Conventional breast cancer detection techniques including X-ray mammography, magnetic resonance imaging, and ultrasound scanning suffer from shortcomings such as excessive cost, harmful radiation, and inconveniences to the patients. These challenges motivated researchers to investigate alternative methods including the use of microwaves. This article focuses on reviewing the background of microwave techniques for breast tumour detection. In particular, this study reviews the recent advancements in active microwave imaging, namely microwave tomography and radar-based techniques. The main objective of this paper is to provide researchers and physicians with an overview of the principles, techniques, and fundamental challenges associated with microwave imaging for breast cancer detection. Furthermore, this study aims to shed light on the fact that until today, there are very few commercially available and cost-effective microwave-based systems for breast cancer imaging or detection. This conclusion is not intended to imply the inefficacy of microwaves for breast cancer detection, but rather to encourage a healthy debate on why a commercially available system has yet to be made available despite almost 30 years of intensive research.

Assessing a person's intelligence level is required in many situations, such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual's fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed. For this purpose, we employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven's Advanced Progressive Matrices (RAPM) test. Using visual oddball cognitive task, neural activity of each group was measured and analyzed over three midline electrodes (Fz, Cz, and Pz). To predict whether an individual belongs to LA or HA group, features were extracted using wavelet decomposition of EEG signals recorded in visual oddball task and support vector machine (SVM) was used as a classifier. Two different types of Haar wavelet transform based features have been extracted from the band (0.3 to 30 Hz) of EEG signals. Statistical wavelet features and wavelet coefficient features from the frequency bands 0.0–1.875 Hz (delta low) and 1.875–3.75 Hz (delta high), resulted in the 100 and 98% prediction accuracies, respectively, both for 2D and 3D contents. The analysis of these frequency bands showed clear difference between LA and HA groups. Further, discriminative values of the features have been validated using statistical significance tests and inter-class and intra-class variation analysis. Also, statistical test showed that there was no effect of 2D and 3D content on the assessment of fluid intelligence level. Comparisons with state-of-the-art techniques showed the superiority of the proposed system.

We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states. For data collection, half of sixty-eight healthy individuals watched the learning material in 2D format whereas the rest watched the same material in 3D format. After learning task, memory recall tasks were performed after 30 minutes (STM) and two months (LTM), and EEG signals were recorded. In case of STM, 97.5% prediction accuracy was achieved for 3D and 96.6% for 2D and, in case of LTM, it was 100% for both 2D and 3D. The statistical analysis of the results suggested that for learning and memory recall both 2D and 3D materials do not have much difference in case of STM and LTM.

Electroencephalography (EEG) has been widely adopted for investigating brain behavior in different cognitive tasks e.g. learning and memory. In this paper, we propose a pattern recognition system for discriminating the true and false memories in case of short-term memory (STM) for 3D and 2D educational contents by analyzing EEG signals. The EEG signals are converted to scalp-maps (topomaps) and city-block distance is applied to reduce the redundancy and select the most discriminative topomaps. Finally, statistical features are extracted from selected topomaps and passed to Support Vector Machine (SVM) to predict brain states corresponding to true and false memories. A sample of thirty four healthy subjects participated in the experiments, which consist of two tasks: learning and memory recall. In the learning task, half of the participants watched 2D educational contents and half of them watched the same contents in 3D mode. After 30 minutes of retention, they were asked to perform memory recall task, in which EEG signals were recorded. The classification accuracy of 97.5% was achieved for 3D as compared to 96.5% for 2D. The statistical analysis of the results suggest that there is no significant difference between 2D and 3D educational contents on STM in terms of true and false memory assessment.

In this paper, an electrically-small microwave dipole sensor is used with machine learning algorithms to build a noninvasive continuous glucose monitoring (CGM) system. As a proof of concept, the sensor is used on aqueous (water-glucose) solutions with different glucose concentrations to check the sensitivity of the sensor. Knowledge-driven and data-driven approaches are used to extract features from the sensor's signals reflected from the aqueous glucose solution. Machine learning is used to build the regression model in order to predict the actual glucose levels. Using more than 19 regression models, the results show a good accuracy with Root Mean Square Error of 1.6 and 1.7 by Matern 5/2 Gaussian Process Regression (GPR) algorithm using the reflection coefficient's magnitude and phase.