E-nose pattern recognition and drift compensation methods

With new developments and upcoming technologies, new sensing techniques are becoming available. Unfortunately, none of these techniques provides output interpreted the way human perception works. An inability to improve the effectiveness of these technologies limits their use in dedicated applications and increases their complexity. The growing adoption of this technology makes it critical to create a system capable of handling e-nose challenging issues such as noise, drift, imbalanced data, dynamic environment, and high uncertainties. Without appropriate pattern recognition methods that allow inferences to be derived based on patterns observed within these data sets, it will not be possible to improve the performance of current e-nose systems. In this chapter, e-nose drift issue is introduced and the available drift counteraction methods is discussed

Biologically inspired pattern recognition for E-nose sensors

The high sensitivity, stability, selectivity and adaptivity of mailman olfactory system is a result of a large number of olfactory receptors feeding into an extensive layers of neural processing units. Olfactory receptor cells (ORC) contribute significantly in the sense of smells. Bloodhounds have four billion ORC making them ideal for tracking while human have about 30 million ORC. E-nose stability, sensitivity and selectivity have been a challenging task. We hypothesize that appropriate signal processing with an increase number of sensory receptors can significantly improve odour recognition in e-nose. Adding physical receptors to e-nose is costly and can increase system complexity. Therefore, we propose an Artificial Olfactory Receptor Cells Model (AORCM) inspired by neural circuits of the vertebrate olfactory system to improve e-nose performance. Secondly, we introduce and adaptation layer to cope with drift and unknown changes. The major layers in our model are the sensory transduction layer, sensory adaptation layer, artificial olfactory receptors layer (AORL) and artificial olfactory cortex layer (AOCL). Each layer in the proposed system is biologically inspired by the mammalian olfactory system. The experiments are executed using chemo-sensory arrays data generated over three-year period. The propose model resulted in a better performance and stability compared to other models. To our knowledge, e-nose stability, selectivity and sensitivity are still unsolved problem. Our paper provides a new approach in improving e-nose pattern recognition over long period of time

An Integration of New Digital Image Scrambling Technique on PCA-Based Face Recognition System

Systems using biometric authentication offer greater security than traditional textual and graphical password-based systems for granting access to information systems. Although biometric-based authentication has its benefits, it can be vulnerable to spoofing attacks. Those vulnerabilities are inherent to any biometric-based subsystem, including face recognition systems. The problem of spoofing attacks on face recognition systems is addressed here by integrating a newly developed image encryption model onto the principal component pipeline. A new model of image encryption is based on a cellular automaton and Gray Code. By encrypting the entire ORL faces dataset, the image encryption model is integrated into the face recognition system’s authentication pipeline. In order for the system to grant authenticity, input face images must be encrypted with the correct key before being classified, since the entire feature database is encrypted with the same key. The face recognition model correctly identified test encrypted faces from an encrypted features database with 92.5% accuracy. A sample of randomly chosen samples from the ORL dataset was used to test the encryption performance. Results showed that encryption and the original ORL faces have different histograms and weak correlations. On the tested encrypted ORL face images, NPCR values exceeded 99%, MAE minimum scores were over (>40), and GDD values exceeded (0.92). Key space is determined by u2sizeA0 where A0 represents the original scrambling lattice size, and u is determined by the variables on the encryption key. In addition, a NPCR test was performed between images encrypted with slightly different keys to test key sensitivity. The values of the NPCR were all above 96% in all cases

Bio-inspired learning approach for electronic nose

The high sensitivity, stability, selectivity and adaptivity of mailman olfactory system is a result of a large number of olfactory receptors feeding into extensive layers of neural processing units. Olfactory receptor cells (ORC) contribute significantly in the sense of smells. Bloodhounds have 4\ua0billion ORC making them ideal for tracking while human has about 30\ua0million ORC. E-nose stability, sensitivity and selectivity have been a challenging task. We hypothesize that appropriate signal processing with an increased number of sensory receptors can significantly improve odour recognition in e-nose. Adding physical receptors to e-nose is costly and can increase system complexity. Therefore, we propose an artificial olfactory receptor cells model inspired by neural circuits of the vertebrate olfactory system to improve e-nose performance. Secondly, we introduce and adaptation layer to cope with drift and unknown changes. The major layers in our model are the sensory transduction layer, sensory adaptation layer, artificial olfactory receptors layer and artificial olfactory cortex layer. Each layer in the proposed system is biologically inspired by the mammalian olfactory system. The experiments are executed using chemo-sensory arrays data generated over a 3-year period. The proposed model resulted in a better performance and stability compared to other models. However, e-nose stability, selectivity and sensitivity issues remain unsolved problems. Our paper provides a new approach to improve e-nose pattern recognition over a long period of time