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Updates found with 'different base stations'

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Updates found with 'different base stations'

VLSI PROJECTS ABSTRACT 2016 -2017A LOW-COST RADIATION-HARDENED METHOD FOR PIPELINE PROTECTION IN MICROPROCESSORSABSTRACT: The aggressive scaling of semiconductor technology has significantly increased the radiation-induced soft-error rate in modern microprocessors. Meanwhile, due to the increasing complexity of modern processor pipelines and the limited error-tolerance capabilities that previous radiation hardening techniques can provide, the existing pipeline protection mechanisms cannot achieve complete protection. This paper proposes a complete and cost-effective pipeline protection mechanism using a self-checking architecture. The radiation-hardened pipeline is achieved by incorporating soft-error- and timing-error-tolerant flip-flop (SETTOFF)-based self-checking cells into the sequential cells of the pipeline. A replay recovery mechanism is also developed at the architectural level to recover the detected errors. The proposed pipeline protection technique is implemented in an Open RISC microprocessor in a 65-nm technology. A gate-level transient fault-injection and analysis technique is used to evaluate the error-tolerance capability of the proposed hardened pipeline design. The results show that compared with the techniques such as triple modular redundancy, the SETTOFF-based self-checking technique requires over 30% less area and 80% less power overheads. Meanwhile, the error-tolerant and self-checking capabilities of the register allow the proposed pipeline protection technique to provide a noticeably higher level of reliability for different parts of the pipeline compared with the previous pipeline protection techniques.
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VLSI PROJECTS ABSTRACT 2016 -2017LOW-POWER SYSTEMS FOR DETECTION OF SYMPTOMATIC PATTERNS IN AUDIO BIOLOGICAL SIGNALSABSTRACT: In this paper, we present a low-power, efficacious, and scalable system for the detection of symptomatic patterns in biological audio signals. The digital audio recordings of various symptoms, such as cough, sneeze, and so on, are spectrally analyzed using a discrete wavelet transform. Subsequently, we use simple mathematical metrics, such as energy, quasi-average, and coastline parameter for various wavelet coefficients of interest depending on the type of pattern to be detected. Furthermore, a mel-frequency cepstrum-based analysis is applied to distinguish between signals, such as cough and sneeze, which have a similar frequency response and, hence, occur in common wavelet coefficients. Algorithm-circuit codesign methodology is utilized in order to optimize the system at algorithm and circuit levels of design abstraction. This helps in implementing a low-power system as well as maintaining the efficacy of detection. The system is scalable in terms of user specificity as well as the type of signal to be analyzed for an audio symptomatic pattern. We utilize multiplierless implementation circuit strategies and the algorithmic modification of mel cepstrum computation to implement low power system in the 65-nm bulk Si technology. It is observed that the pattern detection system achieves about 90% correct classification of five types of audio health symptoms. We also scale the supply voltage due to lower frequency of operation and report a total power consumption of ~184 µW at 700 mV supply.
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