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Classification of waste for recycling has been a focal point for scientists interested in the field of conservation of the environment. Recycling consists of numerous steps, of which one of the most crucial is the segregation of recyclables from all other waste. Due to a lack of safety standards in developing countries, waste collection is often done manually by domestic helpers, or "rag-pickers". Such a process risks individual and public health. The waste collection methods may ultimately cause waste to become non-recyclable due to cross-contamination. Literature shows that research in this direction focuses on a single class of waste detection. The proposed work investigates CNN, YOLO, and faster RCNN-based multi-class classification methods to detect different types of waste at the collecting point. The smart dustbin proposed employs these computer vision methods with a Raspberry Pi microcontroller and camera module. The experimental results for multi-class classification show that the CNN has 80% of accuracy with 60% of the loss. Whereas the YOLO algorithm shows an accuracy of 88% and a loss of 40%. But the best results were obtained from faster RCNN object detection with API, with an accuracy of 91% and a loss of 16%. There is already an existing method for making a smart dustbin, so the results are compared to show how computer vision can be used to make a smart dustbin. This shows how computer vision can be used to make a smart dustbin. Doi: 10.28991/ESJ-2022-06-03-015 Full Text: PDF

Channel or spectral estimation is a ground-breaking feature in wireless communication systems as it helps in obtaining information about a wireless channel at any state of time. Employing this can help in reducing the intensity of noise and bit error rate (BER). Here, images are processed, channels are estimated and image restoration is being done. Orthogonal Frequency Division Multiplexing (OFDM) is looked upon as a very popular multiplexing cum modulation technique used in wireless communication systems and so, it is selected to play a cardinal role in channel estimation. The LMS algorithm is preferred in this technique since it is a simpler technique and provides results which are desirable. To simulate realistic conditions, OFDM signal is passed through Additive White Gaussian Noise (AWGN) and also a multipath fading channel. In this paper, an FFT based OFDM, adopting several digital modulation techniques like BPSK, QPSK, 8PSK and 16QAM is being implemented on images. The values of BER is observed for different values of SNR and a comparison is drawn out for the aforementioned modulation techniques. Results prove that BPSK provides the least BER for a particular value of SNR and hence, it can be observed to give the best performance.

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Agriculture provides a solution to the vast majority of problems that threaten human existence. When it comes to agriculture, new or contemporary technology can have a significant impact on a number of factors, including how much food is produced and how long it stays edible. The application of best management practices, for instance, is very common these days in the quest to improve agriculture. New hybrids are resistant to illnesses, use fewer pesticides, have natural defenses against pests, and may be grown in methods that minimize the number of diseases and pests that can affect them. Plants are capable of producing oxygen and medicines in addition to the food that they provide. Consequently, agriculture depends on plants that are in good health. A plant needs water, sunlight, and crucial fertilizer in order to receive the nutrients it needs to have a healthy plant. So, it is necessary to keep an eye on the health of the plant. The article discusses various technological solutions that can be implemented to automate the plant monitoring system. The Internet of Things and cloud computing are two technologies that are contributing to the development of intelligent technology by supplanting traditional agricultural practices. This clever device checks on the well-being of the plants. In order to enable intelligent agriculture, the technology relies on sensors that are dependent on IoT sensors. These sensors monitor the temperature, soil moisture, intensity of the sun's light, air quality value of the soil, vibration, and humidity in the immediate environment of the plant. The networking of these sensors ensures that the plant will continue to be healthy and will function in the appropriate manner. The findings that have been obtained up to this point are encouraging for the continuance of this strategy, which results in the highest possible profit for farmers. Doi: 10.28991/ESJ-2022-06-05-07 Full Text: PDF

A novel design of a 30 GHz microstrip line-fed antenna for 5G communication has been presented in this paper. 5G is the latest industry standard in mobile communication, which is designed to deliver higher data speeds, lower latency, greater network capacity, and higher reliability. It uses major parts of the mmWave spectrum (28 GHz to 40 GHz), allowing for a wide range of applications like mobiles, vehicles, medical devices, and other IoT networks. This mmWave network requires efficient antennas for its effective communication. Patch antennas use the function of oscillating their physical structure to the wavelength of the transmitting wave. Thus, higher efficiency can be achieved in the mmWave spectrum due to its proximity to the actual dimensions of the patch antenna, which also allows us to design antennas at small sizes and high reliability. The design in this report has a patch antenna with a centre frequency of 30 GHz. The antenna was optimized for this frequency based on the best reflection coefficient and gain while keeping the restraints of staying within the FR-2 band of 28 GHz to 33 GHz. The proposed antenna has been implemented using Rogers RT5880 substrate for high gain and performance across a wide range of frequencies. The feed is also accompanied by a quarter-wave feed cut for performance increase and impedance matching. The design has a gain of 8.45, with a reflection coefficient of -8 dB at a resonant frequency of 30 GHz. It shows great directivity of 5o and VSWR of 2.3 over a bandwidth of 3.5 GHz. It also employs a 0.4 mm C slot, which induces a dipole effect, thereby increasing the directivity and gain of the antenna. Hence, it is recommended for use in applications related to 5G mobile communication. Doi: 10.28991ESJ-2022-06-06-06 Full Text: PDF

Nanofibers membranes were synthesized using electrospinning method for air filtration application. Polyacrylonitrile (PAN) with three different concentrations as the polymeric matrix of the nanofibers membrane is used. In the aerosol filtration, the pressure drop is one of the most important parameters, which is determined by the membrane characteristics. One of the parameters that influence the characteristics of membrane is concentration of polymer solution, in which it will determine the diameter of fiber. In this study, the relation between the PAN concentration and the pressure drop in air filtration test was examined. Three different concentrations of PAN solution (6, 9, and 12 wt.%) were employed under the same process parameters of electrospinning. The fiber diameter distribution of each membrane was measured from its scanning electron microscope (SEM) image. The three concentrations resulted in significant different effect to the pressure drop that proved the existing correlation between the polymer concentration and the air pressure drop.