Correlation of detector dog alerts to cocaine decomposition products found in illicit forensic specimens

The volatile chemicals which comprise the odor of the illicit drug cocaine have been analyzed by adsorption onto activated charcoal followed by solvent elution and GC/MS analysis. A series of field tests have been performed to determine the dominant odor compound to which dogs alert. All of our data to date indicate that the dominant odor is due to the presence of methyl benzoate which is associated with the cocaine, rather than the cocaine itself. When methyl benzoate and cocaine are spiked onto U.S. currency, the threshold level of methyl benzoate required for a canine to signal an alert is typically 1-10 $\mu$g. Humans have been shown to have a sensitivity similar to dogs for methyl benzoate but with poorer selectivity/reliability. The dominant decomposition pathway for cocaine has been evaluated at elevated temperatures (up to 280$\sp\circ$C). Benzoic acid, but no detectable methyl benzoate, is formed. Solvent extraction and SFE were used to study the recovery of cocaine from U.S. currency. The amount of cocaine which could be recovered was found to decrease with time. ^

Signal processing of physiological signals for automated assessment of stress in computer users

The need to provide computers with the ability to distinguish the affective state of their users is a major requirement for the practical implementation of affective computing concepts. This dissertation proposes the application of signal processing methods on physiological signals to extract from them features that can be processed by learning pattern recognition systems to provide cues about a person's affective state. In particular, combining physiological information sensed from a user's left hand in a non-invasive way with the pupil diameter information from an eye-tracking system may provide a computer with an awareness of its user's affective responses in the course of human-computer interactions. In this study an integrated hardware-software setup was developed to achieve automatic assessment of the affective status of a computer user. A computer-based "Paced Stroop Test" was designed as a stimulus to elicit emotional stress in the subject during the experiment. Four signals: the Galvanic Skin Response (GSR), the Blood Volume Pulse (BVP), the Skin Temperature (ST) and the Pupil Diameter (PD), were monitored and analyzed to differentiate affective states in the user. Several signal processing techniques were applied on the collected signals to extract their most relevant features. These features were analyzed with learning classification systems, to accomplish the affective state identification. Three learning algorithms: Naïve Bayes, Decision Tree and Support Vector Machine were applied to this identification process and their levels of classification accuracy were compared. The results achieved indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in the emotional states of the experimental subjects. These results also revealed that the inclusion of pupil diameter information significantly improved the performance of the emotion recognition system. ^