The cable television (CATV) industry has grown significantly since its inception in the late 1940’s. Originally, a CATV network was comprised of several homes that were connected to community antennae via a network of coaxial cables. The only signal processing done was by an analogue amplifier, and transmission only occurred in one direction (i.e. from the antennae/head-end to the subscribers). However, as CATV grew in popularity, demand for services such as pay-per-view television increased, which lead to supporting transmission in the upstream direction (i.e. from subscriber to the head-end). This greatly increased the signal processing to include frequency diplexers. CATV service providers began to expand the bandwidth of their networks in the late 90’s by switching from analogue to digital technology. In an effort to regulate the manufacturing of new digital equipment and ensure interoperability of products from different manufacturers, several cable service providers formed a non-for-profit consortium to develop a data-over-cable service interface specification (DOCSIS). The consortium, which is named CableLabs, released the first DOCSIS standard in 1997. The DOCSIS standard has been upgraded over the years to keep up with increased consumer demand for large bandwidths and faster transmission speeds, particularly in the upstream direction. The latest version of the DOCSIS standard, DOCSIS 3.1, utilizes orthogonal frequency-division multiple access (OFDMA) technology to provide upstream transmission speeds of up to 1 Gbps. As cable service providers begin the process of upgrading their upstream receivers to comply with the new DOCSIS 3.1 standard, they require a means of testing the various functions that an upstream receiver may employ. It is convenient for service providers to employ cable modem (CM) plus channel emulator to perform these tests in-house during the product development stage. Constructing the emulator in digital technology is an attractive option for testing. This thesis approaches digital emulation by developing a digital model of the CMs and upstream channel in a DOCSIS 3.1 network. The first step in building the emulator is to simulate its operations in MATLAB, specifically upstream transmission over the network. The MATLAB model is capable of simulating transmission from multiple CMs, each of which transmits using a specific “transmission mode.” The three transmission modes described in the DOCSIS 3.1 standard are included in the model. These modes are “traffic mode,” which is used during regular data transmission; “fine ranging mode,” which is used to perform fine timing and power offset corrections; and “probing” mode, which is presumably used for estimating the frequency response of the channel, but also is used to further correct the timing and power offsets. The MATLAB model is also capable of simulating the channel impairments a signal may encounter when traversing the upstream channel. Impairments that are specific to individual CMs include integer and fractional timing offsets, micro-reflections, carrier phase offset (CPO), fractional carrier frequency offset (CFO), and network gain/attenuation. Impairments common to all CMs include carrier hum modulation, AM/FM ingress noise, and additive white Gaussian noise (AWGN). It is the hope that the MATLAB scripts that make up the simulation be translated to Verilog HDL to implement the emulator on a field-programmable gate array (FPGA) in the near future. In the event that an FPGA implementation is pursued, research was conducted into designing efficient fractional delay filters (FDFs), which are essential in the simulation of micro-reflections. After performing an FPGA implementation cost analysis between various FDF designs, it was determined that a Kaiser-windowed sinc function FDF with roll-off parameter β = 3.88 was the most cost-efficient choice, requiring at total of 24 multipliers when implemented using an optimized structure.

The demand for efficient and reliable high rate communication is ever increasing. In this thesis we study different challenges in such systems, and their possible solutions. A goal for many years has been to implement as much as possible of a radio system in the digital domain, the ultimate goal being so called software defined radio (SDR) where the inner workings of a radio standard can be changed completely by changing the software. One important part of an SDR receiver is the high speed analog-to-digital converter (ADC) and one path to reach this high speed is to use a number of parallel, time-interleaved, ADCs. Such ADCs are, however, sensitive to sampling instant offsets, DC level offsets and gain offsets. This thesis discusses estimators based on fractional-delay filters and one application of these estimmators is to estimate and calibrate the relative delay, gain, and DC level offset between the ADCs comprising the time interleaved ADC. In this thesis we also present a technique for carrier frequency offset (CFO) estimation in orthogonal frequency division multiplexing (OFDM) systems. OFDM has gone from a promising digital radio transmission technique to become a mainstream technique used in several current and future standards. The main attractive property of OFDM is that it is inherently resilient to multipath reflections because of its long symbol time. However, this comes at the cost of a relatively high sensitivity to CFO. The proposed estimator is based on locating the spectral minimas within so-called null or virtual subcarriers embedded in the spectrum.~The spectral minimas are found iteratively over a number of symbols and is therefore mainly useful for frequency offset tracking or in systems where an estimate is not immediately required, such as in TV or radio broadcasting systems. However, complexity-wise the estimator is relatively easy to implement and it does not need any extra redundancy beside a nonmodulated subcarrier. The estimator performance is studied both in a channel with additive white Gaussian noise and in a multipath frequency selective channel environment. Interpolators and decimators are an important part of many systems, e.g. radio systems, audio systems etc. Such interpolation (decimation) is often performed using cascaded interpolators (decimators) to reduce the speed requirements in different parts of the system. In a fixed-point implementation, scaling is needed to maximize the use of the available word lengths and to prevent overflow. In the final part of the thesis, we present a method for scaling of multistage interpolators/decimators using multirate signal processing techniques. We also present a technique to estimate the output roundoff noise caused by the internal quantization.

