Biblio

One of the primary motivations for using ultrasound reflectometry for fingerprint imaging is the promise of increased spoof resistance over conventional optical or capacitive sensing approaches due to the ability for ultrasound to determine the elastic impedance of the imaged material. A fake 3D printed plastic finger can therefore be easily distinguished from a real finger. However, ultrasonic sensors are still vulnerable to materials that are similar in impedance to tissue, such as water or rubber. Previously we demonstrated an ultrasonic fingerprint reader operating with 1.3GHz ultrasound based on pulse echo impedance imaging on the backside silicon interface. In this work, we utilize the large bandwidth of these sensors to differentiate between a finger and materials with similar impedances using the frequency response of elastic impedance obtained by transducer excitation with a wideband RF chirp signal. The reflected signal is a strong function of impedance mismatch and absorption [Hoople 2015].

Genes, proteins and other metabolites present in cellular environment exhibit a virtual network that represents the regulatory relationship among its constituents. This network is called Gene Regulatory Network (GRN). Computational reconstruction of GRN reveals the normal metabolic pathway as well as disease motifs. Availability of microarray gene expression data from normal and diseased tissues makes the job easier for computational biologists. Reconstruction of GRN is based on neural modeling. Here we have used discrete and continuous versions of a meta-heuristic algorithm named Firefly algorithm for structure and parameter learning of GRNs respectively. The discrete version for this problem is proposed by us and it has been applied to explore the discrete search space of GRN structure. To evaluate performance of the algorithm, we have used a widely used synthetic GRN data set. The algorithm shows an accuracy rate above 50% in finding GRN. The accuracy level of the performance of Firefly algorithm in structure and parameter optimization of GRN is promising.

Conventional photoacoustic microscopy (PAM) involves detection of optically induced thermo-elastic waves using ultrasound transducers. This approach requires acoustic coupling and the spatial resolution is limited by the focusing properties of the transducer. We present an all-optical PAM approach that involved detection of the photoacoustically induced surface displacements using an adaptive, two-wave mixing interferometer. The interferometer consisted of a 532-nm, CW laser and a Bismuth Silicon Oxide photorefractive crystal (PRC) that was 5×5×5 mm3. The laser beam was expanded to 3 mm and split into two paths, a reference beam that passed directly through the PRC and a signal beam that was focused at the surface through a 100-X, infinity-corrected objective and returned to the PRC. The PRC matched the wave front of the reference beam to that of the signal beam for optimal interference. The interference of the two beams produced optical-intensity modulations that were correlated with surface displacements. A GHz-bandwidth photoreceiver, a low-noise 20-dB amplifier, and a 12-bit digitizer were employed for time-resolved detection of the surface-displacement signals. In combination with a 5-ns, 532-nm pump laser, the interferometric probe was employed for imaging ink patterns, such as a fingerprint, on a glass slide. The signal beam was focused at a reflective cover slip that was separated from the fingerprint by 5 mm of acoustic-coupling gel. A 3×5 mm2 area of the coverslip was raster scanned with 100-μm steps and surface-displacement signals at each location were averaged 20 times. Image reconstruction based on time reversal of the PA-induced displacement signals produced the photoacoustic image of the ink patterns. The reconstructed image of the fingerprint was consistent with its photograph, which demonstrated the ability of our system to resolve micron-scaled features at a depth of 5 mm.