Procurement Of Equipment For It Department Under Modrob Grant 2020-21 In Ait Workstation 1 • Workstation Hardware: - 1 x Intel Xeon 16 Core CPU, - 4 x 16GB DDR4 ECC RAM, - 1 x 2TB 2.5” SATA 6Gb/s 7.2K RPM HDD’s, - 1 x 240 GB 2.5” SATA 6Gb/s, SSD’s, - 1 x 800W SMPS Power supply , - Tower Mount Cabinet chassis - 18” Display compatible for workstation, USB Mouse & Keyboard • GPU: 1x 24GB NVIDIA GeForce RTX 3090 (Ampere Architecture) Workstation Hardware: - 1 x Intel Xeon 6 Core CPU - 4 x 16GB DDR4 ECC RAM, - 1 x 2TB 2.5” SATA 6Gb/s 7.2K RPM HDD’s, - 1 x 240 GB 2.5” SATA 6Gb/s, SSD’s, - 1 x 800W SMPS Power supply , - Tower Mount Cabinet chassis - 18” Display compatible for workstation, USB Mouse- Keyboard • GPU: 1x 24GB NVIDIA GeForce RTX 3090 (Ampere Architecture) Software Libraries & OS: The setup contains Ubuntu-18 OS with the following pre-installed libraries, utilities, tools and SDKs. Essentials Utilities: CUDA, cuDNN, TensorRT Machine Learning: vowpal wabbit, XGBoost,Numpy, Scikit, Pandas, other relevant py libs Deep Learning: NVidia DIGITS, Tensor Flow, Caffe, Caffe2, PyTorch, Torch, Theano Dataset: Image Net, CIFAR-10, KITTI pre-loaded for out-of-box development - Other: Tesseract(For OCR), Jarvis (for Voice interface) Deep Learning Inference Embedded Platform which is again used as edge computing setup comprises of 6-core NVIDIA Carmel ARMv8 CPU, 384-core Volta GPU, 8GB LPDDR4, 16GB eMMC(optional), 2x 4kp30 H.264/H.265 encoder & 2x 4kp60 H.264/H.265 decoder, MIPI CSI-2 lanes,2x PCIe controllers, Ports and Peripherals includes: 4x USB 3.1 A, USB 2.0 Micro B, 2x MIPI CSI-2, HDMI 2.0, DisplayPort 1.4, Gigabit Ethernet, M.2 Key-E with PCIe, M.2 Key-M NVMe with PCIe, MicroSD card slot, 2x I2C, 2x SPI, UART, I2S, GPIOs etc., 120GB SSD along with pre-loaded Linux OS having tools like OpenCV, Open GL, Vulkan, Tensor Flow, Tensor RT, CUDA, NVidia Vison Works etc... Eco System configured to work with GPU Board for Deep Learning Inference Eco System also configured to work with Advance Image/Video Processing applications 18” HDMI Display compatible for GPU board along with HDMI Cable, USB mouse & Keyboard Deep Learning Inference Embedded Platform Platform which is again used as edge computing setup comprises of Octal-core NVIDIA Carmel ARMv8.2 CPU @ 2.26GHz; 512-core Volta GPU @ with 64 Tensor Cores; Dual Deep Learning Accelerator (DLA) engines; 32GB 256-bit LPDDR4x @ 2133MHz (137GB/s); 32GB eMMC 5.1; Vision Accelerator engine; (4x) 4Kp60 H.264/H.265 video encoder; (2x) 8Kp30 / (6x) 4Kp60 H.265 video decoder Ports and Peripherals includes: 16x) MIPI CSI-2 lanes, (8x) SLVS-EC lanes; up to 6 active sensor streams and 36 virtual camera channels; (5x) PCIe Gen 4 controllers | 1x8, 1x4, 1x2, 2x1 ; (3x) Root Port & Endpoint; (2x) Root Port; (3x) USB 3.1 + (4x) USB 2.0; (3x) eDP 1.4 / DP 1.2 / HDMI 2.0 @ 4Kp60; 10/100/1000 BASE-T Ethernet + MAC + RGMII PHY; Dual CAN bus controller; UART, SPI, I2C, I2C, GPIOs 120GB SSD along with pre-loaded Linux OS having tools like OpenCV, Open GL, Vulkan, Tensor Flow, Tensor RT, Cuda, NVidia Vison Works etc... Eco System configured to work with GPU Board for Advance Image/Video Processing Eco System configured to work with GPU Board for Deep Learning Inference 18” HDMI Display compatible for GPU board along with HDMI Cable, USB Mouse & Keyboard Thermal camera (Qty = 1) • Frame Rate: 8.6 Hz • Pixel Size: 17 μm • Radiometric Accuracy: High gain: Greater of +/- 5°C or 5% (typical) Low gain: Greater of +/- 10°C or 10% (typical) • Scene Dynamic Range: -10-140 °C (high gain); up to 450°C (low gain) typical • Spectral Range: 8 μm to 14 μm • Thermal Sensitivity: 0.050° C • Non-Operating Temperature Range: -40 °C to +80 °C • Optimum Temperature Range: -10°C to +80°C • Array format: 80 × 60, progressive scan • FOV - Diagonal: 63.5° • FOV - Horizontal: 50° (nominal) • Thermal video over USB • GPIO and peripheral breakouts to easily attach other devices • Powered via USB • UART, I2C, and GPIO Expansion 3D- Stereo camera (Qty = 1) • Infrared (IR) camera resolution 512 × 424 pixels • RGB camera resolution 1920 × 1080 pixels • Field of view 70 × 60 degrees, 7x7 depth pixels per degree • Framerate 30 frames per second • Operative measuring range from 0.5 to 4.5 m • Recommended min. distance 1.4m • Recommended max. distance 4m • Microphone array 4 microphones, 48KHz • Object pixel size (GSD) between 