Hybrid wired-wireless Network-on-Chip (WiNoC) has emerged as an alternative solution to the poor scalability and performance issues of conventional wireline NoC design for future System-on-Chip (SoC). Existing feasible wireless solution for WiNoCs in the form of millimeter wave (mm-Wave) relies on free space signal radiation which has high power dissipation with high degradation rate in the signal strength per transmission distance. Moreover, over the lossy wireless medium, combining wireless and wireline channels drastically reduces the total reliability of the communication fabric. Surface wave has been proposed as an alternative wireless technology for low power on-chip communication. With the right design considerations, the reliability and performance benefits of the surface wave channel could be extended. In this paper, we propose a surface wave communication fabric for emerging WiNoCs that is able to match the reliability of traditional wireline NoCs. First, we propose a realistic channel model which demonstrates that existing mm-Wave WiNoCs suffers from not only free-space spreading loss (FSSL) but also molecular absorption attenuation (MAA), especially at high frequency band, which reduces the reliability of the system. Consequently, we employ a carefully designed transducer and commercially available thin metal conductor coated with a low cost dielectric material to generate surface wave signals with improved transmission gain. Our experimental results demonstrate that the proposed communication fabric can achieve a 5dB operational bandwidth of about 60GHz around the center frequency (60GHz). By improving the transmission reliability of wireless layer, the proposed communication fabric can improve maximum sustainable load of NoCs by an average of 20.9% and 133.3% compared to existing WiNoCs and wireline NoCs, respectively.

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Amyotrophic lateral sclerosis, also known as ALS, is a progressive nervous system disorder that affects nerve cells in the brain and spinal cord, resulting in the loss of muscle control. For individuals with ALS, where mobility is limited to the movement of the eyes, the use of eye-tracking-based applications can be applied to achieve some basic tasks with certain digital interfaces. This paper presents a review of existing eye-tracking software and hardware through which eye-tracking their application is sketched as an assistive technology to cope with ALS. Eye-tracking also provides a suitable alternative as control of game elements. Furthermore, artificial intelligence has been utilized to improve eye-tracking technology with significant improvement in calibration and accuracy. Gaps in literature are highlighted in the study to offer a direction for future research.

Machine learning approaches are powerful techniques commonly employed for developing cancer prediction models using associated gene expression and mutation data. Our survey provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (breast, lung, kidney, ovarian, liver, central nervous system and gallbladder) for survival prediction,tumor identification and stratification. We also provide an overview of biomarker studies that are associated with these cancer types. The survey captures multiple aspects of machine learning associated cancer studies,including cancer classification, cancer prediction, identification of biomarker genes, microarray, and RNA-Seq data. We discuss the technical issues with current cancer prediction models and the corresponding measurement tools for determining the activity levels of gene expression between cancerous tissues and noncancerous tissues. Additionally, we investigate how identifying putative biomarker gene expression patterns can aid in predicting future risk of cancer and inform the provision of personalized treatment.

Three-dimensional Network-on-Chip (3D NoC) architectures have gained a lot of popularity to solve the on-chip communication delays of next generation System-on-Chip (SoC) systems. However, the vertical interconnects of 3D NoC are expensive and complex to manufacture. Also, 3D router architecture consumes more power and occupies more area per chip°oorplan compared to a 2D router. Hence, more e±cient architectures should be designed. In this paper, we propose area e±cient and low power 3D heterogeneous NoC architectures, which combines both the power and performance bene¯ts of 2D routers and 3D NoC-bus hybrid router architectures in 3D NoC architectures. Experimental results show a negligible penalty (less than 5%) in average packet latency of the proposed heterogeneous 3D NoC architectures compared to typical homogeneous 3D NoCs, while the heterogeneity provides power and area e±ciency of up to 61% and 19.7%, respectively.