ABSTRACT The detection of an emergency signal can be estimated by the cancellation of surrounding noise and achieving the desired signal in order to alert the automobilist. The aim of the thesis is to detect the emergency signal arriving nearer to the automobilist carrying hearing aids. Recent studies show that this can be achieved by designing various kinds of fixed and adaptive beam formers. A beam former does spatial filtering in the sense that it separates two signals with overlapping frequency content originating from distinctive directions. In this contribution, robust beam former namely Wiener beam former is designed and analyzed collaboratively in a group under the consideration of hearing aid constraints such as the microphone distance. A fractionally delay (FD) are designed to get a maximally flat group delay. The studies had been carried out by comparing noise cancellation algorithms like LMS, NLMS, LLMS and RLS algorithms. By comparing Omni-directional and multi-directional microphones the SNR can be studied. In this thesis work, first proposing appropriate microphone array setup with improved beam forming techniques by using required adaptive algorithm (NLMS) in order to get better quality using the Microphone arrays. Microphone arrays have been widely used to improve the performance of speech recognition systems as well as to benefit for people who need hearing aids. With the help of microphone arrays, it can choose to focus on signals from a specific direction. To getting better signal quality in microphone array using adaptive algorithms, these are help in the noise suppression in accordance with the different beam forming techniques. The proposed system is implemented successfully and validated using MATLAB simulation tool. The emergency signal is different in different countries, so we identify any type of emergency signal by training through neural networks. / Vamshi Krishna Lakum: +46760190899

In recent years, genetic algorithms (GAs) began to be used in many disciplines such as pattern recognition, robotics, biology, and medicine to name just a few. GAs are based on Darwin's principle of natural selection which happens to be a slow process and, as a result, these algorithms tend to require a large amount of computation. However, they offer certain advantages as well over classical gradient-based optimization algorithms such as steepest-descent and Newton-type algorithms. For example, having located local suboptimal solutions they can discard them in favor of more promising local solutions and, therefore, they are more likely to obtain better solutions in multimodal problems. By contrast, classical optimization algorithms though very efficient, they are not equipped to discard inferior local solutions in favour of more optimal ones. This dissertation is concerned with the design of several types of digital filters by using GAs as detailed bellow. In Chap. 2, two approaches for the design of fractional delay (FD) filters based on a GA are developed. The approaches exploit the advantages of a global search technique to determine the coefficients of FD FIR and allpass-IIR filters based on the so-called Farrow structure. The GA approach was compared with a least-squares approach and was found to lead to improvements in the amplitude response and/or delay characteristic. In Chap. 3, a GA-based approach is developed for the design of delay equalizers. In this approach, the equalizer coefficients are optimized using an objective function based on the passband filter-equalizer group delay. The required equalizer is built by adding new second-order sections until the desired accuracy in terms of the flatness of the group delay with respect to the passband is achieved. With this approach stable delay equalizers satisfying arbitrary prescribed specifications with the desired degree of group-delay flatness can easily be obtained. In Chap. 4, a GA-based approach for the design of multiplierless FIR filters is developed. A recently-introduced GA, called orthogonal GA (OGA) based on the so-called experimental design technique, is exploited to obtain fixed-point implementations of linear-phase FIR filters. In this approach, the effects of finite word length are minimized by considering the filter as a cascade of two sections. The OGA leads to an improved amplitude response relative to that of an equivalent direct-form cascade filter obtained using the Remez exchange algorithm. In Chap. 5, a multiobjective GA for the design of asymmetric FIR filters is proposed. This GA uses a specially tailored elitist nondominated sorting GA (ENSGA) to obtain so-called Pareto-optimal solutions for the problem at hand. Flexibility is introduced in the design by imposing phase-response linearity only in the passband instead of the entire baseband as in conventional designs. Three objective functions based on the amplitude-response error and the flatness of the group-delay characteristic are explored in the design examples considered. When compared with a WLS design method, the ENSGA was found to lead to improvements in the amplitude response and passband group-delay characteristic. In Chap. 6, a hybrid approach for the design of IIR filters using a GA along with a quasi-Newton (QN) algorithm is developed. The hybrid algorithm, referenced to as the genetic quasi-Newton (GQN) algorithm combines the flexibility and reliability inherent in the GA with the fast convergence and precision of the QN algorithm. The GA is used as a global search tool to explore different regions in the parameter space whereas the QN algorithm exploits the efficiency of a gradient-based algorithm in locating local solutions. The GQN algorithm works well with an arbitrary random initialization and filters that would satisfy prescribed amplitude-response specifications can easily be designed