Fiber-optic networks are continually evolving to accommodate ever-increasing data transport rates demanded by modern applications, devices, and services. Network operators are now beginning to deploy systems with 100 Gb/s per-wavelength data rates while maintaining the 50 GHz dense wavelength division multiplexing grid that is (generally) standard for 10 Gb/s systems. Advanced modulation formats incorporating both amplitude- and phase-based data symbols are necessary to meet the spectral efficiency requirements of fiber-optic data transport. These modulation formats require coherent detection, enabling future networks to take advantage of advances in silicon CMOS via digital signal processing algorithms and techniques. The primary challenge for future networks is the fiber nonlinear response; changes in the intensity of the propagating optical signal induce changes in the optical fiber refractive index. Limiting the allowed propagation intensity will reduce these nonlinear effects and correspondingly limit the total available signal-to-noise ratio (SNR) within the channel. Predicting the nonlinear SNR limits of fiber-optic transport for data rates 100 Gb/s and beyond is a primary purpose of this research. This dissertation expressly matches several novel expressions for nonlinear interference accumulation to experimental data and demonstrates robust theoretical prediction of nonlinear transmission penalties. The experiments were performed to isolate the transmission performance of the fiber medium in the highly dispersive regime -- no dispersion compensation or Raman amplification was employed and all other hardware was kept static. These results are the first experimental validation of the nonlinear interference expressions on a fiber-type basis. Second, this dissertation moves to data transport beyond per-wavelength rates of 100 Gb/s by employing 16QAM at baud rates as high as 32 GHz. It examines signal processing strategies for 16QAM transport and extends the nonlinear interference prediction techniques to 16QAM. The results reveal that the SNR requirements of 16QAM as limited by nonlinear interference will likely limit deployments to high-density regional and metro networks.

When doing recording work in the studio, disturbing sound must be removed. In this thesis, the purpose of this thesis is to formulate a mathematical equation, by using MATLAB to identify a system, then using the system to do cancellation of disturbing sound. The method of doing cancellation is to subtract the simulated output by the actual output, and then the disturbing sound was cancelled. The main thesis work will focus on the system identification, which is the process of determining the characteristic of an unknown system. Three systems were identified with the same model structure, which is linear (ARX) model. After finding out the model, the model quality must be evaluated. If the model is valid, there is a discussion if it is possible to run the mathematical equation in the real application, and how is the market today.

One technique that is used to reduce the errors brought upon signals, when transmitted over noisy channels, is error control coding. One type of such coding, which has a good performance, is turbo coding. In some of the TMS320C64xTM digital signal processors there is a built in coprocessor that performs turbo decoding. This thesis is performed on the account of Communication Developments, within Saab AB and presents an evaluation of this coprocessor. The evaluation deals with both the memory consumption as well as the data rate. The result is also compared to an implementation of turbo coding that does not use the coprocessor. / En teknik som används för att minska de fel som en signal utsätts för vid transmission över en brusig kanal är felrättande kodning. Ett exempel på sådan kodning som ger ett mycket bra resultat är turbokodning. I några digitalsignalprocessorer, av sorten TMS320C64xTM, finns en inbyggd coprocessor som utför turboavkodning. Denna uppsats är utförd åt Communication Development inom Saab AB och presenterar en utvärdering av denna coprocessor. Utvärderingen avser såväl minnesförbrukning som datatakt och innehåller även en jämförelse med en implementering av turbokodning utan att använda coprocessorn.

State of the art model based coders offer a perceptually acceptable reconstructed speech quality at bit-rates as low as 2000 bits per second. However, the performance of these coders rapidly deteriorates below this rate, primarily since very few bits are available to encode the model parameters with high fidelity. This thesis aims to meet the challenge of designing speech coders that operate at lower bit-rates while reconstructing the speech at the receiver at the same or even better quality than state of the art low bit-rate speech coders. In one of the contributions, we develop a plethora of techniques for efficient coding of the parameters obtained by the MELP algorithm, under the assumption that the classification of the frames of the MELP coder is available. Also, a simple and elegant procedure called dynamic codebook reordering is presented for use in the encoders and decoders of a vector quantization system that effectively exploits the correlation between vectors of parameters obtained from consecutiv speech frames without introducing any delay, distortion or suboptimality. The potential of this technique in significantly reducing the bit-rates of speech coders is illustrated. Additionally, the thesis also attempts to address the issues of designing such very low bit-rate speech coders so that they are robust to environmental noise. To impart robustness, a speech enhancement framework employing Kalman filters is presented. Kalman filters designed for speech enhancement in the presence of noise assume an autoregressive model for the speech signal. We improve the performance of Kalman filters in speech enhancement by constraining the parameters of the autoregressive model to belong to a codebook trained on clean speech. We then extend this formulation to the design of a novel framework, called the multiple input Kalman filter, that optimally combines the outputs from several speech enhancement systems. Since the low bit-rate speech coders compress the parameters significantly, it is very important to protect the transmitted information from errors in the communication channel. In this thesis, a novel channel-optimized multi-stage vector quantization codec is presented, in which the stage codebooks are jointly designed.

