Development of self-organizing methods for radio spectrum sensing

A problem of wide-band radio spectrum analysis in real time was solved and presented in the dissertation. The goal of the work was to develop a spectrum sensing method for primary user emission detection in radio spectrum by investigating new signal feature extraction and intelligent decision making techniques. A solution of this problem is important for application in cognitive radio systems, where radio spectrum is analyzed in real time. In thesis there are reviewed currently suggested spectrum analysis methods, which are used for cognitive radio. The main purpose of these methods is to optimize spectrum description feature estimation in real-time systems and to select suitable classification threshold. For signal spectrum description analyzed methods used signal energy estimation, analyzed energy statistical difference in time and frequency. In addition, the review has shown that the wavelet transform can be used for signal pre-processing in spectrum sensors. For classification threshold selection in literature most common methods are based on statistical noise estimate and energy statistical change analysis. However, there are no suggested efficient methods, which let classification threshold to change adaptively, when RF environment changes. It were suggested signal features estimation modifications, which let to increase the efficiency of algorithm implementation in embedded system, by decreasing the amount of required calculations and preserving the accuracy of spectrum analysis algorithms. For primary signal processing it is suggested to use wavelet transform based features extraction, which are used for spectrum sensors and lets to increase accuracy of noisy signal detection. All primary user signal emissions were detected with lower than 1% false alarm ratio. In dissertation, there are suggested artificial neural network based methods, which let adaptively select classification threshold for the spectrum sensors. During experimental tests, there was achieved full signals emissions detection with false alarm ratio lower than 1%. It was suggested self organizing map structure modification, which increases network self-training speed up to 32 times. This self-training speed is achieved due to additional inner weights, which are added in to self organizing map structure. In self-training stage network structure changes especially fast and when topology, which is suited for given task, is reached, in further self-training iterations it can be disordered. In order to avoid this over-training, self-training process monitoring algorithms must be used. There were suggested original methods for self-training process control, which let to avoid network over-training and decrease self-training iteration quantity

Energy detector implementaton in FPGA for estimation of word boundaries

This paper describes implementation of the word boundary estimation module in FPGA. The boundary estimation module is based on energy detector. This module is optimized for implementation in FPGA. It occupies 54 logical elements “Slice” and uses only 0.7% of “Spartan-6 LX45” resources. Experiments with this module were performed at different signal/noise (S/N) ratio. For S/N of 20 dB and 15 dB word boundaries were estimated with 100% accuracy. Acceptable results were also achieved, for S/N ratio of 10 dB and 5 dB, as the estimation accuracy was 95% and 93%, respectively. Article in Lithuanian. Energijos detektoriaus, naudojamo žodžio riboms nustatyti, įgyvendinimas lauku programuojama logine matrica Santrauka. Pateikiamas žodžio ribų nustatymo modulio įgyvendinimas lauku programuojama logine matrica (LPLM). Žodžio riboms nustatyti pasirinktas energijos detektorius, nes šis metodas, naudojant skaitmenines signalų apdorojimo priemones, įgyvendinamas efektyviai. Žodžio ribų nustatymo modulis buvo optimizuotas tiek, kad, įgyvendintas LPLM, jis užėmė 54 loginius elementus „Slice“ – tik 0,7 % „Spartan-6 LX45“ lusto išteklių. Eksperimentuojant nustatyta, kad esant 20 dB ir 15 dB signalo triukšmo santykiui, žodžio ribos nustatomos tiksliai, o kai šis santykis yra 10 dB ir 5 dB, žodžio ribos nustatomos 95 % ir 93 % tikslumu. Reikšminiai žodžiai: lauku programuojama loginė matrica, žodžio ribų nustatymas, energijos detektorius, tylusis intervalas, signalo triukšmo santykis

