A Novel Approach of ECG Signal Enhancement Using Adaptive Filter Based on Whale Optimization Algorithm

An Electro cardiogram is commonly used in biomedical signal processing. It is used to monitor minor electrical changes in the human body. The electrical changes originate due to the function of heart. The anomalies of heart are found by ECG. In this work the Whale optimization algorithm is used to de-noising the ECG signal. The Whale optimization algorithm is used with Adaptive filter which filter the corrupted ECG signal. The performance of the ANC will be improved by calculating the optimum weight value. The WOA technique gives the best result on the different fidelity parameter compare to PSO, MPSO and ABC. The WOA technique gives the significant improvement in accuracy. It gives a good SNR, MSE, ME result compare to PSO, MPSO and ABC. The WOA gives 80% improvement in SNR 88% in MSE and 89% in ME as compared to PSO. So, by using WOA we get a desired ECG component. The WOA reduces the noise in ECG signal and improves the quality of signal.

Segmentation of Enhalus acoroides seagrass from underwater images using the Mask R-CNN method

Seagrass is a Spermatophyta plant that has many roles, including as a primary producer in the food chain in the waters. Monitoring of seagrass meadows and conditions needs to be done in order to achieve a healthy marine ecosystem. The steps taken in monitoring seagrass are by detecting and segmenting it. The purpose of the study is to implement and get information about the performance of the Mask R-CNN algorithm in detecting and segmenting the Enhalus acoroides. The dataset consists of 500 Enhalus acoroides images that had gone through a color correction and labelling process. The training process was performed with the configuration of 0.001 learning rate, batch size of 4 and some image augmentation was used to avoid overfitting. The optimum weight value was obtained after conducting the learning process with 100 epochs. A confusion matrix was used to evaluate detection performance, and linear regression was used to evaluate the segmentation produced by the model. The model evaluation results showed an accuracy value of 0.9246, a precision value of 0.9507, a recall value of 0.9712 and a correlation coefficient value of 0.8771. The value indicates that the model can detect and segment the seagrass Enhalus acoroides well and accurately.

A Randomly Weighted Minimum Arborescence with a Random Cost Constraint

We study the minimum spanning arborescence problem on the complete digraph [Formula: see text], where an edge e has a weight We and a cost Ce, each of which is an independent uniform random variable Us, where [Formula: see text] and U is uniform [Formula: see text]. There is also a constraint that the spanning arborescence T must satisfy [Formula: see text]. We establish, for a range of values for [Formula: see text], the asymptotic value of the optimum weight via the consideration of a dual problem.

The Development of Automatic Pineapple Washing and Drying Machine

Indonesia is a country that produce a large variety and quantity of fruit. One of the prominent fruits is pineapple. Small and medium-sized industries that process pineapples into derivative products have sprung up in various regions. However, most of the processing is still conducted manually. One thing that still needs to be improved is the washing and drying process. Therefore, this research developed an automatic washing and drying machine. The developed machine uses a one-tube system so it could reduce the transfer process from one stage to the next. Testing on the developed machines has been carried out. The test results show that this machine is capable to process as much as 6.5 kg of pineapple in one batch. Another test to determine the most effective duration of washing and drying process was also carried out. The test results show that the effective washing time is 180 seconds, and the drying process is 90 seconds. Apart from the level of cleanliness and dryness of pineapple, this test also observes the damage of pineapple as a result of the washing and drying process. Simulations to measure the total time to operate the machine have been carried out. The data shows that it takes 303 seconds, starting from inserting pineapple to the tube, washing, draining, and finally removing pineapples from the tube. By using both data of average total processing time and optimum weight, the capacity of developed machine could be calculated. The capacity of this machine is around 77 kg per hour or 8.008 kg per month. With this capacity, this machine can be used by SME with a large production capacity.

