increment up to 2.38% to 5.27%. The code and the pre-trained model will be available at https://github.com/linlei1214/SITS-BERT upon publication. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

Concerns for spectrum congestion have spurred extensive research efforts on efficient spectrum management. Therefore, devising schemes for spectrum sharing between radar and wireless communication systems has become an important area of research. Joint communications-radar (JCR) system is among the several approaches proposed to achieve this objective. In JCR systems, additional components and processes are added to an existing standardized communication platform to enable radar functions. Moreover, the communication waveform is used as an integrated JCR waveform, i.e., the same signal is used to communicate information to a receiver and to perform radar detection and estimation operations for a nearby target. The most common application of JCR systems is found in vehicle-to-vehicle (V2V) communication scenario. In this article, an overview of the spectrum sharing methods is presented, with focus on JCR systems in automotive applications. We first review the recent works on IEEE 802.11p- and IEEE 802.11ad-based radars. A basic description of the modeling of JCR system and channels is presented, followed by discussions on main components and processes employed in various JCR systems. Mainly, we are interested in how the radar detection and estimation functions are performed in conjunction with the communication receiver functions with minimal alterations in the existing system. At the end of the paper, some performance trade-offs between the communication and radar sub-systems are also discussed.

This paper deals with the bit error rate (BER) of LoRa signal over Rayleigh and Rice channels. Exact analytical expressions of the BER in LoRa systems have been proposed in the literature, but they are not tractable and raise problems of computation precision. We then hereby suggest asymptotic expressions of the BER, which are at the same time tractable and accurate over a wide BER range. source code: https://github.com/b-com/ber_LoRa

A document by vahid haghighatdoost . Click on the document to view its contents.

Microwave imaging (MWI) systems are usually enclosed within casings, e.g., in order to contain the utilized coupling liquid or to help mount the antenna system. On the other hand, inverse scattering algorithms, which are used to process the measured microwave scattering data, often assume that the background medium of the imaging system extends to infinity (i.e., unbounded background medium assumption). Thus, they do not consider the reflections occurring at the system enclosure. For such algorithms to yield successful images, these reflections need to be minimized, e.g., via the use of a lossy coupling liquid. As an alternative to a lossy background medium which also reduces the desired signal level, this paper investigates the use of metallic-backed absorbing metasurfaces as the MWI system enclosure in order to (i) reduce these reflections, and also (ii) to shield the MWI system from external interference. Using simulated data, we then show that standard inverse scattering algorithms, employing the free-space assumption, can successfully process the data collected under the metasurface enclosure and yield acceptable permittivity images. The advantages and disadvantages of absorbing metasurface enclosure, along with the limitations of this study, will also be discussed. Finally, an absorbing metasurface is fabricated and its reflectivity is experimentally evaluated.

To satisfy the needs of market customization, the traditional manufacturing is gradually transforming and upgrading into the intelligent manufacturing. In mass customization (MC), the assembly operations of modular products tend to be organized as the form of flexible mixed-model assembly line (FMMAL). The dynamic scheduling problem of FMMAL is quite complex with three issues of the product sequencing, station allocation and material delivery. At the same time, the disruption events of station failure, product inserting and product reworking are also considered. To solve this problem, a comprehensive framework combining the architecture of Internet of Manufacturing Things (IoMT) with the dynamic scheduling algorithms is proposed. Firstly, the IoMT-based FMMAL are constructed via the multi-agent system (MAS) and ubiquitous environment. Secondly, a mathematical model of FMMAL are formulated with the decision variables, optimization objectives and constraint conditions. Thirdly, the IoMT-oriented algorithms for dynamic scheduling are proposed including the fuzzy analytic hierarchy process (FAHP) for normalization, weighted sum of properties-improved genetic algorithm (WSP-IGA) for prescheduling, priority weights search-simulated annealing (PWS-SA) for rescheduling. Lastly, a discrete event simulation of a numerical case is conducted to demonstrate the practicality and validity of proposed theories and algorithms. The results show that the proposed hyper-heuristics (WSP-IGA and PWS-SA) can respectively realize the prescheduling and rescheduling of FMMAL in four modes including the synthesized mode, time-efficient mode, just-in-time mode and energy-saving mode, which are superior to the four referenced meta-heuristics.

Low power ESO based active disturbance rejection control (LPADRC) was proposed and the performance recovery was analyzed.

This work is part of an innovative e-learning project allowing the development of an advanced digital educational tool that provides feedback during the process of learning handwriting for young school children (three to eight years old). In this paper, we describe a new method for children handwriting quality analysis. It automatically detects mistakes, gives real-time on-line feedback for children’s writing, and helps teachers comprehend and evaluate children’s writing skills. The proposed method adjudges five main criteria: shape, direction, stroke order, position respect to the reference lines, and kinematics of the trace. It analyzes the handwriting quality and automatically gives feedback based on the combination of three extracted models: Beta-Elliptic Model (BEM) using similarity detection (SD) and dissimilarity distance (DD) measure, Fourier Descriptor Model (FDM), and perceptive Convolutional Neural Network (CNN) with Support Vector Machine (SVM) comparison engine. The originality of our work lies partly in the system architecture which apprehends complementary dynamic, geometric, and visual representation of the examined handwritten scripts and in the efficient selected features adapted to various handwriting styles and multiple script languages such as Arabic, Latin, digits, and symbol drawing. The application offers two interactive interfaces respectively dedicated to learners, educators, experts or teachers and allows them to adapt it easily to the specificity of their disciples. The evaluation of our framework is enhanced by a database collected in Tunisia primary school with 400 children. Experimental results show the efficiency and robustness of our suggested framework that helps teachers and children by offering positive feedback throughout the handwriting learning process using tactile digital devices.

