High performance 8-bit approximate multiplier using novel 4:2 approximate compressors for fast image processing

In this paper, a novel 8-bit approximate multiplier is proposed based on three novel 4:2 approximate compressors which its delay and error is less than those of the multipliers constructed by traditional 4:2 approximate compressors, and its delay is also less than that of an 8-bit multiplier constructed by using 3:2 precise compressors. To do so, each novel compressor is designed such that its output carry is independent of the output carry of its previous compressor in the multiplier. Therefore, the problem of carry propagation delay is eliminated and a fast multiplier is constructed. To obtain the most accurate multiplier, the best compressor of the three proposed compressors for each multiplier’s column is determined using the genetic algorithm. Moreover, one can use the approximate compressors only at the k least significant multiplier’s columns for more error reduction. The proposed multiplier is used for image blending and image compression. Our simulations show that for example the error and the delay of the proposed method for k=9 is at-least 32.52% and 33.10% less than those of traditional 4:2 approximate compressor based multipliers, respectively.Abstract: In this paper, a novel 8-bit approximate multiplier is proposed based on three novel 4:2 approximate compressors which its delay and error is less than those of the multipliers constructed by traditional 4:2 approximate compressors, and its delay is also less than that of an 8-bit multiplier constructed by using 3:2 precise compressors. To do so, each novel compressor is designed such that its output carry is independent of the output carry of its previous compressor in the multiplier. Therefore, the problem of carry propagation delay is eliminated and a fast multiplier is constructed. To obtain the most accurate multiplier, the best compressor of the three proposed compressors for each multiplier’s column is determined using the genetic algorithm. Moreover, one can use the approximate compressors only at the k least significant multiplier’s columns for more error reduction. The proposed multiplier is used for image blending and image compression. Our simulations show that for example the error and the delay of the proposed method for k=9 is at-least 32.52% and 33.10% less than those of traditional 4:2 approximate compressor based multipliers, respectively

A Novel K-means-based Feature Reduction

The aim of feature reduction is reduction of the size of data file, elimination of irrelevant features, and discovery of the effective data features for data analysis. Irrelevant data features can skew data analysis such as data clustering. Therefore, maintaining the data structure or data clusters must be taken into consideration in feature reduction. In this article, with regard to the success of k-means-based clustering methods, a feature reduction method is presented based on weighted k-means (wk-means). More specifically, firstly, data features are weighted using wk-means method. A feature with a high weight is not a better feature for clustering than a feature with a low weight, necessarily, and the weight of a feature only change feature range for better clustering. Then, by using a novel mathematical model, a group of weighted features with the least effect on data clusters are eliminated and the remaining features are selected. Contrary to sparse k-means method, the number of selected features can be determined explicitly by the user in our proposed method. Experimental results on four real datasets show that the accuracy of clusters obtained by wk-means after feature reduction by the proposed method is better than that of sparse k-means, PCA and LLE

A Novel K-means-based Feature Reduction

The aim of feature reduction is reduction of the size of data file, elimination of irrelevant features, and discovery of the effective data features for data analysis. Irrelevant data features can skew data analysis such as data clustering. Therefore, maintaining the data structure or data clusters must be taken into consideration in feature reduction. In this article, with regard to the success of k-means-based clustering methods, a feature reduction method is presented based on weighted k-means (wk-means). More specifically, firstly, data features are weighted using wk-means method. A feature with a high weight is not a better feature for clustering than a feature with a low weight, necessarily, and the weight of a feature only change feature range for better clustering. Then, by using a novel mathematical model, a group of weighted features with the least effect on data clusters are eliminated and the remaining features are selected. Contrary to sparse k-means method, the number of selected features can be determined explicitly by the user in our proposed method. Experimental results on four real datasets show that the accuracy of clusters obtained by wk-means after feature reduction by the proposed method is better than that of sparse k-means, PCA and LLE

An Approach for Fall Prediction Based on Kinematics of Body Key Points Using LSTM

Many studies have used sensors attached to adults in order to collect signals by which one can carry out analyses to predict falls. In addition, there are research studies in which videos and photographs were used to extract and analyze body posture and body kinematics. The present study proposes an integrated approach consisting of body kinematics and machine learning. The model data consist of video recordings collected in the UP-Fall Detection dataset experiment. Three models based on long-short-term memory (LSTM) network—4p-SAFE, 5p-SAFE, and 6p-SAFE for four, five, and six parameters—were developed in this work. The parameters needed for these models consist of some coordinates and angles extracted from videos. These models are easy to apply to the sequential images collected by ordinary cameras, which are installed everywhere, especially on aged-care premises. The accuracy of predictions was as good as 98%. Finally, the authors discuss that, by applying these models, the health and wellness of adults and elderlies will be considerably promoted