Electrical and Computer Engineering

This paper proposes a fuzzy logic diet journaling system based on the premise that food intake is a control system. Past work in fuzzy diet analysis is covered. This continues into a discussion over strengths and weaknesses of the past work and a synthesis of requirements for a fuzzy diet analysis system with food recommendations. The proposed system is described and implemented. Two methods for nutritional feedback, one fuzzy and the other crisp, are compared. A comparison is made between the usefulness of fuzzy and crisp diet data from a user’s perspective.

Abstract—For 100 years, there has been no change in the basic
structure of the electrical power grid. Experiences have shown
that the hierarchical, centrally controlled grid of the 20th Century
is ill-suited to the needs of the 21st Century. To address the
challenges of the existing power grid, the new concept of smart
grid has emerged. The smart grid can be considered as a modern
electric power grid infrastructure for enhanced efficiency and
reliability through automated control, high-power converters,
modern communications infrastructure, sensing and metering
technologies, and modern energy management techniques based
on the optimization of demand, energy and network availability,
and so on. While current power systems are based on a solid
information and communication infrastructure, the new smart
grid needs a different and much more complex one, as its dimension is much larger. This paper addresses critical issues on
smart grid technologies primarily in terms of information and
communication technology (ICT) issues and opportunities. The
main objective of this paper is to provide a contemporary look
at the current state of the art in smart grid communications as
well as to discuss the still-open research issues in this field. It is
expected that this paper will provide a better understanding of the
technologies, potential advantages and research challenges of the
smart grid and provoke interest among the research community
to further explore this promising research area.

Increasing global demand for rechargeable lithium (Li) ion batteries has been driving the research on innovative processing techniques and material systems for cheaper production of battery electrodes. While cost reduction is important, obtaining desired microstructures in the active electrode material is also very critical for efficient performance of the batteries. Conventional processing of bulk scale Liion battery electrodes involves time consuming and multi-step processes starting from the production of active powder materials, blending with conductive additives and binders to develop the electrode material coating on a current collector. On the other hand, thin film battery technologies employ expensive vapor based or sputtering or laser ablation techniques to develop the electrodes. In this study, an innovative, rapid and a two step scalable manufacturing process has been developed. While capable of developing porous and ultrafine/nano structured oxide based LiCoO 2 cathode material directly on a charge collector from metallic Cobalt (Co) coatings, the process does not require polymeric binders. Following this approach, LiCoO 2 cathodes were synthesized directly on a stainless steel charge collector from plasma sprayed Co coatings via thermal treatments using aqueous LiNO 3 solution. X-ray diffraction (XRD) studies confirmed presence of LiCoO 2 hexagonal phase. Microstructural and phase analysis showed porous active material with ultrafine/nano structural features along with imperfections (e.g., dislocations). Electrochemical characterization illustrated an average voltage around 3.9 V with a specific discharge capacity around 70-85% of the nominal capacity (∼138 mAh/g) against Li counter electrode. However, process optimization in terms of plasma spray coatings and thermal treatments, and addition of carbon may enhance the performance of LiCoO 2 electrodes. Absence of polymeric binders makes these electrodes suitable for high temperature battery applications.

Indium oxide (In 2 O 3 ) has been widely used in sensors, solar cells and microelectronics. There are several techniques available for making In 2 O 3 such as vapor, electrochemical and atomic layer deposition, which are not only expensive but also time consuming processes. In this study, an inexpensive and straightforward synthesis approach is being presented to make micron/submicron size single crystals as well as nanostructured adherent coatings of In 2 O 3 using Indium Chloride (InCl 3 ) powders and InCl 3 solution precursor. Both the powders and the solution precursor were calcined in a furnace to obtain the crystals; however, the liquid precursor was also treated by a DC plasma jet to obtain the nanostructured coatings. The phase transformations during thermal decomposition of InCl 3 powders and solution precursor were investigated via differential scanning calorimetry studies. The phase structure and crystallinity of the crystals and coatings were confirmed by X-ray diffraction. Microstructural characterization of the crystals and coatings was done by scanning electron microscopy, transmission electron microscopy and atomic force microscopy techniques. Size of the crystals was observed to be dependent on the heating schemes adapted during calcination. Solution precursor plasma sprayed In 2 O 3 coatings showed porosity and ultrafine particulates with grain size ranging between 10 and 75 nm. Resistivity was determined to be ∼0.553 ± 0.337 k cm. Optical transmittance of In 2 O 3 coatings was ∼60-78% in the visible region and it was observed to decrease with increasing the number passes or the thickness of the coatings. Based on the optical transmission data, direct band gap of 3.57 eV was determined.

