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Inexact design has been recognized as very viable approach to achieve significant gains in the energy, area and speed efficiencies of digital circuits. By deliberately trading error in return for such these gains, inexact circuits and architectures have been shown to be especially useful in contexts where our senses such as sight and hearing, can compensate for the loss in accuracy. It is therefore important to understand, characterize the manner in which our sensorial systems interact and compensate for the loss in accuracy. Further use this knowledge to optimize and guide the manner in which inexactness is introduced. For the first time, we achieve both of these goals in this paper in the context of human audition-specifically, using the architecture of a hearing-aid and the DSP primitive of an FIR filter as our candidate. Our algorithms for designing an inexact hearing-aid thus use intelligibility as the metric. The resulting inexact FIR filter in the hearing aid is 1.5X or 1.8X more efficient in terms of power-area product while producing 5% or 10% less intelligible speech respectively when compared with the corresponding exact version. 1

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Plant species recognition from visual data has always been a challenging task for Artificial Intelligence (AI) researchers, due to a number of complications in the task, such as the enormous data to be processed due to vast number of floral species. There are many sources from a plant that can be used as feature aspects for an AI-based model, but features related to parts like leaves are considered as more significant for the task, primarily due to easy accessibility, than other parts like flowers, stems, etc. With this notion, we propose a plant species recognition model based on morphological features extracted from corresponding leaves’ images using the support vector machine (SVM) with adaptive boosting technique. This proposed framework includes the pre-processing, extraction of features and classification into one of the species. Various morphological features like centroid, major axis length, minor axis length, solidity, perimeter, and orientation are extracted from the digital images of various categories of leaves. In addition to this, transfer learning, as suggested by some previous studies, has also been used in the feature extraction process. Various classifiers like the kNN, decision trees, and multilayer perceptron (with and without AdaBoost) are employed on the opensource dataset, FLAVIA, to certify our study in its robustness, in contrast to other classifier frameworks. With this, our study also signifies the additional advantage of 10-fold cross validation over other dataset partitioning strategies, thereby achieving a precision rate of 95.85%.