Programmable Networks – From Software Defined Radio to Software Defined Networking. Abstract. Current implementations of Internet systems are very hard to be upgraded. The ossification of existing standards restricts the development of more advanced communication systems. New research initiatives, such as virtualization, software- defined radios, and software- defined networks, allow more flexibility for networks. However, until now, those initiatives have been developed individually. We advocate that the convergence of these overlying and complementary technologies can expand the amount of programmability on the network and support different innovative applications. Hence, this paper surveys the most recent research initiatives on programmable networks. We characterize programmable networks, where programmable devices execute specific code, and the network is separated into three planes: data, control, and management planes. We discuss the modern programmable network architectures, emphasizing their research issues, and, when possible, highlight their practical implementations. We survey the wireless and wired elements on the programmable data plane. Next, on the programmable control plane, we survey the divisor and controller elements. We conclude with final considerations, open issues and future challenges. Beagle bone based projects list anil durgam. Embedded system(Robotics)1. Beagle. Phone. Beagle. Phone is experimental cape for developing analog. Vo. IP gateway. This cape is based on Silicon Labs Si. Pro. Slic. The Pro. SLIC is a low- voltage CMOS device that provides a complete. CPE). applications. Beagle. Phone is chip- compatible with Asterisk PBX and can be. This project is still under developing. Planned features: FXO version of the cape. Overvoltage/overcurrent protection. Asterisk drivers. Traffic light controller. Streaming video beaglebone black. Software Defined Radio onField Programmable Gate Array Karel L STERCKX. architecture Design Software. Importing Open-BTS Software on Beagle Board. In this video, you look at video streaming (3. Beaglebone black. To realise this c language project i used: v. JPEG, UDP protocol transfer, open. CV image show. 4. Autonomous Solar Car. The aim of this project is to build a solar. Resistive matching has been employed to extract. This is done in. order to maximize the amount of energy that is transferred from the solar cell. Li- ion battery. This battery is used to drive an H- bridge motor. Software Defined Network ArchitectureBeagle. Bone. 5. Building a Home Security System with Beagle. Bone. ne of the best kept secrets of the security industry is just. · Welcome! BeagleBoard.org hopes to be accepted as a mentoring organization in the Google Summer of Code for 2015! Below, we've collected project ideas for. Implementing the receiver of the Open Digital mobile radio on the Beagle board. Software-defined radio on an embedded. - Design the GPP software architecture. - - A new web SDR in New Zealand (https://forums.radioreference.com/software-defined-radio/329778-new-web-sdr-new-zealand.html). Beagle. Bone has all the. Security companies make a fortune each year by charging exorbitant fees. You will learn how easy it is to make an alarm system with. Beagle. Bone Home Automation. Home automation lets you control daily activities such as. Beagle. Bone is a low- cost, high- expansion. Beagle. Board. It is small and comes with the. ARM capabilities you expect from a Beagle. Board. Beagle. Bone. Linux to places it has never gone before. Starting with the absolute basics, Beagle. Bone Home Automation. Internet- age home. This book will show you how to set up Linux on Beagle. Bone. You will learn how to use Python to control different electronic components and. This book starts with the very basics of Linux administration. You will learn the basics. The "hardware jargon" is. Network programming is also a big part of this book, as the. Internet through a smartphone. You will also learn how to create a fully working Android. Application of image processing techniques for the extraction of vehicle. ARM target board. Correct recognition of vehicles violating traffic rules and. Automatic Number Plate Recognition (ANPR) systems plays an important role in. ANPR systems are typically standalone systems. The. limited processing capabilities hinders the efficient determination of location. This paper. presents an adopted method for the image preprocessing, image detection and. Application. of suitable Gaussian Filters along with other image preprocessing techniques. Binary jumps and Image differencing techniques have been carried out in this. An ARMCortex. A8 processor with 1. GHz processor and 5. MB RAM has been. selected as the candidate embedded processor in this work. Experiments show. Design of multi- threaded real time embedded video acquisition system from. IP cameras have become the de facto imaging sensor used for. Video Compression schemes are adopted for efficient. Currently H. 2. 64 is the. Logging of. the videos from the imaging sensors is the fundamental responsibility of these. This paper presents an efficient method for the multi. H. 2. 