Indium oxide (In 2 O 3 ) has been widely used in sensors, solar cells and microelectronics. There are several techniques available for making In 2 O 3 such as vapor, electrochemical and atomic layer deposition, which are not only expensive but also time consuming processes. In this study, an inexpensive and straightforward synthesis approach is being presented to make micron/submicron size single crystals as well as nanostructured adherent coatings of In 2 O 3 using Indium Chloride (InCl 3 ) powders and InCl 3 solution precursor. Both the powders and the solution precursor were calcined in a furnace to obtain the crystals; however, the liquid precursor was also treated by a DC plasma jet to obtain the nanostructured coatings. The phase transformations during thermal decomposition of InCl 3 powders and solution precursor were investigated via differential scanning calorimetry studies. The phase structure and crystallinity of the crystals and coatings were confirmed by X-ray diffraction. Microstructural characterization of the crystals and coatings was done by scanning electron microscopy, transmission electron microscopy and atomic force microscopy techniques. Size of the crystals was observed to be dependent on the heating schemes adapted during calcination. Solution precursor plasma sprayed In 2 O 3 coatings showed porosity and ultrafine particulates with grain size ranging between 10 and 75 nm. Resistivity was determined to be ∼0.553 ± 0.337 k cm. Optical transmittance of In 2 O 3 coatings was ∼60-78% in the visible region and it was observed to decrease with increasing the number passes or the thickness of the coatings. Based on the optical transmission data, direct band gap of 3.57 eV was determined.

Indium oxide (In 2O 3) has been widely used in sensors, solar cells and microelectronics. There are several techniques available for making In 2O 3 such as vapor, electrochemical and atomic layer deposition, which are not only expensive but also time consuming processes. In this study, an inexpensive and straightforward synthesis approach is being presented to make micron/submicron size single crystals as well as nanostructured adherent coatings of In 2O 3 using Indium Chloride (InCl 3) powders and InCl 3 solution precursor. Both the powders and the solution precursor were calcined in a furnace to obtain the crystals; however, the liquid precursor was also treated by a DC plasma jet to obtain the nanostructured coatings. The phase transformations during thermal decomposition of InCl 3 powders and solution precursor were investigated via differential scanning calorimetry studies. The phase structure and crystallinity of the crystals and coatings were confirmed by X-ray diffraction. Microstructural characterization of the crystals and coatings was done by scanning electron microscopy, transmission electron microscopy and atomic force microscopy techniques. Size of the crystals was observed to be dependent on the heating schemes adapted during calcination. Solution precursor plasma sprayed In 2O 3 coatings showed porosity and ultrafine particulates with grain size ranging between 10 and 75 nm. Resistivity was determined to be ˜0.553 ± 0.337 kΩ cm. Optical transmittance of In 2O 3 coatings was ˜60-78% in the visible region and it was observed to decrease with increasing the number passes or the thickness of the coatings. Based on the optical transmission data, direct band gap of 3.57 eV was determined.

Over recent years, we have seen a significant rise in electric mobility to overcome the anthropogenic emissions by conventional gasoline vehicles. However, the prerequisite of smoothening of peaks and imbalances through bidirectional charging is concatenated with the undesired impacts on reliability and security of power system operation when there are probable intrusions in the eXtreme Fast Charging (XFC) station, hence destabilizing the charging networks. So this paper applies STRIDE based threat modeling to analyze and identify multiple potential threats endured by the cyber-physical system (CPS) of XFC station by using weighted attack defense tree. Potential mitigation strategies are then suggested for the identified severe threats. In addition, this paper also develops a stochastic probabilistic tool, the Hidden Markov Model (HMM) for modeling the security attacks for a given range of identified attack vectors and hence employing an appropriate defense strategy against the malicious hacker. Also, a weighted attack defense tree has been developed to generate various attack scenarios. In the end, the results of the proposed work are substantiated and validated if it is able to considerably improve overall charging efficiency and cyber-physical security of the charging station network.

