Transceiver architectures and sub-mW fast frequency-hopping synthesizers for ultra-low power WSNs

Wireless sensor networks (WSN) have the potential to become the third wireless revolution after wireless voice networks in the 80s and wireless data networks in the late 90s. This revolution will finally connect together the physical world of the human and the virtual world of the electronic devices. Though in the recent years large progress in power consumption reduction has been made in the wireless arena in order to increase the battery life, this is still not enough to achieve a wide adoption of this technology. Indeed, while nowadays consumers are used to charge batteries in laptops, mobile phones and other high-tech products, this operation becomes infeasible when scaled up to large industrial, enterprise or home networks composed of thousands of wireless nodes. Wireless sensor networks come as a new way to connect electronic equipments reducing, in this way, the costs associated with the installation and maintenance of large wired networks. To accomplish this task, it is necessary to reduce the energy consumption of the wireless node to a point where energy harvesting becomes feasible and the node energy autonomy exceeds the life time of the wireless node itself. This thesis focuses on the radio design, which is the backbone of any wireless node. A common approach to radio design for WSNs is to start from a very simple radio (like an RFID) adding more functionalities up to the point in which the power budget is reached. In this way, the robustness of the wireless link is traded off for power reducing the range of applications that can draw benefit form a WSN. In this thesis, we propose a novel approach to the radio design for WSNs. We started from a proven architecture like Bluetooth, and progressively we removed all the functionalities that are not required for WSNs. The robustness of the wireless link is guaranteed by using a fast frequency hopping spread spectrum technique while the power budget is achieved by optimizing the radio architecture and the frequency hopping synthesizer Two different radio architectures and a novel fast frequency hopping synthesizer are proposed that cover the large space of applications for WSNs. The two architectures make use of the peculiarities of each scenario and, together with a novel fast frequency hopping synthesizer, proved that spread spectrum techniques can be used also in severely power constrained scenarios like WSNs. This solution opens a new window toward a radio design, which ultimately trades off flexibility, rather than robustness, for power consumption. In this way, we broadened the range of applications for WSNs to areas in which security and reliability of the communication link are mandatory

Efficient VLSI Architectures for Image Compression Algorithms

An image, in its original form, contains huge amount of data which demands not only large amount of memory requirements for its storage but also causes inconvenient transmission over limited bandwidth channel. Image compression reduces the data from the image in either lossless or lossy way. While lossless image compression retrieves the original image data completely, it provides very low compression. Lossy compression techniques compress the image data in variable amount depending on the quality of image required for its use in particular application area. It is performed in steps such as image transformation, quantization and entropy coding. JPEG is one of the most used image compression standard which uses discrete cosine transform (DCT) to transform the image from spatial to frequency domain. An image contains low visual information in its high frequencies for which heavy quantization can be done in order to reduce the size in the transformed representation. Entropy coding follows to further reduce the redundancy in the transformed and quantized image data. Real-time data processing requires high speed which makes dedicated hardware implementation most preferred choice. The hardware of a system is favored by its lowcost and low-power implementation. These two factors are also the most important requirements for the portable devices running on battery such as digital camera. Image transform requires very high computations and complete image compression system is realized through various intermediate steps between transform and final bit-streams. Intermediate stages require memory to store intermediate results. The cost and power of the design can be reduced both in efficient implementation of transforms and reduction/removal of intermediate stages by employing different techniques. The proposed research work is focused on the efficient hardware implementation of transform based image compression algorithms by optimizing the architecture of the system. Distribute arithmetic (DA) is an efficient approach to implement digital signal processing algorithms. DA is realized by two different ways, one through storage of precomputed values in ROMs and another without ROM requirements. ROM free DA is more efficient. For the image transform, architectures of one dimensional discrete Hartley transform (1-D DHT) and one dimensional DCT (1-D DCT) have been optimized using ROM free DA technique. Further, 2-D separable DHT (SDHT) and 2-D DCT architectures have been implemented in row-column approach using two 1-D DHT and two 1-D DCT respectively. A finite state machine (FSM) based architecture from DCT to quantization has been proposed using the modified quantization matrix in JPEG image compression which requires no memory in storage of quantization table and DCT coefficients. In addition, quantization is realized without use of multipliers that require more area and are power hungry. For the entropy encoding, Huffman coding is hardware efficient than arithmetic coding. The use of Huffman code table further simplifies the implementation. The strategies have been used for the significant reduction of memory bits in storage of Huffman code table and the complete Huffman coding architecture encodes the transformed coefficients one bit per clock cycle. Direct implementation algorithm of DCT has the advantage that it is free of transposition memory to store intermediate 1-D DCT. Although recursive algorithms have been a preferred method, these algorithms have low accuracy resulting in image quality degradation. A non-recursive equation for the direct computation of DCT coefficients have been proposed and implemented in both 0.18 µm ASIC library as well as FPGA. It can compute DCT coefficients in any order and all intermediate computations are free of fractions and hence very high image quality has been obtained in terms of PSNR. In addition, one multiplier and one register bit-width need to be changed for increasing the accuracy resulting in very low hardware overhead. The architecture implementation has been done to obtain zig-zag ordered DCT coefficients. The comparison results show that this implementation has less area in terms of gate counts and less power consumption than the existing DCT implementations. Using this architecture, the complete JPEG image compression system has been implemented which has Huffman coding module, one multiplier and one register as the only additional modules. The intermediate stages (DCT to Huffman encoding) are free of memory, hence efficient architecture is obtained

Hardward and algorithm architectures for real-time additive synthesis

Additive synthesis is a fundamental computer music synthesis paradigm tracing its origins to the work of Fourier and Helmholtz. Rudimentary implementation linearly combines harmonic sinusoids (or partials) to generate tones whose perceived timbral characteristics are a strong function of the partial amplitude spectrum. Having evolved over time, additive synthesis describes a collection of algorithms each characterised by the time-varying linear combination of basis components to generate temporal evolution of timbre. Basis components include exactly harmonic partials, inharmonic partials with time-varying frequency or non-sinusoidal waveforms each with distinct spectral characteristics. Additive synthesis of polyphonic musical instrument tones requires a large number of independently controlled partials incurring a large computational overhead whose investigation and reduction is a key motivator for this work. The thesis begins with a review of prevalent synthesis techniques setting additive synthesis in context and introducing the spectrum modelling paradigm which provides baseline spectral data to the additive synthesis process obtained from the analysis of natural sounds. We proceed to investigate recursive and phase accumulating digital sinusoidal oscillator algorithms, defining specific metrics to quantify relative performance. The concepts of phase accumulation, table lookup phase-amplitude mapping and interpolated fractional addressing are introduced and developed and shown to underpin an additive synthesis subclass - wavetable lookup synthesis (WLS). WLS performance is simulated against specific metrics and parameter conditions peculiar to computer music requirements. We conclude by presenting processing architectures which accelerate computational throughput of specific WLS operations and the sinusoidal additive synthesis model. In particular, we introduce and investigate the concept of phase domain processing and present several “pipeline friendly” arithmetic architectures using this technique which implement the additive synthesis of sinusoidal partials