Electronics Project: IMPLEMENTATION OF IEEE 802.11A WLAN BASEBAND PROCESSOR

Due to the low-cost and high-data-rate, the popularity of IEEE 802.11-based Wireless Local Area Networks (WLAN) is growing exponentially. An existing software implementation for a fully-compliant 802.11a full-rate digital base-band transmitter requires the use of a 22- processor array running at a 1.0GHz clock frequency to reach 54Mbits/s performance.Digital signal processors (DSPs) are a special class of processor optimized for signal-processing applications in communication systems.The 802.11a standard operates in the 5GHz band with possible data rates of 6, 9, 12, 18, 24, 36, 48, and 54 Mbits/s. The 802.11g standard released in 2003 operates in the 2.4GHz band and supports all the data rates defined in the 802.11a and 802.11b standards. For the higher data rates in 802.11a, the 802.11g standard uses the same OFDM technology in 802.11a, while backward compatibility is added to support the lower data rates of 802.11b . In this project, a software-based 802.11a digital base-band processor simulated using Verilog HDL implementation.

Electronics Project: FPGA IMPLEMENTATIONS OF THE HUMMINGBIRD CRYPTOGRAPHIC ALGORITHM

  Abstract—Hummingbird is a new ultra-lightweight cryptographic algorithm targeted for resource-constrained devices like RFID tags, smart cards, and wireless sensor nodes. In this paper, we describe efficient hardware implementations of a stand-alone Hummingbird component in field-programmable gate array (FPGA) devices. We implement an encryption only core and an encryption/decryption core on the low-cost Xilinx FPGA series Spartan-3 and compare our results with other reported lightweight block cipher implementations on the same series. Our experimental results highlight that in the context of low-cost FPGA implementation Hummingbird has favorable efficiency and low area requirements.

Electronics Project:DESIGN OF LOW POWER AND HIGH SPEED CONFIGURABLE BOOTH MULTIPLIER

    Abstract: A configurable multiplier optimized for low power and high speed operations and which can be configured either for single 16-bit multiplication operation, single 8-bit multiplication or twin parallel 8-bit multiplication is designed. The output product can be truncated to further decrease power consumption and increase speed by sacrificing a bit of output precision. Furthermore, the proposed multiplier maintains an acceptable output quality with enough accuracy when truncation is performed. Thus it provides a flexible arithmetic capacity and a tradeoff between output precision and power consumption. The approach also dynamically detects the input range of multipliers and disables the switching operation of the non effective ranges. Thus the ineffective circuitry can be efficiently deactivated, thereby reducing power consumption and increasing the speed of operation. Thus the proposed multiplier outperforms the conventional multiplier in terms of power and speed efficiencies.

Electronics Project:Object Recognition

Identification of object or its location in a stored movie/ live webcam:

Object recognition is a process for identifying a specific object in a digital image or video. Object recognition algorithms rely on matching or learning algorithms using appearance-based or feature-based techniques. Common techniques include edges, gradients, Histogram of Oriented Gradients (HOG), Haar wavelets, and linear binary patterns. Object recognition is useful in applications such as video stabilization, automated vehicle parking systems, and cell counting in bioimaging, robot navigation, medical diagnosis, security, industrial inspection and automation.

This project recognizes/identifies the object or its location in the video file provides or through live webcam video.

Electronics Project:Range Reduction

Effect of Range Reduction in Image Compression:

This project demonstrates the influence of range reduction algorithm at low bit rate encoding using JPEG encode. The range [0-255] of pixel values is mapped to [64-235] in this project using a look-up-table. Subsequently, the file is compressed to achieve the same bit-rate (file size) as that of the compressed full range image. (The quality factor chosen may need adjustment if changing the input image.) The compressed files are uncompressed for quality comparison. In case of range reduction, the range is restored back on uncompression.

The effect of range reduction is to increase spatial correlation of neighborhood pixels and thereby improving compression ratio.

Electronics Project:Audio file compression

   Audio file compression using different techniques:

              The compression of digital audio data is an important topic. Compressing (reducing) the data storage requirements of digital audio allows us to fit more songs into our memory and download them faster. We will apply ideas from interpolation, least-squares approximation, and other topics, in order to reduce the storage requirements of digital audio files. All of our approaches replace the original audio signal by approximations that are made up by a linear combination of cosine functions.

Large storage requirements limit the amount of audio data that can be stored on compact discs, flash memory, and other media. Large file sizes also give rise to long download times for retrieving songs from the internet. For these reasons (and others), there is considerable interest in shrinking the storage requirements of sampled sound.

• ·         Least-squares data compression:

              Least-squares data fitting can be thought of as a method for replacing a (large) set of data with a model and a (smaller) set of model coefficients that approximate the data by minimizing the norm of the difference between the data and the model.

• ·         Digital filtering:

              The DCT algorithm can be used to not only interpolate data, but to compute a least-squares fit to the data by omitting frequencies. The process of computing a least-squares fit to digitized signals by omitting frequencies is called digital filtering. Digital filtering can reduce the storage requirements of digital audio by simply lopping off parts of the data that correspond to specific frequencies. Of course, cutting out frequencies affects the sound quality of data. However, the human ear is not equally sensitive to all frequencies. In particular, we generally do not perceive very high and very low frequencies nearly as well as mid-range frequencies. In some cases, we can filter out these frequencies without significantly affecting the perceived quality.

             Digital filtering is an effective technique for compressing audio data in many situations, especially telephony. Cutting out entire frequency ranges is rather a brute-force method, however. There are more effective ways to reduce the storage required of digital audio data, while also maintaining a high-quality sound. One idea is this: rather than cutting out “less-important” frequencies altogether, we could store the corresponding model coefficients with lower precision - that is, with fewer bits. This technique is called quantization. The “less-important” frequencies are determined by the magnitude of their DCT model coefficients. Coefficients of small magnitude correspond to cosine frequencies that do not contribute much to the sound sample. A key idea of methods is to focus the compression on parts of the signal that are perceptually not very important.