Traditional waste management system operates based on daily schedule which is highly inefficient and costly. The existing recycle bin has also proved its ineffectiveness in the public as people do not recycle their waste properly. With the development of Internet of Things (IoT) and Artificial Intelligence (AI), the traditional waste management system can be replaced with smart sensors embedded into the system to perform real time monitoring and allow for better waste management. The aim of this research is to develop a smart waste management system using LoRa communication protocol and TensorFlow based deep learning model. LoRa sends the sensor data and Tensorflow performs real time object detection and classification. The bin consists of several compartments to segregate the waste including metal, plastic, paper, and general waste compartment which are controlled by the servo motors. Object detection and waste classification is done in TensorFlow framework with pre-trained object detection model. This object detection model is trained with images of waste to generate a frozen inference graph used for object detection which is done through a camera connected to the Raspberry Pi 3 Model B+ as the main processing unit. Ultrasonic sensor is embedded into each waste compartment to monitor the filling level of the waste. GPS module is integrated to monitor the location and real time of the bin. LoRa communication protocol is used to transmit data about the location, real time and filling level of the bin. RFID module is embedded for the purpose of waste management personnel identification.

In this article, a novel shaped metamaterial sensor is presented for the recognition of various oils, fluids, and chemicals using microwave frequency. The performance of the designed sensor structure has been studied both theoretically and experimentally, and it works well. A new sample holder for convenient operation is created and located just behind the designed structure. The results of this study performed better than those of prior liquids sensing studies. Various designs were explored using the Genetic Algorithm (GA), and it is embedded in the Computer Simulation Technology (CST) microwave studio, to optimize the optimal dimensions of the resonator. The suggested metamaterial sensor has a good-quality factor and sensitivity in both frequency shifting and amplitude changing. The resonance frequency shifted to 100 MHz between olive and corn oils, 70 MHz between sunflower and palm oils, 80 MHz between clean and waste brake fluids, and 90 MHz between benzene and carbon-tetrachloride chemicals. The quality factor of the sensor is 135, sensitivity is 0.56, and the figure of merit is 76 which expresses its efficient performance. Furthermore, the proposed sensor can sensitively distinguish different liquids by using the frequency shifting property. The study was carried out in three stages: dielectric constant (DK) measurement with the N1500A dielectric measurement kit, simulation of the structure, and experimental test study with the vector network analyzer. Since the recommended sensor has high sensitivity, good quality factor, and excellent performance, hence it can be used in chemical, oil, and microfluidic industries for detecting various liquid samples.

In this research paper, the sensing capabilities of a tri circle split ring resonator shaped metamaterial is shown within the X-band frequency range for detection of various oil samples both theoretically and experimentally. Mathematical modelling was used to analyze the sensor efficiency for the various oil samples. A new sample holder has been made for the proposed sensor structure, and it showed significant performance. The simulated and measured transmission coefficient has been revealed in this study and monitoring the shift of resonance frequency and sensitivity of the metamaterial sensor for different oil samples. Since the dielectric constants of the oil samples differ, hence the resonance frequency is shifted. The obtained result revealed that the proposed sensor can detect a wide range of liquids, including (i) coconut and extra virgin coconut oils, (ii) olive and extra virgin olive oils, (iii) clean and waste engine oils, (iv) sunflower and canola oils. The resonance frequency has shifted to about 210, 230, 170, and 200 MHz for the above-mentioned oil samples, respectively. The sensitivity of the proposed MTM sensor is −83 dB (mg/L) which is a new analysis and quite significant as a potential sensor structure. Surface current and electric field distributions were used to deduce the sensing process. Since the recommended sensor is cheap and highly sensitive; so, it can be used in a variety of fields, such as detection of various liquids, microfluidic sensing, and industrial applications.

This paper represents a penta band square enclosed star-shaped modified split ring resonator (SRR) based single negative meta-atom absorber (MAA) for multi-band microwave regime applications. FR-4 low-cost material has been used as a substrate to make the MAA unit cell with 0.101λ₀ × 0.101λ₀ of electrical size, where λ₀ is the wavelength calculated at the lower resonance frequency of 3.80 GHz. There are two outer square split ring and one inner star ring shape resonator of 0.035 mm thickness of copper placed on the one side, and another side of the substrate has full copper to construct the desired unit cell. The MAA unit cell provides five absorption peaks of 97.87%, 93.65%, 92.66%, 99.95%, and 99.86% at the frequencies of 3.80, 5.65, 8.45, 10.82, and 15.92 GHz, respectively, which covers S-, C-, X-, and Ku- bands. The properties of MAA have been investigated and analyzed in the E-, H-fields and surface current. The EMR and highest Q factor of the designed MAA is 9.87 and 30.41, respectively, and it shows a single negative (SNG) property. Different types of parametric analysis have been done to show the better performance of absorption. Advanced Designed System (ADS) software has been used for equivalent circuit to verify the simulated S₁₁ result obtained from the CST-2019 software. Experimental outcomes of the MAA unit cell have a good deal with the simulated result and measured result of the 24 × 20 array of unit cells also shown. Since the unit cell provides superior EMR, excellent Q-factor, and highest absorption so the recommended MAA can be effectively used as a penta band absorber in microwave applications, like notch filtering, sensing, reducing the unintended noise generated with the copper component of the satellite and radar antennas.