Investigation of the use of bandpass filter for lifting wavelet transform (LWT) with (SPIHT) set partitioning in hierarchical trees of image applied in (IoT) internet of things | |
| Author | Tun Tun Win |
| Call Number | AIT RSPR no.ICT-16-03 |
| Subject(s) | Electric filters, Bandpass Internet of things Wavelets (Mathematics) |
| Note | A research submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Communication Technologies, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Research studies project report ; no. ICT-16-03 |
| Abstract | This research is submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Communications Technologies. Investigation of the use of Bandpass Filter (BPF) for Lifting Wavelet Transform (LWT) with (SPIHT) Set Partitioning in Hierarchical Trees of Image applied in Internet of Things (IoT) is considered and also applied in Machine to Machine (M2M) image communications system. The performances of LWT, Inverse Wavelet Transform (IWT), SPIHT, binary encoding and decoding, and signal to noise ratio (SNR) enhancing of IoT network are to be examined. In these investigated algorithm, first the image pre-processing is performed and then the low or high frequency image is transformed with LWT to get sub-band transform. Second, sub-band transform image is received by the SPIHT encoding for compressing. After the information run through the SPIHT and binary encoding stages, the program produced compressed image file as a form of bits stream for transmission between transmitter and receiver. The Bandpass Filter (BPF) is implemented at the receiver to eliminate the thermal noise and narrow band noises from signal in IoT network. In the de-compressor process, binary decoder received the bits stream from the binary encoder after finished the BPF process and then it is passed over the SPIHT decoding, and IWT function for generating the reconstructed image. For this study, the lifting sub-band transform is used for transforming wavelet and SPIHT algorithm is used for compressing an image, binary coding is used for entropy coding and BPF is used for developing SNR in channel of IoT network. The simulation results show that the performance of wavelet transforming, SPIHT coding, and binary coding, compare Peak Signal to Noise Ratio (PSNR) and computation time with other algorithms, and SNR advancing in system. The quality of reconstructed image is evaluated with the PSNR. |
| Year | 2016 |
| Corresponding Series Added Entry | Asian Institute of Technology. Research studies project report ; no. ICT-16-03 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information and Communication Technology (ICT) |
| Chairperson(s) | Attaphongse Taparugssanagorn; |
| Examination Committee(s) | Teerapat Sanguankotchakorn;Mongkol Ekpanyapong; |
| Scholarship Donor(s) | Asian Institute of Technology Fellowship; |
| Degree | Research Studies Project Report (M. Eng.) - Asian Institute of Technology, 2016 |