This paper presents our technological approach for Externally Modulated Lasers (EMLs), based on Semi-Insulating Buried Heterostructure (SIBH) waveguide. We present the main EML design rules and compromises, then apply them to different EMLs aiming at major telecom and datacom applications.

Fuzzy Multi Criteria Decision is process of Selection and range, However they have different way to use it in Decisional Problem, our situation is to Select the best Zone Between the others looking the Criteria of Temperature, Socio Economic, Acclimatization, Population, to select the adequate Alternative by using The AHP Method proposed by Saaty [3], also, we have added the FAHP (Fuzzy AHP) Method, it’s for more precision and exactly result.

In recent years, Wireless Sensor Networks (WSNs) technologies are used in many important areas, including weather forecasting, security, environmental monitoring, health care, and industry. Nonetheless, due to the constraints of WSNs in terms of energy, processing, connectivity, computation and data integrity. An important issue in this research field is the efficient management of the large number of WSN’s data. Therefore, there is a need for strong and scalable high-performance computing and large storage infrastructure for real-time processing and storage of WSN’s data. This article attempts to discuss data collection methods in WSNs, sensor cloud, and IoT. Also, in this context, a general overview of WSNs and sensor cloud platforms, including their definitions, architectures, and applications are given. We investigate, highlight and report recent advances in data collection technology by categorizing and classifying data collection methods in WSNs, sensor cloud, IoT and developing a taxonomy based on these classifications. Furthermore, the analysis and synthesis of existing works in this research domain is highlighted. The identification of key research challenges for future work and use case in this research domain were also highlighted and discussed.

Pedestrian detection is considered one of the most challenging problems in computer vision, as it involves the combination of classification and localization within a scene. Recently, convolutional neural networks (CNNs) have been demonstrated to achieve superior detection results compared to traditional approaches. Although YOLOv3 (an improved You Only Look Once model) is proposed as one of state-of-the-art methods in CNN-based object detection, it remains very challenging to leverage this method for real-time pedestrian detection. In this paper, we propose a new framework called SA YOLOv3, a scale-aware You Only Look Once framework which improves YOLOv3 in improving pedestrian detection of small scale pedestrian instances in a real-time manner. Our network introduces two sub-networks which detect pedestrians of different scales. Outputs from the sub-networks are then combined to generate robust detection results. Experimental results show that the proposed SA YOLOv3 framework outperforms the results of YOLOv3 on public datasets and run at an average of 11 fps on a GPU.

Discontinuous reception (DRX), wherein a user equipment (UE) temporarily disables its receiver, is a critical power saving feature in modern cellular systems. DRX is likely to be aggressively used at mmWave and sub-THz frequencies due to the high front-end power consumption. A key challenge for DRX at these frequencies is blockage-induced link outages: A UE will likely need to track many directional links to ensure reliable multi-connectivity, thereby increasing the power consumption. In this paper, we explore reinforcement learning-based link tracking policies in connected mode DRX that reduce power consumption by tracking only a fraction of the available links, but without adversely affecting the outage and throughput performance. Through detailed, system level simulations at 28 GHz (5G) and 140 GHz (6G), we observe that even sub-optimal link tracking policies can achieve considerable power savings with relatively little degradation in outage and throughput performance, especially with digital beamforming at the UE. In particular, we show that it is feasible to reduce power consumption by 75% and still achieve up to 95% (80%) of the maximum throughput using digital beamforming at 28 GHz (140 GHz), subject to an outage probability of at most 1%.

As technology is advancing so house are also getting smarter. Modern houses are gradually shifting from convectional switches to centralized control system, involving remote control switches. Presently, convectional wall switches located in different parts of the house makes it difficult for user to go near them to operate. Even it becomes more difficult for elder people and for physically handicap people to do so. Internet of Things (IoT) conceptualizes the idea of remotely connecting and monitoring real world objects through the Internet. When it comes to our house, this concept can be aptly incorporated to make it smarter, safer and automated. It is a concept where each device is assign to an IP address and through that IP address anyone makes that device identifiable on internet. The Internet is an evolving entity. It started as the Internet of Computers. Research studies have forecast an explosive growth in the number of things or devices that will be connected to the Internet. The resulting network is called the Internet of Things (IoT). IoT is having the potential to change the lifestyle of peoples. In day today’s life, people prefer more of automatic systems rather than any manual systems. The major elements of IoT based home automation system are Raspberry pi and the Relay along with their driving circuitry. Home automation can be defined as a mechanism removing as much human interaction as technically possible and desirable in various domestic processes and replacing them with programmed electronic systems. This project is intended to construct a home automation system that uses any mobile device to control the home appliances. This home automation system is based on IoT. Home automation is very exciting field when it uses new technologies like Internet of Things. Raspberry pi is credit card size computer. Raspberry pi supports large number of peripherals. Raspberry pi is having different communication media like Ethernet port, HDMI port, USB port, Display Serial Interface, Camera Serial Interface, Bluetooth, Bluetooth low energy. It allows to control number of home appliances simultaneously.

This paper proposes a design flow for channel design by a fast hierarchical optimization method based on Channel Operating Margin (COM). Unlike the common design of experiment (DOE), the method performs the design by a hierarchical flow in the level of electrical characteristic while not the usual horizontal structure of physical parameters. It significantly reduces the number of huge samples used in DOE, and thus is able to offer a fast estimation approach for channel in the early design period. In each stage of the vertical flow, the electrical properties of component are extracted by 2.5D or 3D full wave simulator, and thus ensure the accuracy of the channel design. Design strategy for the most crucial discontinuity, Via, to achieve an impedance matching is addressed. Package effect on the COM is also discussed as a reference for adjusting the 3dB criterion of COM in the future.