This paper proposes a sensorless control construction adapting to different speed with DSP2407 as the signal processing control core for rotation compressor. The sensorless control method obtains the rotor position by detecting the back electromotive force signals directly, then obtains better communications and the speed estimation by using digital signal which controlling power switches. Finally ,it carries out speed feedback control and current feedback control to improvt efficiency. Comparing adaptive-step control with traditional-step control and six-step control , the experiment result shows that adaptive-step control has better efficiency and lower vibration.

The switched reluctance motor has the advantages of the low production cost, high operating efficiency, high stability, and high start torque. It can deliver a wide speed range, and therefore make it very attractive to the engineers and researchers. The double salient structure of SRM result in a non-linear stator inductance, so the output reluctance torque has a highly non-linear behavior. A digital signal processor based drive system for SRM is developed and implemented in this thesis using the TI TMS320F240 DSP system which is with universal peripheral interface circuits. The built-in pulse width modulation(PWM) module of the DSP system can auto-generate PWM output signal by setting the relative registers to simplify the hardware design. This research built a complete drive system for SRM, both the closed-loop velocity controller and current compensator were designed according to the proportional-integral(PI) control mechanism, and all schemes were coded in the DSP program. Simulation and experiment results demonstrate that the proposed drive system makes reluctance torque output very smoothly with a preferable velocity response.

This paper design a Digital-Singal-Processor based which cooperating with the technique of switching power supply to implement the Brushless DC motor driver for electric vehicle ,and use the asymmetry pulse-width modulation theorem in sinusoidal PWM switch to increase motion efficiency of motor and decrease the power depletion ,thus can improve the current- spike from analog controller effectively ,and prove the feasibility of the system.

This paper designs and implements a DSP based intelligent battery charger and residual capacity estimator for electric vehicle. This system uses the proposed new electric circuit structure and the intelligent fuzzy charge algorithm to charge batteries, and the improved coulometric measurement with accurate residual capacity estimation to estimate the residual capacity of batteries. From the experimental results, the charger can achieve the purpose of fast and uniform charge with charging time six (6) to eight (8) hours, and will not cause the damage of battery because of using the intelligent fuzzy charge algorithm can give different charging current depend on the difference of voltage, capacity and temperature of battery; the residual capacity estimator can accurate estimate the residual capacity of batteries due to calculating the increment current and considering the aging factor.

Electric power utilities and end users are becoming increasingly concerned about the quality of power supply. To reduce the losses caused by power service disturbances, mitigation devices are available for improving the power quality. The first step in the power quality improvement is to monitor the system behavior by using some Electronic recording devices. A design and implementation of a digital signal processor based power quality monitoring device is presented in this thesis. Several event-triggering methods are studied and implemented to detect system disturbances. Simulation and test results indicate that the proposed design can meet the requirements for power measurements and transient event recording during steady and transient states.

This paper Management and Diagnosis a DSP based intelligent battery charger and residual capacity estimator for electric vehicle. This system uses the proposed new electric circuit structure and the intelligent fuzzy charge algorithm to charge batteries, and the improved coulometric measurement with accurate residual capacity estimation to estimate the residual capacity of batteries. From the experimental results, the charger can achieve the purpose of fast and uniform charge with charging time six (6) to eight (8) hours, and will not cause the damage of battery because of using the intelligent fuzzy charge algorithm can give different charging current depend on the difference of voltage, capacity and temperature of battery; the residual capacity estimator can accurate estimate the residual capacity of batteries due to calculating the increment current and considering the aging factor.