The Development of an Automated Waste Segregator

Accumulation of waste is a major global concern, and recycling is considered one of the most effective methods to solve the problem. However, recycling requires proper segregation of waste according to waste types. This paper develops an automatic waste segregator, capable of identifying and segregating six types of wastes; metal, paper, plastic, glass, cardboard, and others. The proposed system employs Convolutional Neural Network (CNN) technology, specifically the Inception-v3 architecture, as well as two physical sensors; weight and metal sensors, to classify and segregate the waste. Overall classification accuracy of the system is 86.7%. Classification performance of the developed waste segregator has been evaluated further using the precision and recall; with high precision obtained for cardboard, metal, and other waste types, and high recall for metal and glass. These results demonstrate the applicability of the developed system in effectively segregating waste at source, and thereby, reducing the need for the commonly labor-intensive segregation at waste facility. Deploying the system has the potential of reducing waste management problems by assisting recycling companies in sorting recyclable waste, through automation

The Development of an Automated Waste Segregator

Accumulation of waste is a major global concern, and recycling is considered one of the most effective methods to solve the problem. However, recycling requires proper segregation of waste according to waste types. This paper develops an automatic waste segregator, capable of identifying and segregating six types of wastes; metal, paper, plastic, glass, cardboard, and others. The proposed system employs Convolutional Neural Network (CNN) technology, specifically the Inception-v3 architecture, as well as two physical sensors; weight and metal sensors, to classify and segregate the waste. Overall classification accuracy of the system is 86.7%. Classification performance of the developed waste segregator has been evaluated further using the precision and recall; with high precision obtained for cardboard, metal, and other waste types, and high recall for metal and glass. These results demonstrate the applicability of the developed system in effectively segregating waste at source, and thereby, reducing the need for the commonly labor-intensive segregation at waste facility. Deploying the system has the potential of reducing waste management problems by assisting recycling companies in sorting recyclable waste, through automation

Robust cepstral feature for bird sound classification

Birds are excellent environmental indicators and may indicate sustainability of the ecosystem; birds may be used to provide provisioning, regulating, and supporting services. Therefore, birdlife conservation-related researches always receive centre stage. Due to the airborne nature of birds and the dense nature of the tropical forest, bird identifications through audio may be a better solution than visual identification. The goal of this study is to find the most appropriate cepstral features that can be used to classify bird sounds more accurately. Fifteen (15) endemic Bornean bird sounds have been selected and segmented using an automated energy-based algorithm. Three (3) types of cepstral features are extracted; linear prediction cepstrum coefficients (LPCC), mel frequency cepstral coefficients (MFCC), gammatone frequency cepstral coefficients (GTCC), and used separately for classification purposes using support vector machine (SVM). Through comparison between their prediction results, it has been demonstrated that model utilising GTCC features, with 93.3% accuracy, outperforms models utilising MFCC and LPCC features. This demonstrates the robustness of GTCC for bird sounds classification. The result is significant for the advancement of bird sound classification research, which has been shown to have many applications such as in eco-tourism and wildlife management

Design and Simulation of Photonic Crystal Fiber for Liquid Sensing

This research article was published by MDPI 2021A simple hexagonal lattice photonic crystal fiber model with liquid-infiltrated core for different liquids: water, ethanol and benzene, has been proposed. In the proposed structure, three air hole rings are present in the cladding and three equal sized air holes are present in the core. Numerical investigation of the proposed fiber has been performed using full vector finite element method with anisotropic perfectly match layers, to show that the proposed simple structure exhibits high relative sensitivity, high power fraction, relatively high birefringence, low chromatic dispersion, low confinement loss, small effective area, and high nonlinear coefficient. All these properties have been numerically investigated at a wider wavelength regime 0.6–1.8 μm within mostly the IR region. Relative sensitivities of water, ethanol and benzene are obtained at 62.60%, 65.34% and 74.50%, respectively, and the nonlinear coefficients are 69.4 W−1 km−1 for water, 73.8 W−1 km−1 for ethanol and 95.4 W−1 km−1 for benzene, at 1.3 μm operating wavelength. The simple structure can be easily fabricated for practical use, and assessment of its multiple waveguide properties has justified its usage in real liquid detection

