A low power model for Code Division Multiple Access (CDMA) based cellular communication system is developed. The dynamic power is minimized by reducing the frequency of the Phase Lock Loop (PLL) after lock is established. The paper addresses the feasibility of lowering the clock frequency of the processing unit that models the PLL is addressed and modulator/demodulator functions of the system while maintaining synchronization with the memory unit and other peripherals. The system is simulated with Matlab considering various Signal-to-Noise Ratios (SNR). For a given SNR, the minimum frequency required for the PLL to maintain lock is determined. The Matlab file is translated to VHDL code, simulated and synthesized with Mentor tools, and the layout then generated. Mach-Pa 5-V software system from Mentor tools is utilized to estimate the power consumed by the simulated device. A Xilinx file is also generated and downloaded for Field Programmable Gate Arrays (FPGA) implementation. A 50 MHz clock frequency of the processing unit was first considered and then lowered to 20 MHz for the low power study. Lowering the base and clock frequency resulted in near 30% reduction in power
In this paper, we describe the project, the weekly activities of the team, the method for assessing teamwork, the results of the assessment of teamwork, the outcomes of the project, and the website. This paper covers both the technical and educational activities of the senior capstone design project including design approaches and weekly topics given by guest lectures that assist student accomplishment while in progressing with their technical activities. II.
One of the central goals of precision health is the understanding and interpretation of high-dimensional biological data to identify genes and markers associated with disease initiation, development and outcomes. Significant effort has been committed to harness gene expression data as real-valued matrices for multiple analyses while accounting for time-to-event modeling by including survival times. Traditional biological analysis has focused separately on non-negative matrix factorization (NMF) of the gene expression data matrix and survival regression with Cox proportional hazards model. In this work, Cox proportional hazards regression is integrated with NMF by imposing survival constraints. This is accomplished by jointly optimizing the Frobenius norm and partial log likelihood for events such as death or relapse. Simulation results based on synthetic data demonstrated the superiority of the proposed methodology, when compared to other NMF algorithms, in finding survival associated gene clusters. In addition, using breast cancer gene expression data, the proposed technique can unravel critical clusters of cancer genes. The discovered gene clusters reflect rich biological implications and can help identify survival-related biomarkers. Towards the goal of precision health and cancer treatments, the proposed algorithm can help understand and interpret high-dimensional heterogeneous genomics data with accurate identification of survival-associated gene clusters.
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