PERENCANAAN JALUR MOBILE ROBOT PADA LINGKUNGAN DINAMIS BERBASIS COMPACT GENETIC ALGORITHM

Permasalahan yang timbul pada sebuah pencarian dan pembentukan jalur optimal pada sebuah mobile robot adalah kemampuan untuk menghindarkan diri dari halangan, kecepatan algoritma dan jarak jalur yang dibentuk. Beberapa metode sebelumnya : novel (seperti Adaptif Path Planner, Potential Field Method, Road Map dan Djikstra) kebanyakan hanya mampu menyelesaikan dua diantara ketiga parameter yang dipersyaratkan tersebut, yaitu kecepatan algoritma dan kemampuan menghindari tumbukan. Sedangkan Algoritma Genetika juga hanya mampu menyelesaikan dua parameter yaitu kemampuan menghindari tumbukan dan jarak terpendek, namun gagal di kecepatan algoritma sehingga sulit untuk dijadikan sebuah sistem online. Untuk itu perlu digunakan sebuah sistem baru yang lebih cepat namun tetap mampu menghindari halangan dan jarak terpendek tercapai, yaitu dengan Algoritma Genetika Kompak (cGA). Penelitian ini diawali dengan mengidentifikasi area kosong dan halangan (obstacle) yang bersifat dinamis dimana posisinya dalam area dapat berpindah. Setelah area dan halangan diketahui, maka Algoritma Genetika Kompak (cGA) akan mulai membangun jalur terpendek dan paling aman (tidak menumbuk halangan) dengan memanfaatkan beberapa via point yang diberikan secara acak diluar area halangan (obstacle). Setelah jalur dengan jarak terpendek dan teraman ditemukan, maka sebuah simulator robot akan berjalan sebagai visualisasi gerakan yang menggambarkan gerakan pada robot sesungguhnya. Dengan menggunakan metode cGA yang telah diaplikasikan pada sistem, diperoleh hasil yang sama dengan Algoritma Genetika konvensional dalam hal penghindaran halangan dan jarak yang diperoleh adalah yang terpendek, serta satu lagi parameter waktu pencarian solusi yang lebih cepat

Optimized BER for channel equalizer using cuckoo search and neural network

The digital data transfer faces issues regarding Inter-Symbol Interference (ISI); therefore, the error rate becomes dependent upon channel estimation and its equalization. This paper focuses on the development of a method for optimizing the channel data to improve ISI by utilizing a swarm intelligence series algorithm termed as Cuckoo Search (CS). The adjusted data through CS is cross-validated using Artificial Neural Network (ANN). The data acceptance rate is considered with 0-10% marginal error which varies in the given range with different bit streams. The performance evaluation of the proposed algorithm using the Average Bit Error Rate (A-BER) and Logarithmic Bit Error Rate (L-BER) had shown an overall improvement of 30-50% when compared with the Kalman filter based algorithm

PERENCANAAN JALUR MOBILE ROBOT PADA LINGKUNGAN DINAMIS BERBASIS COMPACT GENETIC ALGORITHM

Permasalahan yang timbul pada sebuah pencarian dan pembentukan jalur optimal pada sebuah mobile robot adalah kemampuan untuk menghindarkan diri dari halangan, kecepatan algoritma dan jarak jalur yang dibentuk. Beberapa metode sebelumnya : novel (seperti Adaptif Path Planner, Potential Field Method, Road Map dan Djikstra) kebanyakan hanya mampu menyelesaikan dua diantara ketiga parameter yang dipersyaratkan tersebut, yaitu kecepatan algoritma dan kemampuan menghindari tumbukan. Sedangkan Algoritma Genetika juga hanya mampu menyelesaikan dua parameter yaitu kemampuan menghindari tumbukan dan jarak terpendek, namun gagal di kecepatan algoritma sehingga sulit untuk dijadikan sebuah sistem online. Untuk itu perlu digunakan sebuah sistem baru yang lebih cepat namun tetap mampu menghindari halangan dan jarak terpendek tercapai, yaitu dengan Algoritma Genetika Kompak (cGA). Penelitian ini diawali dengan mengidentifikasi area kosong dan halangan (obstacle) yang bersifat dinamis dimana posisinya dalam area dapat berpindah. Setelah area dan halangan diketahui, maka Algoritma Genetika Kompak (cGA) akan mulai membangun jalur terpendek dan paling aman (tidak menumbuk halangan) dengan memanfaatkan beberapa via point yang diberikan secara acak diluar area halangan (obstacle). Setelah jalur dengan jarak terpendek dan teraman ditemukan, maka sebuah simulator robot akan berjalan sebagai visualisasi gerakan yang menggambarkan gerakan pada robot sesungguhnya. Dengan menggunakan metode cGA yang telah diaplikasikan pada sistem, diperoleh hasil yang sama dengan Algoritma Genetika konvensional dalam hal penghindaran halangan dan jarak yang diperoleh adalah yang terpendek, serta satu lagi parameter waktu pencarian solusi yang lebih cepat

