The efficiency–accuracy trade-off of approximate-computing spans a diverse array of techniques at both the hardware and software levels. While this diversity is key to the success of approximation research, it also entails considerable complexity in developing and validating new approximation techniques. To overcome this complexity and foster innovation in research, we propose REACT, a modeling...

This paper proposes a nonparametric steganalysis method for quantization index modulation (QIM) based steganography. The proposed steganalysis method uses irregularity (or randomness) in the test-image to distinguish between the coverand the stego-image. We have shown that plain-quantization (quantization without message embedding) induces regularity in the resulting quantized-image; whereas me...

Information entropy as a universal and fascinating statistical concept is helpful for numerous problems in the computational sciences. Approximate entropy (ApEn), introduced by Pincus (1991), can classify complex data in diverse settings. The capability to measure complexity from a relatively small amount of data holds promise for applications of ApEn in a variety of contexts. In this work we a...

New directions in computing and algorithms has lead to some new applications that have tolerance to imprecision. Although, These applications are creating large volumes of data which exceeds the capability of today’s computing systems. Therefore, researchers are trying to find new techniques to alleviate this crisis. Approximate Computing is one promising technique that uses a trade off between...

Emerging applications such as R(ecognition), M(ining), and S(ynthesis) suit themselves well to approximate computing due to their intrinsic noise tolerance. RMS applications process massive, yet noisy and redundant data by probabilistic, often iterative, algorithms. Usually the solution space has many more elements than one, rendering a range of application outputs valid, as opposed to a single...

in this paper, we prove some approximate fixed point results for proximinal valued $beta$-contractive multifunctions on metric spaces. we show that our results generalize some old fixed point results in the literature.