Design and Implementation of Smart Glass with Voice Detection Capability to Help Visually Impaired People

The main components of Cognitive Radio networks are Primary Users (PU) and Secondary Users (SU). The most essential method used in Cognitive networks is Spectrum Sensing, which detects the spectrum band and opportunistically accesses the free white areas for different users. Exploiting the free spaces helps to increase the spectrum efficiency. But the existing spectrum sensing techniques such as energy detectors, cyclo-stationary detectors suffer from various problems such as complexity, non-responsive behaviors under low Signal to Noise Ratio (SNR) and computational overhead, which affects the performance of the sensing accuracy. Many algorithms such as Long-Short Term Memory (LSTM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) play an important role in designing intelligent spectrum sensing techniques due to the excellent learning ability of deep learning frameworks, but still require improvisation in terms of sensing accuracy under dynamic environmental conditions. This paper, we propose the novel and hybrid CNN-Cuttle-Fish Optimized Long Short Term Memory (COLSTM), an improved version of LSTM that is well suited for the dynamic changes of environmental SNR with less computational overhead and complexity. The proposed COLSTM based spectrum sensing technique exploits the various statistical features from spectrum data of PU to improve the sensing efficiency. Furthermore, the addition of shuttle-fish optimization in LSTM has reduced the computational overhead and complexity which in turn enhanced the sensing performances. The proposed methodology is validated on spectrum data acquired using RaspberryPi-RTLSDR experimental test-beds. The proposed spectrum sensing technique and the existing classical spectrum sensing techniques are compared. Experimental results show that the proposed scheme has shown the brighter enhancement of performance under different SNR environments. Further, the improvised performance has been achieved at low complexity and low computational overhead when compared with the other existing LSTM networks.

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“…3 illustrates the functioning algorithm. The detailed working of the proposed cuttlefish algorithm is explained in [33,34].…”

Section: Cuttle Fish Algorithm-an Overviewmentioning

confidence: 99%

Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

Arunachalam¹,

SureshKumar²

2023

Computer Systems Science and Engineering

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“…After that, the jar was shaken at least twice a day. As orange oil may irritate skin, gloves were used while preparing the oil [15]. The Mason jar was opened and the mixture was strained using a strainer.…”

Section: Icmmt-22mentioning

confidence: 99%

DI-diesel engine fueled with orange peel calophylluminophyllum seed blended biodiesel

Balaji

Shenbagaraman

Ranjith

2023

J. Phys.: Conf. Ser.

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Engines that run on diesel fuel are very important to the functioning of contemporary society. At the same time, they are a significant contributor to the pollution of the atmosphere. Because both the comfort of the atmosphere and the inter-related, we are unable to cut down on the usage of diesel at this time. This is a very precarious scenario. The number of vehicles that run on diesel continues to grow on a daily basis. We have no choice but to switch to alternate fuels such as biodiesel if we want to prevent this problem. In the current experiment, orange peel oil was combined with diesel in two distinct proportions: 20% orange oil and 80% diesel, and 40% orange oil and 60% diesel. The purpose of this was to compare the two different combinations. According to the findings, the mixture consisting of 40% orange oil and 60% diesel is the most effective one. Then, Calophyllum inophyllum oil was further added using a transesterification process mixed with potassium hydroxide, isopropyl alcohol, pH indicator, and methanol. Then, different cases were tested: (40% orange oil + 20% Calophyllum inophyllum + 60% diesel), (40% orange oil + 40% Calophyllum inophyllum + 20% diesel) and (100% diesel). The tests show that the break thermal efficiency of O40+C20+D40 and O40+C40+D20 is 4.36 % and 4.76 % which is almost similar. O40+C40+D20 increase the NOx emissions slightly, but it decreases the emissions of HC, CO, CO2, and smoke compared to O40+C20+D40. So, O40+C40+D20 is the best alternative fuel for diesel engines.

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“…In the research that has been done on the topic, color extensions are often broken down into three distinct categories: output fusion techniques, multi-dimensional gradient methods, and vector approaches. Given that the objective is to combine the results made by applying the detector to each component of the color space that is being considered [15], output fusion seems to be the method that is both the most often used and the one that costs the least amount of money.…”

Section: Literature Reviewmentioning

confidence: 99%

Application access for chromatic color detection

Umarani,

Dimpusowjanya,

Poojitha

et al. 2023

J. Phys.: Conf. Ser.

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The process of identifying specific examples of an item inside an image or video is referred to as “object detection,” and it is one of the computer vision techniques. When individuals look at pictures or movies, they are able to recognize and find things that are of interest to them. The primary goal of the field of object recognition is to achieve this level of intelligence via the use of a computer. Image segmentation and blob analysis, which uses simple object properties such as size, shape, or color, and Feature-based object detection, which uses feature extraction, matching, and RANSAC to estimate the location of an object, are two of the many common techniques that are used in conjunction with deep learning and machine learning-based object detection. There are also many other common techniques that are used in conjunction with deep learning and machine learning-based object detection. The most challenging aspects of object detection are the processes of categorizing things and pinpointing their locations. When seen from a different perspective, the same object might seem to be completely different. Detection algorithms have a tough time distinguishing between various things at different scales and perspectives since the sizes and proportions of items almost always alter. The camera input would be used to determine an object’s shape, according to this project’s proposal. Circle, square, and triangle are the three distinct shapes that we are determining an item to be right now. The finished result is shown in MATLAB’s graphical user interface (GUI). The productive identification process is carried out with the assistance of MATLAB. Using the SFTA Algorithm, the application extracts features from the specific image of a video. The neural network algorithm is used to classify the image of video. Image segmentation and image detection are two examples of related computer vision methods that are inextricably intertwined with object recognition. These techniques allow us to better comprehend and analyze the situations shown in movies and still photographs. It has been determined whether or not the suggested system is suitable. Because of this, the testing has shown that the suggested system generates generally accurate indications of real performance at a rate of 99.4%.

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Design and Implementation of Smart Glass with Voice Detection Capability to Help Visually Impaired People

Cited by 14 publications

References 8 publications

Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

DI-diesel engine fueled with orange peel calophylluminophyllum seed blended biodiesel

Application access for chromatic color detection

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