{"id":2,"date":"2016-03-09T19:39:47","date_gmt":"2016-03-09T18:39:47","guid":{"rendered":"http:\/\/naolink.com\/?page_id=2"},"modified":"2020-12-04T01:13:26","modified_gmt":"2020-12-04T00:13:26","slug":"connectivity","status":"publish","type":"page","link":"https:\/\/naolink.com\/index.php\/connectivity\/","title":{"rendered":"SKILLS & EXPERTISE"},"content":{"rendered":"\n
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MACHINE LEARNING & DEEP LEARNING<\/span><\/strong><\/span><\/h3>\n\n\n\n
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Artificial Intelligence in IoT applications using GPU<\/strong>, TPU<\/strong>, Neural Network Accelerator<\/strong>.<\/p>\n\n\n\n

Development Flow with multi-Frameworks: Tensorflow\/Pytorch<\/strong>, MXNeT<\/strong>, Keras<\/strong>, Caffee<\/strong> \u2026 Manage Learning process.<\/p>\n\n\n\n

Modelling, combining, and optimizing mayers, Fine-tuned neural networks algorithms, for improved latency, throughput, power efficiency, and memory consumption.DAA<\/p>\n\n\n\n

Input DATA<\/strong>: Images & Video, Object recognition, Classification, Sensors signals.<\/p>\n\n\n\n

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WIRELESS CONNECTIVITY for IOT<\/span><\/strong><\/span><\/h3>\n\n\n\n
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Wireless telecommunication network designed to allow long range or short range communications at high rate among devices (IoT), such as sensors operated on a battery<\/p>\n\n\n\n

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Connected Objects (IoT) – Technologies and Implementation<\/p>\n\n\n\n

Radiofrequency and Connectivity, differents protocols and technologies depending of needs as rate, range, networking capabilities, interoperability and frequency (868Mhz, 900Mhz, 2.4Ghz, 3-5Ghz) :<\/p>\n\n\n\n

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Positioning of radio communication technologies according to capabilities<\/span><\/strong><\/figcaption><\/figure>\n\n\n\n
  • Short range: <\/strong> BT , BLE (Bluetooth low energy<\/em>), NFC, 802.15-4<\/li><\/ul>\n\n\n\n
    • Long Range<\/strong>: Sigfox, Lora, other LPWAN.<\/li><\/ul>\n\n\n\n
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      Major Wireless Techonologies<\/strong><\/span><\/figcaption><\/figure><\/div>\n\n\n\n
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      EMBEDDED SOFTWARE<\/span><\/strong><\/h3>\n\n\n\n
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      <\/p>\n\n\n\n

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      Prototype an IoT system, real-time and embedded Systems and Application<\/p>\n\n\n\n

      Knowledge of parallel embedded architectures and DSP processors and Signal Processing Algorithms<\/p>\n\n\n\n

      Vibration analysis application utilizing FFT and statistical analysis<\/p>\n\n\n\n

      Projects done in Machine Learning sensors in Motion and Computer Vision<\/p>\n\n\n\n

      C\/C+, Python, Java Programming, versioning\/revision control systems (GIT)<\/p>\n<\/div><\/div>\n\n\n\n

      <\/p>\n<\/div><\/div>\n\n\n\n

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      Bootloader and board support package development<\/p>\n\n\n\n

      Embedded device drivers for Ethernet, USB, SPI, I2C, UART, and more<\/p>\n\n\n\n

      RTOS development, integration, and porting<\/p>\n\n\n\n

      Network protocols (TCP\/IP, UDP)<\/p>\n\n\n\n

      MAC and PHY layer software for multiple wireless technologies<\/p>\n\n\n\n

      Digital video broadcast protocols and MPEG transport<\/p>\n<\/div><\/div>\n<\/div><\/div>\n<\/div><\/div>\n\n\n\n

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      HARDWARE DESIGN<\/strong><\/span><\/h3>\n\n\n\n
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      Electronics Design<\/strong><\/span><\/p>\n\n\n\n

      IoT ou M2M Digital and RF systems Design<\/p>\n\n\n\n

      System Prototyping on chips and FPGA or board level (Proof of Concept), VHDL Programming<\/p>\n\n\n\n

      Architectural issues, Modelisation, Simulation, Design methodology of HW\/SW partitioning and co-design.<\/p>\n\n\n\n

      Printed Circuit CAD<\/strong><\/span><\/p>\n\n\n\n

      Subcontracted manufacturing of PCB using short lead times<\/p>\n\n\n\n

      Assembly : prototypes, small batches with components supply<\/p>\n\n\n\n

      Products Design, Manufacturing and Testing.<\/p>\n\n\n\n

      \"\"<\/figure>\n<\/div><\/div>\n\n\n\n

      Complete set of PCB schematic editing tools,\u00a0 layout and library content and community-driven features.<\/p>\n","protected":false},"excerpt":{"rendered":"

      MACHINE LEARNING & DEEP LEARNING Artificial Intelligence in IoT applications using GPU, TPU, Neural Network Accelerator. Development Flow with multi-Frameworks: Tensorflow\/Pytorch, MXNeT, Keras, Caffee \u2026 Manage Learning process. Modelling, combining, and optimizing mayers, Fine-tuned neural networks algorithms, for improved latency, throughput, power efficiency, and memory consumption.DAA Input DATA: Images & Video, Object recognition, Classification, Sensors<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-2","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/naolink.com\/index.php\/wp-json\/wp\/v2\/pages\/2","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/naolink.com\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/naolink.com\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/naolink.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/naolink.com\/index.php\/wp-json\/wp\/v2\/comments?post=2"}],"version-history":[{"count":113,"href":"https:\/\/naolink.com\/index.php\/wp-json\/wp\/v2\/pages\/2\/revisions"}],"predecessor-version":[{"id":927,"href":"https:\/\/naolink.com\/index.php\/wp-json\/wp\/v2\/pages\/2\/revisions\/927"}],"wp:attachment":[{"href":"https:\/\/naolink.com\/index.php\/wp-json\/wp\/v2\/media?parent=2"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}