The growing demand for electrical energy and the impact of global warming leads to a paradigm shift in the power sector. This has led to the increased usage of renewable energy sources. Due to the intermittent nature of the renewable sources of energy, devices capable of storing electrical energy are required to increase its reliability. The most common means of storing electrical energy is battery systems. Battery usage is increasing in the modern days, since all mobile systems such as electric vehicles, smart phones, laptops, etc., rely on the energy stored within the device to operate. The increased penetration rate of the battery system requires accurate modelling of charging profiles to optimise performance. This paper presents an extensive study of various battery models such as electrochemical models, mathematical models, circuit-oriented models and combined models for different types of batteries. It also discusses the advantages and drawbacks of these types of modelling. With AI emerging and accelerating all over the world, there is a scope for researchers to explore its application in multiple fields. Hence, this work discusses the application of several machine learning and meta heuristic algorithms for battery management systems. This work details the charging and discharging characteristics using the black box and grey box techniques for modelling the lithium-ion battery. The approaches, advantages and disadvantages of black box and grey box type battery modelling are analysed. In addition, analysis has been carried out for extracting parameters of a lithium-ion battery model using evolutionary algorithms.

Proper treatment and disposal of industrial pollutants of all kinds are a global issue that presents significant techno-economical challenges. The presence of pollutants such as heavy metal ions (HMIs) and organic dyes (ODs) in wastewater is considered a significant problem owing to their carcinogenic and toxic nature. Additionally, industrial gaseous pollutants (GPs) are considered to be harmful to human health and may cause various environmental issues such as global warming, acid rain, smog and air pollution, etc. Conductive polymer-based nanomaterials have gained significant interest in recent years, compared with ceramics and metal-based nanomaterials. The objective of this review is to provide detailed insights into different conductive polymers (CPs) and their nanocomposites that are used as adsorbents for environmental remediation applications. The dominant types of CPs that are being used as adsorbent materials include polyaniline (PANI), polypyrrole (Ppy), and polythiophene (PTh). The various adsorption mechanisms proposed for the removal of ODs, HMIs, and other GPs by the different CPs are presented, together with their maximum adsorption capacities, experimental conditions, adsorption, and kinetic models reported.

In this paper we present an intelligent chemical sniffer, capable of detecting hazardous volatile organic compounds (VOCs) using Artificial Intelligence (AI) deployed at the Edge. The proposed solution utilizes a multichannel metal-oxide-semiconductor (MOS) gas sensor for generating unique signature responses from the organic compounds. These signature responses are then used for training a quantized neural network model in the cloud and then deployed onto an embedded development platform for edge inference. In this approach we utilized TensorFlow Lite (tflite) for Microcontrollers, an edge computing framework (developed by Google^TM) to quantize a 32-bit floating point precision neural network model into an 8-bit integer precision model for deployment on low power and low memory footprint embedded edge devices. Our quantized model was able to successfully classify all the six VOCs (i.e., Xylene, Hexane, Acetone, Toluene, Methanol and Butanol) with accuracies of 99.8% and 100% on the validation and test datasets respectively. More than 50% reduction in on-device RAM and Flash memory usage was measured for the 8-bit quantized model when compared to the equivalently performing 32-bit floating point model for relatively same inference speed on the edge sensing device. The proposed solution paves the way for mass scale deployment of such intelligent chemical sensing solutions in next generation automated smart factories.

Diagnosing Oral Cavity Cancer (OCC) in its initial stages is an effective way to reduce patient mortality. However, current pre-screening solutions are manual and the resultant clinical treatment isn”t cost-effective for the average individual, primarily in developing nations. In this paper, we present an automated and inexpensive pre-screening solution utilizing Artificial Intelligence (AI) deployed on embedded edge devices to detect benign and pre-malignant superficial oral tongue lesions. The proposed machine vision solution utilizes a clinically annotated photographic dataset of 9 types of superficial oral tongue lesions to retrain a MobileNetV2 neural network using transfer learning. In this approach we also utilized TensorFlow Lite for Microcontrollers to quantize a 32-bit floating point (float32) precision model into an 8-bit integer (int8) model for deployment on power and resource constrained OpenMV Cam H7 Plus embedded edge device. The quantized int8 model was able to detect the 9 tongue lesions with an accuracy of 98.69% on the test set. More than 60% reduction in on-device RAM and Flash memory usage was measured for the int8 model when compared to the equivalently performing float32 model for relatively the same inference speed (~1.1 msec) on the target edge device.

The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as “asymptomatic” COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the “recall” metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892.

Signal attenuation due to dust and sand storms is one of the major problems in the utilization of microwave frequency bands for terrestrial and space communication especially in arid regions such as Northern Africa and Middle Eastern regions. In this paper, a machine learning (ML) model is developed to predict microwave signal attenuation due to atmospheric conditions recorded during dust and sand storms. The model utilizes recorded meteorological data, particularly optical visibility, temperature, relative humidity, atmospheric pressure, and wind speed conditions to predict the signal attenuation for a 22GHz operating frequency terrestrial link in Sudan. Compared to measured values, the predicted signal attenuation values show a relatively optimistic relation between meteorological data and microwave signal attenuation when utilizing all the meteorological features.