Due to digital transformation, an autonomous vehicle (AV) is realized as a network of multiple electronic control units (ECUs) for providing ubiquitous connectivity and controlling various electronic functions ranging from essential safety (power steering, airbags) to comfort (driver or passenger seats), to security and access (keyless entry). Out of different communication busses, controller area network (CAN) is a cardinal bus protocol used as a real-time communication interface between these different electronic devices or ECUs embedded in a vehicle. However, an insufficient security design of CAN bus has rendered the network to be vulnerable to innumerable cyber-attacks and risks, hence jeopardizing its cybersecurity. To address the security issues, it is predominant to realize the malicious ECUs in an invehicle CAN bus network. Therefore, this paper proposes a novel ECU fingerprinting technique, where unique digital signatures extracted as a result of intrinsic characteristics of the ECUs are used to detect the ECU liable for broadcasting counterfeit messages received on the CAN bus. Further, the proposed work analyzes the data from seven distinct ECUs by employing three machine learning (ML) algorithms, i.e., k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Logistic Regression (LR). Further, the performance of the proposed cybersecurity framework is evaluated and compared using the above-mentioned algorithms.

Smart mobility is an imperative facet of smart cities, and the transition of conventional automotive systems to connected and automated vehicles (CAVs) is envisioned as one of the emerging technologies on urban roads. The existing AV mobility environment is perhaps centered around road users and infrastructure, but it does not support future CAV implementation due to its proximity with distinct modules nested in the cyber layer. Therefore, this paper conceptualizes a more sustainable CAV-enabled mobility framework that accommodates all cyber-based entities. Further, the key to a thriving autonomous system relies on accurate decision making in real-time, but cyberattacks on these entities can disrupt decisionmaking capabilities, leading to complicated CAV accidents. Due to the incompetence of the existing accident investigation frameworks to comprehend and handle these accidents, this paper proposes a 5Ws and 1Hbased investigation approach to deal with cyberattack-related accidents. Further, this paper develops STRIDE threat modeling to analyze potential threats endured by the cyber-physical system (CPS) of a CAV ecosystem. Also, a stochastic anomaly detection system is proposed to identify the anomalies, abnormal activities, and unusual operations of the automated driving system (ADS) functions during a crash analysis. INDEX TERMS CAV-enabled transport mobility environment, cybersecurity, STRIDE threat modeling, accident investigation.

Safe and reliable electric vehicle charging stations (EVCSs) have become imperative in an intelligent transportation infrastructure. Over the years, there has been a rapid increase in the deployment of EVCSs to address the upsurging charging demands. However, advances in information and communication technologies (ICT) have rendered this cyberphysical system (CPS) vulnerable to suffering cyber threats, thereby destabilizing the charging ecosystem and even the entire electric grid infrastructure. This paper develops an advanced cybersecurity framework, where STRIDE threat modeling is used to identify potential vulnerabilities in an EVCS. Further, the weighted attack defense tree approach is employed to create multiple attack scenarios, followed by developing Hidden Markov Model (HMM) and Partially Observable Monte-Carlo Planning (POMCP) algorithms for modeling the security attacks. Also, potential mitigation strategies are suggested for the identified threats.

A novel 2×2 MIMO antenna using crown square curve and koch curve is proposed and designed on RT/duroid having thickness of 3.175mm, loss tangent of 0.0009 and dielectric constant of 2.2, by employing MoM based IE3D software. In this geometry, koch curve is etched at the boundary of the iterated crown square patch antenna to produce a fractal boundary and fractal elements are placed parallel to each other at an inter-element distance of 25mm for good isolation. The resulting MIMO system operating at four resonating frequencies, gives much higher gains and bandwidths for all the operating frequencies. Furthermore, it exhibits impedance bandwidth (S11˂ ̵ 10dB) with isolation better than -15dB, which are accepted threshold values for the MIMO systems. Hence, a good MIMO antenna is designed giving excellent output characteristics.

