A Review on E-waste: Fostering the Need for Green Electronics

Misra, Nisha Rani; Kumar, Sandeep; Jain, Arpit

doi:10.1109/icccis51004.2021.9397191

Cited by 53 publications

(13 citation statements)

References 30 publications

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“…The performance of the proposed method was 1% less than the existing age and gender classification methods, and they concluded that mouth and eye regions have more significant features for age classification and gender classification and, therefore, the eye region plays an important role [ 33 ]. Salah Eddine-Bekhouche et al proposed an age and gender classification method using ML-LPQ (Multi-Level Local Phase Quantization) features, and SVM is used to predict age and gender and results are better than existing methods [ 34 ]. Ehsan Fazl-Ersi et al proposed a method for age and gender classification which integrated the various features related to age and gender and it provided more visual information.…”

Section: Literature Workmentioning

confidence: 99%

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Kumar

Rani²,

Jain

et al. 2022

Sensors

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Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.

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Section: Literature Workmentioning

confidence: 99%

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Kumar

Rani²,

Jain

et al. 2022

Sensors

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“…4,5 But the overall disposal ratio of EW remains at a low level of 17.4%. 6,7 The annual EW amount in China has been estimated as 10.1 million metric tons in 2019, which is predicted to be 27.2 million metric tons in 2030 and 51.6 million metric tons in 2050, ranking first in the world. 2,8 EW in China generally includes entire electrical products such as air conditioners, refrigerators, and printers, as well as dismantled and broken parts like batteries, cathode ray tubes, and polychlorinated biphenyl capacitors.…”

Section: Introductionmentioning

confidence: 99%

“…According to a report issued by the United Nations, global EW generation exceeded 53.6 million metric tons in 2019, with an average increase rate of 3.89% (since 2014) . Statistical data also indicates that the global EW output will reach 74.0 million metric tons by 2030, posing severe threats to human health and the ecological environment due to the presence of toxic additives and heavy metals. , International agreements, such as “the Basel Convention,” have established strict control procedures to ensure that EW is appropriately handled in an environmentally sustainable manner. , But the overall disposal ratio of EW remains at a low level of 17.4%. , …”

Section: Introductionmentioning

confidence: 99%

Inventory of PCDD/Fs and Fly Ash Characteristics during the e-Waste and Municipal Solid Waste Co-Incineration Process

et al. 2023

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In the face of a challenging situation for yearly e-waste (EW) increment and disposition, EW co-incineration (EWC) with municipal solid waste (MSW) is a promising solution whose emission characteristics remain unclear. This study is conducted in a 400 tons/day MSW incineration (MSWI) plant to characterize polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and fly ash (FA) properties. Analytical methods include high-resolution gas chromatography/high-resolution mass spectrometry (HRGC/HRMS) for PCDD/ F determination, as well as X-ray diffraction (XRD), X-ray fluorescence (XRF), ion chromatography (IC), and leaching toxicity test for FA. The results indicate that toxic PCDD/F amounts are doubled by EWC in both primary exhaust gas (EG) and FA with international toxic equivalent quantity (I-TEQ) concentrations of 19.00 ± 6.32 ng I-TEQ/Nm 3 and 1.33 ± 0.56 ng I-TEQ/g. After the clean-up of the air pollution control system (APCS), emitted PCDD/Fs in solid and gas phases, as well as major air pollutants (CO, NO x , SO 2 , HCl, and particulate matter), are permissive within the limit values of national standards GB 18485-2014 and GB 16889-2008. Distribution patterns for 136 PCDD/F isomers are analogous in both MSWI and EWC conditions. De novo synthesis is always the major formation route for both EWC and MSWI conditions, while dibenzodioxin/dibenzofuran (DD/ DF) chlorination also contributes. The generation of 136 PCDD/Fs is enhanced by EWC, and the correlation coefficients of Cl and Cu in the fuel with the generation of 136 PCDD/F isomers are found to be R = 0.92 and 0.88, respectively. The main components in FA from MSWI and EWC differ less, both containing CaO, SiO 2 , and Al 2 O 3 . However, Cl and Cu contents, as well as leaching toxicity in FA are elevated by EW. Heavy metals, except for Cd, Ni, and Pb, meet the requirement of the standard.

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“…With the rising prevalence of electronics and technology in modern life and society, waste generation during the manufacturing, exploitation, and disposal of devices and materials is emerging as a serious challenge. [ 1 , 2 , 3 , 4 ]. Plastics are widely used as packaging materials due to their versatility, low cost, low density, and adaptability.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Carbon-Nanoparticle-Filled Poly(Butylene Succinate-co-Adipate) Nanocomposites for Electromagnetic Applications

Bleija

Platnieks²,

Macutkevič³

et al. 2022

Nanomaterials

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Electrostatic dissipative (ESD), anti-static (AS), and electromagnetic interference (EMI) shielding materials are commonly based on commodity fossil-fuel-based plastics. This, in turn, contributes to ever-growing non-biodegradable plastic pollution. Graphene nanoplatelets (GN), multi-walled carbon nanotubes (MWCNT), nanostructured carbon black (NCB), and amorphous carbon black (CB) were utilized as nanofillers to prepare bio-based and biodegradable poly(butylene succinate-co-adipate) (PBSA) nanocomposites. Solvent-cast composites were prepared with 1.1 to 30.0 vol.% nanoparticle loading. The literature mainly focuses on relatively low loadings; therefore, for this research, filler loadings were increased up to 30 vol.% but the maximum loading for NCB and CB loadings only reached 17.4 vol.% due to a lack of dimensional stability at higher loadings. The composites were characterized using tensile testing, volumetric and surface conductivity measurements, thermal conductivity measurements, dielectric spectroscopy in the microwave region, and transmittance in the terahertz range. Tensile tests showed excellent carbon filler compatibility and enhanced tensile strength for loadings up to 5 vol.% (up to 20 vol.% for MWCNT). The highest thermal conductivity values were reached for the MWCNT filler, with the 30.0 vol.% filled composite reaching 0.756 W/mK (262% increase over PBSA). All fillers were able to produce composites that yielded volume conductivities above 10−10 S/m. Composites with MWCNT, GN, and NCB inclusions above the percolation threshold are suitable for EMI applications in the microwave and THz frequency range.

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A Review on E-waste: Fostering the Need for Green Electronics

Cited by 53 publications

References 30 publications

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System

Inventory of PCDD/Fs and Fly Ash Characteristics during the e-Waste and Municipal Solid Waste Co-Incineration Process

Comparison of Carbon-Nanoparticle-Filled Poly(Butylene Succinate-co-Adipate) Nanocomposites for Electromagnetic Applications

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