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.

Urban areas are facing challenges in waste management systems due to the rapid growth of population in cities, causing huge amount of waste generation. As traditional waste management system is highly inefficient and costly, the waste of resources can be utilized efficiently with the integration of the internet of things (IoT) and deep learning model. The main purpose of this research is to develop a smart waste management system using the deep learning model that improves the waste segregation process and enables monitoring of bin status in an IoT environment. The SSD MobileNetV2 Quantized is used and trained with the dataset that consists of paper, cardboard, glass, metal, and plastic for waste classification and categorization. By integrating the trained model on TensorFlow Lite and Raspberry Pi 4, the camera module detects the waste and the servo motor, connected to a plastic board, categorizes the waste into the respective waste compartment. The ultrasonic sensor monitors the waste fill percentage, and a GPS module obtains the real-time latitude and longitude. The LoRa module on the smart bin sends the status of the bin to the LoRa receiver at 915 MHz. The electronic components of the smart bin are protected with RFID based locker, where only the registered RFID tag can be used to unlock for maintenance or upgrading purposes.

Augmented Reality (AR) has proposed several types of interaction techniques such as 3D interactions, natural interactions, tangible interactions, spatial awareness interactions and multimodal interactions. Usually, interaction technique in AR involve unimodal interaction technique that only allows user to interact with AR content by using one modality such as gesture, speech, click, etc. Meanwhile, the combination of more than one modality is called multimodal. Multimodal can contribute to human and computer interaction more efficient and will enhance better user experience. This is because, there are a lot of issues have been found when user use unimodal interaction technique in AR environment such as fat fingers. Recent research has shown that multimodal interface (MMI) has been explored in AR environment and has been applied in various domain. This paper presents an empirical study of some of the key aspects and issues in multimodal interaction augmented reality, touching on the interaction technique and system framework. We reviewed the question of what are the interaction techniques that have been used to perform a multimodal interaction in AR environment and what are the integrated components applied in multimodal interaction AR frameworks. These two questions were used to be analysed in order to find the trends in multimodal field as a main contribution of this paper. We found that gesture, speech and touch are frequently used to manipulate virtual object. Most of the integrated component in MMI AR framework discussed only on the concept of the framework components or the information centred design between the components. Finally, we conclude this paper by providing ideas for future work involving this field.

Computerized brain tumor classification from the reconstructed microwave brain (RMB) images is important for the examination and observation of the development of brain disease. In this paper, an eight-layered lightweight classifier model called microwave brain image network (MBINet) using a self-organized operational neural network (Self-ONN) is proposed to classify the reconstructed microwave brain (RMB) images into six classes. Initially, an experimental antenna sensor-based microwave brain imaging (SMBI) system was implemented, and RMB images were collected to create an image dataset. It consists of a total of 1320 images: 300 images for the non-tumor, 215 images for each single malignant and benign tumor, 200 images for each double benign tumor and double malignant tumor, and 190 images for the single benign and single malignant tumor classes. Then, image resizing and normalization techniques were used for image preprocessing. Thereafter, augmentation techniques were applied to the dataset to make 13,200 training images per fold for 5-fold cross-validation. The MBINet model was trained and achieved accuracy, precision, recall, F1-score, and specificity of 96.97%, 96.93%, 96.85%, 96.83%, and 97.95%, respectively, for six-class classification using original RMB images. The MBINet model was compared with four Self-ONNs, two vanilla CNNs, ResNet50, ResNet101, and DenseNet201 pre-trained models, and showed better classification outcomes (almost 98%). Therefore, the MBINet model can be used for reliably classifying the tumor(s) using RMB images in the SMBI system.

The design of a compact metamaterial ultra-wideband (UWB) antenna with a goal towards application in microwave imaging systems for detecting unwanted cells in human tissue, such as in cases of breast cancer, heart failure and brain stroke detection is proposed. This proposed UWB antenna is made of four metamaterial unit cells, where each cell is an integration of a modified split ring resonator (SRR), capacitive loaded strip (CLS) and wire, to attain a design layout that simultaneously exhibits both a negative magnetic permeability and a negative electrical permittivity. This design results in an astonishing negative refractive index that enables amplification of the radiated power of this reported antenna, and therefore, high antenna performance. A low-cost FR4 substrate material is used to design and print this reported antenna, and has the following characteristics: thickness of 1.6 mm, relative permeability of one, relative permittivity of 4.60 and loss tangent of 0.02. The overall antenna size is 19.36 mm × 27.72 mm × 1.6 mm where the electrical dimension is 0.20 λ × 0.28 λ × 0.016 λ at the 3.05 GHz lower frequency band. Voltage Standing Wave Ratio (VSWR) measurements have illustrated that this antenna exhibits an impedance bandwidth from 3.05 GHz to more than 15 GHz for VSWR < 2 with an average gain of 4.38 dBi throughout the operating frequency band. The simulations (both HFSS and computer simulation technology (CST)) and the measurements are in high agreement. A high correlation factor and the capability of detecting tumour simulants confirm that this reported UWB antenna can be used as an imaging sensor.