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This paper proposes a novel  oblivious image watermarking technique for the copyright protection and authentication of still images, based on Discrete Multiwavelet Transform (DMWT) and Quantization Index Modulus Modulation (QIMM). The multiwavelet coefficients of an image are quantized using QIMM and the coefficients are modified according to the binary watermark logo. Watermark extraction does not require the original image. Experimental results show that, the proposed watermarking scheme is robust to image compression and rotational attacks. It is superior to the method proposed by Lin et al. [10] in terms of Peak Signal to Noise Ratio (PSNR) and Bit Error Rate (BER).

Electrodermal activity (EDA) is an important affective indicator, measured conventionally on the fingers with desktop sensing instruments. Recently, a new generation of wearable, battery-powered EDA devices came into being, encouraging the migration of EDA sensing to other body locations. To investigate the implications of such sensor/location shifts in psychophysiological studies we performed a validation experiment. In this experiment we used startle stimuli to instantaneously arouse the sympathetic system of n = 23 subjects while sitting. Startle stimuli are standard but minimal stressors, and thus ideal for determining the sensor and location resolution limit. The experiment revealed that precise measurement of small EDA responses on the fingers and palm is feasible either with conventional or mobile EDA sensors. By contrast, precise measurement of small EDA responses on the sole is challenging, while on the wrist even detection of such responses is problematic for both EDA modalities. Given that affective wristbands have emerged as the dominant form of EDA sensing, researchers should beware of these limitations.

Increased availability of Multi-Core processors is forcing us to redesign algorithms and applications so as to exploit the available computational power from multiple cores. It is not un-common to employ memory mapping of files in applications involving huge I/O bandwidth to improve the response/service times. This paper mainly focuses on performance of memory mapped files on MultiCore processors. Experiments are carried out with k-means algorithm, a popular Data mining (DM) clustering algorithm, to explore the potential of Multi-Core hardware under OpenMP API and POSIX threads. Observations are made both with static and dynamic threads of OpenMP. Experiments are also conducted with both simulated and real data sets. Experiments indicate that memory mapping of files gives considerable benefit on MultiCore processors also. In addition, the benefit increased with increased physical memory size. Also, the benefit of memory mapping with the selected algorithm is increasing with number of cores.

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