1.4 mm (@ 0.5 m range) and 12 mm (@ 4.5 m range) Night vision camera (Qty = 1) • Interface USB • Image sensor CMOS • Lens 5P High quality lens • Video resolution 1920x1080 – 30 FPS IP camera – wireless (Qty = 1) • Resolution 1920 x1080 • Video Compression H.264 • FPS 15 • Wireless 802.11 b/g/n • Night Vision yes up to 30ft. USB camera (Qty = 1) • Resolution 720p/30FPS • Focus type fixed focus • Interface USB • Lens 5P High quality lens • Built-in mic Mono AIOT Lab Setup 5 x IOT Node - Cortex-M4 (Micro-Python Enabled) 1 x pre-configured AI Node, USB Camera, USB Mouse & Keyboard 5 x All in One General Purpose Board 1x IOT Gateway 1x Bluetooth 1x Router having wifi facility 1x Portable Sensor Kit/ wio node module 1x IOT sensor kit set (Details mentioned in specifications) 1x RFID module 1x Finger Print Sensor module 1x Stepper Motor 1x DC Motor 1x Solid State Relay Module (having 2 SSR) 1x Amazon Echo device 1x ESP32 LoRa SX1278 0.96 Inch Blue OLED Display BT WiFi Module for Arduino 1x ESP32-CAM WiFi Bluetooth Camera Module development Board 1x IDE Configured for Cortex Platform 1x Workbook (Softcopy) Specification: • Different variety of IOT Nodes featuring Five ARM Cortex-M4 with features like Ethernet, USB, Sensor interfacing, UART, I2C, SPI, GPIO interfacing connector etc. • One unit of Artificial Intelligent Node with pre-configured image, USB Camera, USB Mouse & Keyboard. • One unit of Embedded Gateway with HDMI and Ethernet connectivity, USB ports, on board Wi-Fi, on board Bluetooth. Quad Core 1.2GHz CortexA53 64bit CPU, 1 GB RAM. The embedded gateway should be able to connect to the nodes and transmit data to the cloud. The necessary image containing cloud services compatible for IoT should be ported on the board. Also the procedure to configure the same should be provided to end user. • Five unit of All-in-one GPIO board designed to suit the experimentation of IoT applications to featuring On-board 8 LED, 16x2 character LCD, 2 digit 7-segment display, 4 general purpose keys and 2X2 matrix keyboard, I2C and SPI based EEPROM, Stepper motor and DC Motor interface, Relay output, Facility to provide 2 channel ADC input using potentiometer and unity gain amplifier for protection • Voice enabled control using Amazon Alexa using Echo Devices. • Voice enabled control using Google Assistant Application on mobile device. • One unit of Router with power supply • A Bluetooth module for connecting the node to embedded gateway. • A portable sensor kit with facility to interface temperature-humidity sensor to log data on IOT gateway using Wi-Fi protocol. A set of sensors like IMU10DOF Sensor, Temperature & Humidity Sensor, Ultrasonic Sensor, Vibration Sensor, Moisture Sensor, Dust Sensor, Water Sensor, PIR Motion Sensor, Reed sensor, Touch Key Sensor etc… for sensing of data and posting it to cloud. The set of sensors should be compatible with nodes and should be provided with proper connectivity options like base board where the sensors can be mounted. The sensors should be compatible with I2C, SPI protocols etc. The sensors should be pluggable. The base board should have 34 pin connector for I2C, SPI, UART, PWM lines available as well as a 10 pin connector for ADC interface with the node. One unit of Project Sensor modules like RFID and Finger print sensor module each. 2KG Stepper motor and +5V DC motor for demonstration of cloud based control using IoT application Solid State Relay Module (having two SSR) ESP32 LoRa SX1278 0.96 Inch Blue OLED Display BT WiFi Module for Arduino. Supports Sniffer, Station, softAP and Wi-Fi Direct modes • The main chip using Lexin ESP32, Tensilica LX6 dual-core processor, clocked at 240MHz, computing power up to 600DMIPS, chip built-in 520 KB SRAM, 802.11 b / g / N HT40 Wi-Fi transceiver, baseband, protocol stack, and LWIP, integrated dual-mode Bluetooth (traditional Bluetooth and BLE low power Bluetooth). • Data rate: 150 Mbps@ 11n HT40,72 Mbps@ 11n HT20,54 Mbps @ 11g, 11 Mbps @ 11b; Transmit power: 19.5 dBm @ 11b, 16.5 dBm @ 11g, 15.5 dBm @ 11n; Receiver sensitivity up to – 98 dBm; UDP continues to throughput by 135 Mbps ESP32-CAM WiFi Bluetooth Camera Module development Board An IDE configured for IoT applications to be provided for entire lab. Softcopy for Workbook/ Manual featuring basic examples to get started with the target board as well as examples to use internet and