Dynamic evolving neural fuzzy inference system equalization scheme in mode division multiplexing for optical fiber transmission

The performance of optical mode division multiplexing (MDM) is affected by intersymbol interference (ISI) from nonlinear channel impairments arising from higherorder mode coupling and modal dispersion in multimode fiber. However, the existing MDM equalization algorithms can only mitigate the linear distortion, but they cannot address nonlinear distortion in the signal accurately. Therefore, there is a need to explore how ISI can be mitigated to recover the transmitted signal. This research aims to control the broadening of the MDM signal and minimize the undesirable distortion among channels in MMF by signal reshaping at the receiver. A dynamic evolving neural fuzzy inference system (DENFIS) equalization scheme has been used to achieve this objective. This research was conducted through a few steps commencing with modelling the MDM system in Optsim and collecting the data. Then, the signal reshaping parameters were determined. After that, DENFIS equalization, least mean square (LMS) and recursive least squares (RLS) equalizations were implemented and evaluated. Results illustrated that nonlinear DENFIS equalization scheme can improve MDM signal at a higher accuracy than previous linear equalization schemes. DENFIS equalization demonstrates better signal reshaping accuracy with an average root mean square error (RMSE) of 0.0338 and outperformed linear LMS and RLS equalization schemes with high average RMSE values of 0.101 and 0.1914 respectively. The reduced RMSE implies that DENFIS equalization scheme mitigates ISI more effectively in a nonlinear channel. This effect can hasten data transmission rates in MDM. Moreover, the successful offline implementation of DENFIS equalization in MDM encourages future online implementation of DENFIS equalization in embedded optical systems

Application of wavelets and artificial neural network for indoor optical wireless communication systems

Abstract This study investigates the use of error control code, discrete wavelet transform (DWT) and artificial neural network (ANN) to improve the link performance of an indoor optical wireless communication in a physical channel. The key constraints that barricade the realization of unlimited bandwidth in optical wavelengths are the eye-safety issue, the ambient light interference and the multipath induced intersymbol interference (ISI). Eye-safety limits the maximum average transmitted optical power. The rational solution is to use power efficient modulation techniques. Further reduction in transmitted power can be achieved using error control coding. A mathematical analysis of retransmission scheme is investigated for variable length modulation techniques and verified using computer simulations. Though the retransmission scheme is simple to implement, the shortfall in terms of reduced throughput will limit higher code gain. Due to practical limitation, the block code cannot be applied to the variable length modulation techniques and hence the convolutional code is the only possible option. The upper bound for slot error probability of the convolutional coded dual header pulse interval modulation (DH-PIM) and digital pulse interval modulation (DPIM) schemes are calculated and verified using simulations. The power penalty due to fluorescent light interference (FL I) is very high in indoor optical channel making the optical link practically infeasible. A denoising method based on a DWT to remove the FLI from the received signal is devised. The received signal is first decomposed into different DWT levels; the FLI is then removed from the signal before reconstructing the signal. A significant reduction in the power penalty is observed using DWT. Comparative study of DWT based denoising scheme with that of the high pass filter (HPF) show that DWT not only can match the best performance obtain using a HPF, but also offers a reduced complexity and design simplicity. The high power penalty due to multipath induced ISI makes a diffuse optical link practically infeasible at higher data rates. An ANN based linear and DF architectures are investigated to compensation the ISI. Unlike the unequalized cases, the equalized schemes don‘t show infinite power penalty and a significant performance improvement is observed for all modulation schemes. The comparative studies substantiate that ANN based equalizers match the performance of the traditional equalizers for all channel conditions with a reduced training data sequence. The study of the combined effect of the FLI and ISI shows that DWT-ANN based receiver perform equally well in the present of both interference. Adaptive decoding of error control code can offer flexibility of selecting the best possible encoder in a given environment. A suboptimal ?soft‘ sliding block convolutional decoder based on the ANN and a 1/2 rate convolutional code with a constraint length is investigated. Results show that the ANN decoder can match the performance of optimal Viterbi decoder for hard decision decoding but with slightly inferior performance compared to soft decision decoding. This provides a foundation for further investigation of the ANN decoder for convolutional code with higher constraint length values. Finally, the proposed DWT-ANN receiver is practically realized in digital signal processing (DSP) board. The output from the DSP board is compared with the computer simulations and found that the difference is marginal. However, the difference in results doesn‘t affect the overall error probability and identical error probability is obtained for DSP output and computer simulations