Experimental Analysis on Use of Polypropylene in Concrete

Polypropylene may be a thermoplastic polymer utilized as a neighborhood of wide assortment of uses including bundling, materials (e.g., ropes, warm clothing and covers). Polymer cement may be a piece of gathering of cements that utilizes polymers to supplement bond as a canopy. Impregnated solid, polymer cement, and Polymer-Portland-bond concrete are the sorts incorporate polymers. To realize maximum strength of concrete by using optimum weight of polypropylene fibers is the aim of the study. Fiber ferroconcrete is employed during a sort of engineering applications due to its satisfactory and outstanding performance within the industry and construction field. Polypropylene fiber in concrete mix design is used for multiple purposes that include rigid pavement, self- compacting concrete and other applications. 40 cylinders of polypropylene concrete were casted and tested for 7 and 28 days’ strength for both compressive and split lastingness. It was concluded that the many improvement was observed in ultimate compressive strength after 7 and 28 days. The optimum percentage of Polypropylene fiber was obtained to be 5 percent of cement by volume. The addition of bit of polypropylene improved the mechanical properties of concrete.

Optimally-Weighted Image-Pose Approach (OWIPA) for Distracted Driver Detection and Classification

Distracted driving is the prime factor of motor vehicle accidents. Current studies on distraction detection focus on improving distraction detection performance through various techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, the research on detection of distracted drivers through pose estimation is scarce. This work introduces an ensemble of ResNets, which is named Optimally-weighted Image-Pose Approach (OWIPA), to classify the distraction through original and pose estimation images. The pose estimation images are generated from HRNet and ResNet. We use ResNet101 and ResNet50 to classify the original images and the pose estimation images, respectively. An optimum weight is determined through grid search method, and the predictions from both models are weighted through this parameter. The experimental results show that our proposed approach achieves 94.28% accuracy on AUC Distracted Driver Dataset.

Convolutional neural network for 2D adaptive beamforming of phased array antennas with robustness to array imperfections

Achieving robust and fast two-dimensional adaptive beamforming of phased array antennas is a challenging problem due to its high-computational complexity. To address this problem, a deep-learning-based beamforming method is presented in this paper. In particular, the optimum weight vector is computed by modeling the problem as a convolutional neural network (CNN), which is trained with I/O pairs obtained from the optimum Wiener solution. In order to exhibit the robustness of the new technique, it is applied on an 8 × 8 phased array antenna and compared with a shallow (non-deep) neural network namely, radial basis function neural network. The results reveal that the CNN leads to nearly optimal Wiener weights even in the presence of array imperfections.

A Randomly Weighted Minimum Spanning Tree with a Random Cost Constraint

We study the minimum spanning tree problem on the complete graph $K_n$ where an edge $e$ has a weight $W_e$ and a cost $C_e$, each of which is an independent copy of the random variable $U^\gamma$ where $\gamma\leq 1$ and $U$ is the uniform $[0,1]$ random variable. There is also a constraint that the spanning tree $T$ must satisfy $C(T)\leq c_0$. We establish, for a range of values for $c_0,\gamma$, the asymptotic value of the optimum weight via the consideration of a dual problem.

Effects of varying levels of dietary protein on the performance of rabbits

Sixteen Newzealand White rabbits aged 9.5 = 0.5 weeks weighing 0.72-0.99kg were used to evaluate the experimental rations which allowed for good weight gain. They were divided into 4 groups and allotted four different diets for the treatment groups with 10, 13, 16 and 20% CP. Feed intake (FI) for the treatment groups were 58.52, 61.46, 57.35, and 61.06g while average daily gain (ADG) were 4.22, 4.35, 5.32 and 6.02g for the groups. There were significant increase (P<0.01) in the digestibility of CP, EE and NFE within the treatment groups. Diets containing 16-20% CP may provide adequate nutrients for optimum weight gain of Newzealand rabbits.

An experimental investigation on low-cycle fatigue behavior of GO-NH2-reinforced epoxy adhesive

In this study, the effect of adding functionalized aminated graphene oxide up to 0.3 wt% was experimentally investigated on the mechanical properties and fatigue behavior of an epoxy adhesive with intermediate toughness. Accordingly, graphene oxide was prepared by a modified Hummers method and was modified with 3-aminopropyltrimethoxysilane silane agent. Bulk samples were then prepared from neat and reinforced adhesive with different weight amounts of aminated graphene oxide. Dumbbell-shaped samples with a notch were used to limit displacement at break. The results showed the presence of 0.1 wt% and 0.3 wt% aminated graphene oxide nanoplatelets, as optimum weight percentages respectively for the mechanical properties and the fatigue life, improves the modulus of elasticity by 35% and the tensile strength by 44% as well as the fatigue life by about 72–241% under different cyclic loading conditions.