64 encoded video stream from these imaging sensors. An embedded implementation of the proposed method have also been carried out on. ARMCortex. A8 target. The embedded system developed possesses the. IP Cameras. while handling the real time processing constraints. A cloud computing- based image codec system for lossless compression of. Cortex- A8 platform. Aimed to provide an efficient architecture for the storage. In addition, with the highly increased computational ability of. ARM- based Cortex- A8 embedded platform is applied in the proposed. All the images generated are first sent to. On the other hand, images are retrieved and first sent to the. In the proposed system, the decompressed images are. HTTP, which means only a browser is needed for. Moreover, the client can perform image processing, e. As no other application. Furthermore, the. The cloud. computing- based architecture in conjunction with the high efficient coding. The Challenge of Testing the ARM CORTEX- A8/sup TM/ Microprocessor Core. The DFT and test challenges faced, and the solutions. Cortex- A8. microprocessor core are described in this paper. New DFT techniques have been. DFT core solution that. This core comprises. HMHP) and custom blocks. A DFT solution. had to be created that could be utilized by a multitude of customers. Temperature controlling system for LED lighting based on ARM. Since LEDs are generally considered to be an important. ARM- based LED. intelligent lighting system was provided in this paper. According to the. Based on. the ARMCortex- M0. LEDs. The. testing results of the physical prototype show that the designed system works. Thus, the preliminary research proves the possibilities of smart. Smart home design based on Zig. Bee wireless sensor network. Internet of Things and Zig. Bee. wireless sensor network technology. A kind of smart home design based on Zig. Bee. wireless sensor network was proposed in this paper. Texas Instruments MCU. LM3. S9. B9. 6, which is the ARMCortex- M3 based controllers, was used in this system. The. entire system is running on the μC/OS- II embedded real- time multitasking. Users can access this system by a dynamic webpage of Lw. IP. TCP/IP protocol stack or GSM SMS. Using this system users can conveniently know. Zig. Bee wireless sensor network. On- line monitoring system of insulator leakage current based on ARM. A ceramic insulator has an excellent anti- pollution. This paper analyzes the formation mechanism of flashover and. RMS of leakage current(LC) and discharge pulses reflect. This shows that to a large extent the. Existing monitoring device for contaminated. In this paper, a novel low- power and low- cost online. ARMCortex- M and Zigbee wireless network. In addition. the monitoring system not only acquires the leakage current and discharge. After that the results will be displayed on the LCD or sent to the. Zigbee network. Tests show that the monitoring. Brain EEG signal processing for controlling a robotic. Researchers recently proposed new scientific methods for. Brain- Machine Interface (BMI). This paper presents a Brain Machine. Interface (BMI) system based on using the brain electroencephalography (EEG). Signals recorded from one subject using Emotive Epoc. Four channels only were used, in our experiment, AF3, which located at. F7, F3, FC5 which located. Three different techniques were used for features extraction which are: Wavelet. Transform (WT), Fast Fourier Transform (FFT) and Principal Component Analysis. PCA). Multi- layer Perceptron Neural Network trained by a standard back. Classification rates of 9. Experimental results show. Implementation of a Web of Things based Smart Grid to. Renewable Energy Sources. This paper describes a Smart Grid architecture implemented. Web of Things. Web of Things comprise of a set of Web services. Internet enabled Embedded devices. The Web. browser on any computer can act as an interface to the services provided by. Web of Things. The Embedded devices are ARMCortex M3 Processor based devices with Ethernet. CMSIS Real Time Operating System is used for process control on. Lw. IP Protocol Stack is implemented on top of. IP connectivity can be established. The Web. interfaces provide us real time information on each of the energy meters that. Embedded Internet devices using. MODBUS communication protocol. Real Time energy source scheduling, energy. The Embedded Systems lab. Infrastructure at the TIFAC CORE for 3. G/4. G Communication at National Institute. Science and Technology was used for the hardware testing of the embedded. We were greatly helped by the Software developers at NIST Technology. Consultancy Services in designing the web applications and interfaces for our. Web of Things architecture. Embedded System for Biometric Online Signature. This paper describes the implementation on. FPGAs) of an embedded system for online. The recognition algorithm mainly consists of three stages. First, an initial preprocessing is applied on the captured signature, removing. Afterwards, a dynamic time warping algorithm is used to align this processed. Finally, a set of. Gaussian Mixture Model, which. The algorithm was. The implemented system consists of a. VFPU), specifically designed for accelerating the. Moreover, the. proposed architecture also includes a microprocessor, which interacts with the. VFPU, and executes by software the rest of the online signature verification.
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