Application of wavelets and artificial neural network for indoor optical wireless communication systems

This study investigates the use of error control code, discrete wavelet transform (DWT) and artificial neural network (ANN) to improve the link performance of an indoor optical wireless communication in a physical channel. The key constraints that barricade the realization of unlimited bandwidth in optical wavelengths are the eye-safety issue, the ambient light interference and the multipath induced intersymbol interference (ISI). Eye-safety limits the maximum average transmitted optical power. The rational solution is to use power efficient modulation techniques. Further reduction in transmitted power can be achieved using error control coding. A mathematical analysis of retransmission scheme is investigated for variable length modulation techniques and verified using computer simulations. Though the retransmission scheme is simple to implement, the shortfall in terms of reduced throughput will limit higher code gain. Due to practical limitation, the block code cannot be applied to the variable length modulation techniques and hence the convolutional code is the only possible option. The upper bound for slot error probability of the convolutional coded dual header pulse interval modulation (DH-PIM) and digital pulse interval modulation (DPIM) schemes are calculated and verified using simulations. The power penalty due to fluorescent light interference (FL I) is very high in indoor optical channel making the optical link practically infeasible. A denoising method based on a DWT to remove the FLI from the received signal is devised. The received signal is first decomposed into different DWT levels; the FLI is then removed from the signal before reconstructing the signal. A significant reduction in the power penalty is observed using DWT. Comparative study of DWT based denoising scheme with that of the high pass filter (HPF) show that DWT not only can match the best performance obtain using a HPF, but also offers a reduced complexity and design simplicity. The high power penalty due to multipath induced ISI makes a diffuse optical link practically infeasible at higher data rates. An ANN based linear and DF architectures are investigated to compensation the ISI. Unlike the unequalized cases, the equalized schemes don‘t show infinite power penalty and a significant performance improvement is observed for all modulation schemes. The comparative studies substantiate that ANN based equalizers match the performance of the traditional equalizers for all channel conditions with a reduced training data sequence. The study of the combined effect of the FLI and ISI shows that DWT-ANN based receiver perform equally well in the present of both interference. Adaptive decoding of error control code can offer flexibility of selecting the best possible encoder in a given environment. A suboptimal 'soft' sliding block convolutional decoder based on the ANN and a 1/2 rate convolutional code with a constraint length is investigated. Results show that the ANN decoder can match the performance of optimal Viterbi decoder for hard decision decoding but with slightly inferior performance compared to soft decision decoding. This provides a foundation for further investigation of the ANN decoder for convolutional code with higher constraint length values. Finally, the proposed DWT-ANN receiver is practically realized in digital signal processing (DSP) board. The output from the DSP board is compared with the computer simulations and found that the difference is marginal. However, the difference in results doesn‘t affect the overall error probability and identical error probability is obtained for DSP output and computer simulations.EThOS - Electronic Theses Online ServiceGBUnited Kingdo