Small-world networks and disturbed functional connectivity in schizophrenia.

Disturbances in "functional connectivity" have been proposed as a major pathophysiological mechanism for schizophrenia, and in particular, for cognitive disorganization. Detection and estimation of these disturbances would be of clinical interest. Here we characterize the spatial pattern of functional connectivity by computing the "synchronization likelihood" (SL) of EEG at rest and during performance of a 2Back working memory task using letters of the alphabet presented on a PC screen in subjects with schizophrenia and healthy controls. The spatial patterns of functional connectivity were then characterized with graph theoretical measures to test whether a disruption of an optimal spatial pattern ("small-world") of the functional connectivity network underlies schizophrenia. Twenty stabilized patients with schizophrenia, who were able to work, and 20 healthy controls participated in the study. During the working memory (WM) task healthy subjects exhibited small-world properties (a combination of local clustering and high overall integration of the functional networks) in the alpha, beta and gamma bands. These properties were not present in the schizophrenia group. These findings are in accordance with a partially inadequate organization of neuronal networks in subjects with schizophrenia. This method could be helpful for diagnosis and evaluation of the severity of the disease, as well as understanding the pathophysiologic mechanisms underlying cognitive dysfunction in schizophrenia.

A Bayesian framework for multilead SMD post-placement quality inspection.

In this paper, a novel framework is proposed to inspect the placement quality of surface mount technology devices (SMDs), immediately after they have been placed in wet solder paste on a printed circuit board (PCB). The developed approach involves the indirect measurement of each lead displacement with respect to its ideal position, centralized on its pad region. This displacement is inferred from area measurements on the raw image data of the lead region through a classification process. To increase the accuracy in the computation of the lead displacement, we introduce a combined classification/estimation process, in which the individual lead displacement classifications are viewed as measurements (or observations) of the same physical quantity i.e., the displacement of the entire component as a rigid body. Certain geometric relations connecting lead shifts to component displacement are also derived. Employing these relations we can infer a new refined measurement of the shift of each individual lead, a quantity crucial to the calculation of the quality measures. Experimental results highlight the potential of the developed algorithm.