Using Photoplethysmography for Simple Hand Gesture Recognition

A new wearable band is developed which uses three Photoplethysmography (PPG) sensors for the purpose of hand gesture recognition (HGR). These sensors are typically used for heart rate estimation and detection of cardiovascular diseases. Heart rate estimates obtained from these sensors are disregarded when the arm is in motion on account of artifacts. This research suggests and demonstrates that these artifacts are repeatable in nature based on the gestures performed. A comparative study is made between the developed band and the Myo Armband which uses surface-Electromyography (s-EMG) for gesture recognition. Based on the results of this paper which employs supervised machine learning techniques, it can be seen that PPG can be utilized as a viable alternative modality for gesture recognition applications

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Turbulent heat transfer in a trapezoidal channel with transverse and v-shaped ribs on two opposite walls

This study investigates the turbulent heat transfer and friction in a trapezoidal channel with opposite walls roughened with transverse and v-shaped ribs. The roughened channel depicts the internal cooling passage of an aerofoil near the trailing edge. The various configurations investigated for this study are smooth channel, channel with 90° transverse ribs and channel with v-shaped ribs angled at 45°. The pitch-toheight ratio (P/e), rib height-to-hydraulic diameter ratio (e/Dh) and the aspect ratio (W/e) were maintained at 12, 0.1906 and 1, respectively. The configuration was tested for Reynolds number ranging from 7,000 to 40,000. The 45° rib was found to produce the maximum heat transfer and minimum pressure loss

Evaluating Content-centric vs User-centric Ad Affect Recognition

Despite the fact that advertisements (ads) often include strongly emotional content, very little work has been devoted to affect recognition (AR) from ads. This work explicitly compares content-centric and user-centric ad AR methodologies, and evaluates the impact of enhanced AR on computational advertising via a user study. Specifically, we (1) compile an affective ad dataset capable of evoking coherent emotions across users; (2) explore the efficacy of content-centric convolutional neural network (CNN) features for encoding emotions, and show that CNN features outperform low-level emotion descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram (EEG) responses acquired from eleven viewers, and find that EEG signals encode emotional information better than content descriptors; (4) investigate the relationship between objective AR and subjective viewer experience while watching an ad-embedded online video stream based on a study involving 12 users. To our knowledge, this is the first work to (a) expressly compare user vs content-centered AR for ads, and (b) study the relationship between modeling of ad emotions and its impact on a real-life advertising application.Comment: Accepted at the ACM International Conference on Multimodal Interation (ICMI) 201

Affect Recognition in Ads with Application to Computational Advertising

Advertisements (ads) often include strongly emotional content to leave a lasting impression on the viewer. This work (i) compiles an affective ad dataset capable of evoking coherent emotions across users, as determined from the affective opinions of five experts and 14 annotators; (ii) explores the efficacy of convolutional neural network (CNN) features for encoding emotions, and observes that CNN features outperform low-level audio-visual emotion descriptors upon extensive experimentation; and (iii) demonstrates how enhanced affect prediction facilitates computational advertising, and leads to better viewing experience while watching an online video stream embedded with ads based on a study involving 17 users. We model ad emotions based on subjective human opinions as well as objective multimodal features, and show how effectively modeling ad emotions can positively impact a real-life application.Comment: Accepted at the ACM International Conference on Multimedia (ACM MM) 201

Durability properties of fly ash and silica fume blended concrete for marine environment

1803-1812The improvement in durability and strength by replacing the conventional components with supplementary materials in concrete is one of the recently focused areas in concrete technology. From the previous till the recent times serious efforts have been taken to improve the structural adequacy and durability characteristics of concrete so as to efficiently replace the usual conventional concrete. In this present research work, the mechanical and durability properties of the concrete blended with fly ash (FC) and silica fume (SC) are studied in detail. The partial replacement of cement with silica fume and fly ash in the concrete improves the overall property of the concrete, gives a way for the reuse of the supplementary material to be efficiently brought back giving a cleaner environment. The fly ash is used with the replacement percentages of 10, 15 and 20 of the cement whereas for silica fume the replacement percentages are 8, 10 and 12, respectively. Also the study is extended to combination mixes to test the strength and durability and it has been found that the increase in the percentage of the silica fume increases the strength reduces the workability and permeability to a high extent and the inclusion of the fly ash paves a way for the increase in the durability property. The effect of the cementitious material with FC and SC on the concrete is compared with the nominal concrete and also the suitability in the usage of marine environment is validated in accordance with the International codes

Benchmarking of Cell Throughput Using Proportional Fair Scheduler in a Single Cell Environment

The proportional fair (PF) scheduling algorithm compromises between cell throughput and fairness. Many research findings have been published by various researchers about PF algorithm based on mathematical model and simulations. In this paper we have taken the practical route to analyse the algorithm based on three types of subscription. In this benchmarking study, the user subscriptions are differentiated as Gold, Silver and Bronze schemes and they are provisioned with certain throughputs. Apart from subscriptions plans, the channel condition also plays a major role in determining the throughput. So in order to ensure fairness among different subscriptions even in the bad channel conditions and to deliver the provisioned throughputs certain priorities are attached with the subscriptions. As per the subscription plans Gold subscribers are assigned with 50% of the speed offered by the network as maximum based on CAT3 speed (100 Mbps in DL and 50 Mbps in UL), Silver is assigned with 25% of the max speed and Bronze is assigned with 12% of the max speed. The priorities assigned to subscribers determines the fairness in the unfavourable channel conditions - Bronze (high), Silver and Gold (medium). In this paper, an benchmarking tests have been performed with all of three types of subscribers for nearly two hours in the live single cell network without any heterogeneous cells influencing it. Furthermore, the results are compared with the simulation results

Grey Wolf Optimizer and Cuckoo Search Algorithm for Electric Power System State Estimation with Load Uncertainty and False Data

State estimate serves a crucial purpose in the control centre of a modern power system. Voltage phasor of buses in such configurations is referred to as state variables that should be determined during operation. A precise estimation is needed to define the optimal operation of all components. So many mathematical and heuristic techniques can be used to achieve the aforementioned objective. An enhanced power system state estimator built on the cuck search algorithm is described in this work. Several scenarios, including the influence of load uncertainty and the likelihood of false data injection as significant challenges in electrical energy networks, are proposed to analyse the operation of estimators. The ability to identify and correct false data is also assessed in this regard. Additionally, the performance of the presented estimator is compared to that of the weighted least squares, Cuckoo Search algorithm and grey wolf Optimizer. The findings demonstrate that the grey wolf Optimizer overcomes the primary shortcomings of the conventional approaches, including accuracy and complexity, and is also better able to identify and rectify incorrect data. On IEEE 14-bus and 30-bus test systems, simulations are run to show how well the method works