Modeling the Impact of Different Policies on Electric Vehicle Adoption: An Investigative Study

Electric Vehicles (EVs) emerge as a crucial solution for alleviating the environmental footprint of the transportation sector. However, fostering their widespread adoption demands effective, targeted policies. This study introduces a versatile model, amalgamating stakeholders and policies and leveraging local data with broader market applicability. It delineates two key EV adopter groups—innovators and imitators—shedding light on their evolving impact on adoption trends. A pivotal feature of the model is the factoring of EV attractiveness, comprising Life-Cycle Cost (LCC), Driving Range, Charging Time, and infrastructure availability, all of which are expected to improve with the fast technological advancement of EVs. Financial policies, notably subsidies, prove potent in boosting EV adoption but fall short of targeted sales due to imitator lag. In response, a pragmatic solution is proposed: a government-led EV acquisition of 840 EVs, coupled with a 20% subsidy on new EV purchases and a 20% tax on new ICEV purchases, potentially realizing a 30% EV sales target by 2035. Future research avenues may delve into behavioral dynamics prompting imitators’ adoption, optimizing EV infrastructure strategies, and assessing the socio-economic impacts of EVs. Interdisciplinary approaches hold promise for enriched insights for effective EV integration policies

An Image Synthesis Method Generating Underwater Images

The objective of this study is to convert normal aerial images into underwater images based on attenuation values for different water types by utilizing the image formation model (IFM) with Jerlov water types. Firstly, the depth values are derived from RGB-D images. If the depth information is not available, the values between 0.5 m to 10 m are chosen, and the transmission map is estimated by these values. Secondly, the statistical average background light values of Br = 0.6240, Bg = 0.805, and Bb = 0.7651 have been derived by analyzing 890 images using two methods, namely quad-tree decomposition and four-block division. Finally, the conversion of aerial-to-underwater images is done using the derived values, and the images are verified by computer simulation using MATLAB software. The result indicates that this method can easily generate underwater images from aerial images and makes it easier for the availability of ground truth

Estimating Social Background Profiling of Indian Speakers by Acoustic Speech Features

851-860Social background profiling of speakers refers to estimating the geographical origin of speakers by their speech features. Methods for accent profiling that use linguistic features, require phoneme alignment and transcription of the speech samples. This paper proposes a purely acoustic accent profiling model, composed of multiple convolutional networks with global average-pooling layers, to classify the temporal sequence of acoustic features. The bottleneck representations of the convolutional networks, trained with the original signals and their low-pass filtered copies, are fed to a Support Vector Machine classifier for final prediction. The model has been analysed for a speech dataset of Indian speakers from social backgrounds spread across India. It has been shown that up to 85% accuracy is achievable for classifying the geographic origin of speakers corresponding to regional Indian languages; 17% higher than the benchmark deep learning model using the same features. Results have also indicated that classification of accents is easier using the second language of the speakers, as compared to their native language

Role of Restored Underwater Images in Underwater Imaging Applications

Underwater images are extremely sensitive to distortion occurring in an aquatic underwater environment, with absorption, scattering, polarization, diffraction and low natural light penetration representing common problems caused by sea water. Because of these degradation of quality, effectiveness of the acquired images for underwater applications may be limited. An effective method of restoring underwater images has been demonstrated, by considering the wavelengths of red, blue, and green lights, attenuation and backscattering coefficients. The results from the underwater restoration method have been applied to various underwater applications; particularly, edge detection, Speeded Up Robust Feature detection, and image classification that uses machine learning. It has been shown that more edges and more SURF points can be detected as a result of using the method. Applying the method to restore underwater images in image classification tasks on underwater image datasets gives accuracy of up to 89% using a simple machine-learning algorithm. These results are significant as it demonstrates that the restoration method can be implemented on underwater system for various purposes