Abstract-This paper deals with the problem of detecting potential suicide bombers wearing concealed metallic and dielectric objects. The data produced by Millimeter-Wave-Radar system, working on a Multiple Frequency-Multiple Transmitters and Multiple Receivers configuration (MF-MTMR), is synthetically generated by an electromagnetic code based on Finite Differences Frequency Domain (FDFD) method. The numerical code provides the scattered field produced by the subject under test, which is later processed by using a multiple bistatic Synthetic Aperture Radar (SAR) algorithm. The blurring effect produced by the Point Spread Function (PSF) in the SAR image is removed by applying a regularized deconvolution algorithm that uses only magnitude information (no phase). Finally, the SAR algorithm and the deconvolution procedure are tested on a person wearing metallic and dielectric objects. The SAR response of dielectric rods is quite different from the metallic pipes. Our algorithm not only distinguishes between cases but also is capable of estimating the dielectric constant of the rods. Each constitutive parameter directly maps to the dielectric constant of explosive compounds, such as TNT or RDX, making feasible the detection of potential suicide bombers.

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Brain Computer Interfaces (BCI) seek to infer some task symbol, a task relevant instruction, from brain symbols, classifiable physiological states. For example, in a motor imagery robot control task a user would indicate their choice from a dictionary of task symbols (rotate arm left, grasp, etc.) by selecting from a smaller dictionary of brain symbols (imagined left or right hand movements). We examine how a BCI infers a task symbol using selections of brain symbols. We offer a recursive Bayesian decision framework which incorporates context prior distributions (e.g. language model priors in spelling applications), accounts for varying brain symbol accuracy and is robust to single brain symbol query errors. This framework is paired with Maximum Mutual Information (MMI) coding which maximizes a generalization of ITR. Both are applicable to any discrete task and brain phenomena (e.g. P300, SSVEP, MI). To demonstrate the efficacy of our approach we perform SSVEP “Shuffle” Speller experiments and compare our recursive coding scheme with traditional decision tree methods including Huffman coding. MMI coding leverages the asymmetry of the classifier’s mistakes across a particular user’s SSVEP responses; in doing so it offers a 33% increase in letter accuracy though it is 13% slower in our experiment.