Efficient recursive least squares parameter estimation algorithm for accurate nanoCMOS variable gain amplifier performances

ABSTRACT

With the aggressive scaling of integrated circuit technology, parametric estimation is a critical task for designers who looked for solutions to the challenges of some Nanoscale CMOS parameters. This paper presented the prediction of primary parameters of CMOS transistor for 16 nm to 10 nm process nodes using both of Bisquare Weights (BW) method and a novel recursive least squares (RLS) parameter estimation algorithm. The proposed RLS algorithm consists of the minimisation of a quadratic criterion relating to the prediction error in order to attain the best estimated parameters of the developed mathematical model. The obtained results thanks to the proposed RLS algorithm were better than those reached using the BW method. Comparisons between Predictive Technology Model (PTM) data and parameters estimated with RLS algorithm were made to check the validity and the consistency of the proposed algorithm. These predicted primary parameters were helpful to estimate and to optimise the performances of the Variable Gain Amplifier (VGA) which was a basic circuit element with a key role in the design of new upcoming receivers.     

Secret swarm unit: Reactive k-secret sharing

Secret sharing is a fundamental cryptographic task. Motivated by the virtual automata abstraction and swarm computing, we investigate an extension of the k-secret sharing scheme, in which the secret shares are changed on the fly, independently and without (internal) communication, as a reaction to a global external trigger. The changes are made while maintaining the requirement that k or more secret shares may reconstruct the secret and no k ? 1 or fewer can do so.The application considered is a swarm of mobile processes, each maintaining a share of the secret which may change according to common outside inputs, e.g., inputs received by sensors attached to the process.The proposed schemes support addition and removal of processes from the swarm, as well as corruption of a small portion of the processes in the swarm.     

Ex Vivo Human Colon Tissue Exposure to Pristine Graphene Activates Genes Involved in the Binding,Adhesion and Proliferation of Epithelial Cells

Toxicology studies on pristine graphene are limited and lack significant correlations with actual human response. The goal of the current study was to determine the response of total colonic human tissue to pristine graphene exposure. Biopsy punches of colon tissues from healthy human were used to assess the biological response after ex vivo exposure to graphene at three different concentrations (1, 10, and 100 µg/mL). mRNA expression of specific genes or intestinal cytokine abundance was assessed using real-time PCR or multiplex immunoassays, respectively. Pristine graphene-activated genes that are related to binding and adhesion (GTPase and KRAS) within 2 h of exposure. Furthermore, the PCNA (proliferating cell nuclear antigen) gene was upregulated after exposure to graphene at all concentrations. Ingenuity pathway analysis revealed that STAT3 and VEGF signaling pathways (known to be involved in cell proliferation and growth) were upregulated. Graphene exposure (10 µg/mL) for 24 h significantly increased levels of pro-inflammatory cytokines IFNγ, IL-8, IL-17, IL-6, IL-9, MIP-1α, and Eotaxin. Collectively, these results indicated that graphene may activate the STAT3–IL23–IL17 response axis. The findings in this study provide information on toxicity evaluation using a human-relevant ex vivo colon model and serve as a basis for further exploration of its bio-applications.     

Design of low power single-stage bias current control technique- based DVGA for LTE receivers

Analog Integrated Circuits and Signal Processing - In this study, a novel single-stage Digital Variable Gain Amplifier architecture (DVGA) was presented for Long Time Evolution (LTE) receivers. The...