The increasing demand of mobile Internet and the IoTs poses exacting requisites for 5G wireless communications, like high spectral efficiency. Hence, a promising technology, non-orthogonal multiple access (NOMA), is discussed. Unlike standard orthogonal multiple access technologies, NOMA can serve much more users via non-orthogonal resource allocation. The most striking attribute of NOMA is to serve multiple users at the same time/frequency/code, but with different power levels, which produces a remarkable spectral efficiency gain. There are mainly two types of NOMA techniques, power-domain and code-domain. This paper primarily focuses on power-domain NOMA that utilizes superposition coding (SC) at the transmitter and successive interference cancellation (SIC) at the receiver. Also, this paper discusses how NOMA performs when it is combined with other wireless communication techniques, for example cooperative communications, multiple-input multiple-output (MIMO). Furthermore, this p...

Intrusion detection for Controller Area Network (CAN) protocol requires modern methods in order to compete with other electrical architectures. Fingerprint Intrusion Detection Systems (IDS) provide a promising new approach to solve this problem. By characterizing network traffic from known ECUs, hazardous messages can be discriminated. In this article, a modified version of Fingerprint IDS is employed utilizing both step response and spectral characterization of network traffic via neural network training. With the addition of feature set reduction and hyperparameter tuning, this method accomplishes a 99.4% detection rate of trusted ECU traffic. Index Terms-IDS, electronic control unit, controller area network, machine learning (ML), artificial neural network (ANN), automotive electronics (AE).

Safe and secure electric vehicle charging stations (EVCSs) are important in smart transportation infrastructure. The prevalence of EVCSs has rapidly increased over time in response to the rising demand for EV charging. However, developments in information and communication technologies (ICT) have made the cyber-physical system (CPS) of EVCSs susceptible to cyber-attacks, which might destabilize the infrastructure of the electric grid as well as the environment for charging. This study suggests a 5Ws & 1H-based investigation approach to deal with cyber-attack-related incidents due to the incapacity of the current investigation frameworks to comprehend and handle these mishaps. Also, a stochastic anomaly detection system (ADS) is proposed to identify the anomalies, abnormal activities, and unusual operations of the station entities as a post cyber event analysis.

Intrusion detection for Controller Area Network (CAN) protocol requires modern methods in order to compete with other electrical architectures. Fingerprint Intrusion Detection Systems (IDS) provide a promising new approach to solve this problem. By characterizing network traffic from known ECUs, hazardous messages can be discriminated. In this article, a modified version of Fingerprint IDS is employed utilizing both step response and spectral characterization of network traffic via neural network training. With the addition of feature set reduction and hyperparameter tuning, this method accomplishes a 99.4% detection rate of trusted ECU traffic. Index Terms-IDS, electronic control unit, controller area network, machine learning (ML), artificial neural network (ANN), automotive electronics (AE).

Autonomous driving (AD) has developed tremendously in parallel with the ongoing development and improvement of deep learning (DL) technology. However, the uptake of artificial intelligence (AI) in AD as the core enabling technology raises serious cybersecurity issues. An enhanced attack surface has been spurred on by the rising digitization of vehicles and the integration of AI features. The performance of the autonomous vehicle (AV)-based applications is constrained by the DL models' susceptibility to adversarial attacks despite their great potential. Hence, AI-enabled AVs face numerous security threats, which prevent the large-scale adoption of AVs. Therefore, it becomes crucial to evolve existing cybersecurity practices to deal with risks associated with the increased uptake of AI. Furthermore, putting defense models into practice against adversarial attacks has grown in importance as a field of study amongst researchers. Therefore, this study seeks to provide an overview of the most recent adversarial defensive and attack models developed in the domain of AD.