## 11 Hardware and Testbeds
### Camera Motes
The design is intended to be low-cost low-power multimedia sensor node that can support dynamic reconfiguration capabilities and local processing for multimedia content, such as back ground subtraction, image recognition and classification, before wireless transmission. An optimized hardware-oriented support vector machine-like (SVM-like) algorithm called ERSVM is used for image classification process. The devise uses a System on Chip (SoC) for the processing engine, ATMEL FPSLIC, which includes AVR 8-bit RISC MCU, 40K gates FPGA, and 36 KB SRAM. An external 1 MB SRAM is also added to provide the required memory resources for multimedia processing and enable parallelized computation between hardware and software. With these specifications, the device targets a power budget of 500 mW and supports people detection at 15 fps at QVGA (320 \(\times\) 240) image resolution. For wireless communication, a 100 m LMX9820A Bluetooth transceiver has been used because of its low power of consumption, the ease to interface MicrelEye with other devices, and its high data rates (up to 704 kbps). In Video processing algorithm, the FPGA starts the process by acquiring frames from the image sensor, and then a back ground subtraction is done on each acquired frame. After that the region of interest (ROI), 128 \(\times\) 64 subimage, is extracted and stored into on-chip memory to be processed by MCU. The MCU will conduct on the ROI a feature extraction to form the feature vector and image classification using ERSVM algorithm. A camera more called CITRIC is developed in [95] for wireless multimedia sensor networks to enable in-network processing of images in order to reduce communication overheads. The hardware design of the camera platform consists of camera sensor, PDA class processor, 64 MB RAM, 16 MB Flash, and microphone. The camera sensor is a 1.3 megapixel OmniVision OV9655 camera that can support different image resolution -from SXGA (1280 \(\times\) 1024) through VGA, CIF to 40 \(\times\) 30- outputting 8 bit/10 bit images at a rate of 30 fps in VGA and lower resolution and typically consumes 90 mW in active state. The processor is PXA270 frequency-scalable (up to 624 MHz) fixed-point, and it has 256 KB internal SRAM and a wireless MMX coprocessor to accelerate multimedia operations. Then this camera device is connected to standard sensor network more (Tmote Sky) to form the wireless camera mote, CITRIC, which communicates over the IEEE 802.15.4 protocol at a rate of 250 Kbps. The camera mote first performs pre-processing functions on the captured images from the camera sensor and then sends the results over the network to a central server. Also the paper proposes a back-end client/server architecture to provide user interface to the system and support further centralized image processing. The authors implement three applications over the proposed platform which are image compression, target tracking, and camera localization. In image compression application, it is shown that Compressed Sensing (CS) using random matrices provides unique advantages in lossy compression than JPEG standard when both are implemented using the integer DCT implementation that is supported by the fixed point arithmetic processor. The single target tracking application is implemented via background subtraction using frame differencing. Then the foreground pixels are processed for identification and tracking. An implementation of Dual-Camera sensor is presented in [103]. Each of them comprises of a low-power and high-power tier and they are physically connected together to have similar FoV. The low-power camera sensor node (Tier-1) consists of a MICAz more equipped with a low fidelity Cyclops camera sensor, and a 1 GB NAND flash for storing images. The high-power camera sensor node (Tier-2) consists of a more-powerful platform, imote2 equipped with a high fidelity Enalab camera (OV7649 CMOS camera supports color VGA (640 \(\times\) 480) resolution), and a 1 GB SD card for image storage.