FPGA-based implementation of Lithuanian isolated word recognition algorithm

The paper describes the FPGA-based implementation of Lithuanian isolated word recognition algorithm. FPGA is selected for parallel process implementation using VHDL to ensure fast signal processing at low rate clock signal. Cepstrum analysis was applied to features extraction in voice. The dynamic time warping algorithm was used to compare the vectors of cepstrum coefficients. A library of 100 words features was created and stored in the internal FPGA BRAM memory. Experimental testing with speaker dependent records demonstrated the recognition rate of 94%. The recognition rate of 58% was achieved for speaker-independent records. Calculation of cepstrum coefficients lasted for 8.52 ms at 50 MHz clock, while 100 DTWs took 66.56 ms at 25 MHz clock. Article in Lithuanian. Lietuvių kalbos pavienių žodžių atpažinimo algoritmo įgyvendinimas lauku programuojama logine matrica Santrauka. Pateikiamas lietuvių kalbos pavienių žodžių atpažinimo algoritmo įgyvendinimas lauku programuojama logine matrica (LPLM). LPLM įrenginys pasirinktas dėl lygiagrečiai veikiančių procesų įgyvendinimo galimybės taikant VHDL kalbą. Tai užtikrina spartų signalų apdorojimą esant taktiniam dažniui iki 50 MHz. Kalbos požymiams išskirti taikoma kepstrinė šnekos analizė. Požymiams palyginti taikomas dinaminis laiko skalės kraipymo (DSLK) metodas. Sudaryta 100 žodžių požymių biblioteka, kuri saugoma vidinėje LPLM BRAM atmintyje. Pasiektas 94 % atpažinimo tikslumas priklausomai nuo kalbėtojo ir 58 % – nepriklausomai nuo kalbėtojo. Kepstro koeficientų skaičiavimas vienam žodžiui trunka 8,52 ms, esant 50 MHz taktiniam dažniui, ir šimtui DLSK – 66,56 ms, esant 25 MHz taktiniam dažniui. Reikšminiai žodžiai: lauku programuojama loginė matrica, žodžio atpažinimas, kepstras, dinaminis laiko skalės kraipymas

Selection of an optimal adaptive filter for speech signal noise cancellation using C6455 DSP

This article discusses the implementation of the various adaptive filters for the filtering of the noisy speech signal, whose spectrum overlaps or is close to the information signal. Three types of adaptive filters are compared: LMS, NLMS and RLS. The computational load of the C6455 digital signal processor is monitored for different order filters and the efficiency of the filtering is measured using signal-to-noise ratio off the output signal. The results determined for the most suitable family of filters for application in real situations Ill. 8, bibl. 10 (in English; abstracts in English and Lithuanian)

Burst signal detector based on signal energy and standard deviation

Paper focuses on the spectrum sensing for cognitive radio solutions. The new algorithm is proposed for burst signal detection in frequency band where only this type of primary user signal appears (e.g. GSM band). Proposed spectrum sensor use signal energy and standard deviation estimates for primary user signal detection. A single perceptron is proposed to define a threshold for spectrum sensor. To investigate the efficiency of proposed spectrum sensor the real environment measurements were performe d in the frequency band used by GSM system for downlink . A n a dditional analysis of the signal energy estimates showed the periodicity of the energy changes in time domain. The calculation of FFT for signal energy changes in time has proven the performance of proposed spectrum detector for low power (situated far away from spectrum sensor) primary user signal detection in situations where it is covered by environment noise

Self-organizing feature map preprocessed vocabulary renewal algorithm for the isolated word recognition system

Paper focuses on the new vocabulary renewal algorithm designed for the hardware implemented Lithuanian speech recognizer. The isolated word recognition is performed using dynamic time warping of the Mel-frequency cepstrum coefficients (MFCC) estimated during short-time analysis of speech signals. A self-organizing feature map is used to extract the time-dependent MFCC features variations. To increase the isolated word recognition rate, four references are stored in the vocabulary for each word to be recognized. In order to make vocabulary adaptive to long-term changes of the user speech and adapt recognizer to the environment the references should be updated. The renewal of the vocabulary is performed if two conditions are met: the distance between same word references and the distance between new reference and other word references in the feature set should be increased. The comparison of the time-dependent MFCC feature variations is performed using Needleman-Wunsch sequence alignment algorithm