A new pre-processing algorithm for improved discrimination of odor samples is proposed. The pre-processed odor sample outputs from two sensors are input using a learning-vector quantization (LVQ) classifier as a means of odor recognition to be employed within electronic nose applications. The proposed algorithm brings out highly scattered classes while minimizing the within-class scatter of the samples given an odor class. LVQ is observed to operate robustly and reliably in terms of variation of parameters of interest, mainly a learning parameter. Due to the increased performance along with computational simplicity and robustness, the scheme is suitable to sample-by-sample identification of olfactory sensory data and can be easily adapted to hierarchical processing with other sensory data in real-time.
The retinopathy diseases occur when the neurons do not transmit signals from retina to the brain. These disorders are: Diabetic retinopathy, hypertensive retinopathy, macular degeneration, vein branch occlusion, vitreous hemorrhage, and normal retina. This work presents a novel detection algorithm about retinopathy disorders from retina images. For this purpose, the retina images were pre-processed and resized at first. Then the discrete cosine transform was used as feature extraction before applying a neural network classifier. The performance of recognition rates of the novel detection algorithm were found as 50%, 70%, 85%, 90%, and 95% for testing five retinopathy cases respectively.
This paper presents a lossy compression scheme for biomedical images by using a new method. Image data compression using Vector Quantization (VQ) has received a lot of attention because of its simplicity and adaptability. VQ requires the input image to be processed as vectors or blocks of image pixels. The Finite-state vector quantization (FSVQ) is known to give better performance than the memory less vector quantization (VQ). This paper presents a novel combining technique for image compression based on the Hierarchical Finite State Vector Quantization (HFSVQ) and the neural network. The algorithm performs nonlinear restoration of diffraction-limited images concurrently with quantization. The neural network is trained on image pairs consisting of a lossless compression named hierarchical vector quantization. Simulations results are presented that demonstrate improvements in visual quality and peak signal-to-noise ratio of the restored images.