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As urbanization moves towards globalization in the next century, the evolution of smart city technologies has also brought new approaches to traditional public wellbeing problems such as food accessibility at both global and local scales. Technology implemented using the Internet of Things and data analytics offers unique advantages and challenges to address issues related to food access. In addition, interdisciplinary efforts are necessary to effectively utilize emerging technologies to address the issue of food insecurity while considering the underlying complex social, economic, and environmental dimensions. In this paper, we discuss the multi-dimensional nature of the food accessibility problem in U.S. metropolitan regions and explore the connection between the fields of engineering, social science, agriculture, education, and life sciences, with respect to their collective impact on addressing the food accessibility problem. We also present our team’s ongoing efforts to identify and address food insecurity problems in Richmond, Virginia through interdisciplinary research.

Biomedical IoT applications require the amplification of biomedical signals with low frequencies and small amplitudes, while suppressing Direct Current Voltage offsets. CMOS OTA-based amplifier design that meets this requirement is a research topic of interest to neuroscience scientists and clinicians. This article describes a literature review on different kinds of OTA used in neural signal capturing applications. A 120dB high gain, 4pV/√(Hz) noise and 0.5µW ECG amplifier for heart rate analyzing sensor node used in biomedical IOT application is designed using 90nm CMOS process. Wilson current-mirror method is used in designing 1V-powered CMOS ECG OTA.

This paper elucidates the control mechanism employed for the speed control of three phase induction motors used in a sand manufacturing unit. Moreover, this plant uses three phase induction motors for its various operations and has a production rate of around 125-160 tons of sand per hour. These motors use variable frequency drives (VFD) which are based on programmable logic controllers (PLC). The paper expounds the operation of the plant in-depth, the drawbacks of using aforementioned method of speed control and the need for choosing other speed control strategies so as to improve the speed control of induction motors and thus increases the output of the plant.

The Intelligent Transportation System is an important application to reduce accidents on the road using technology such as license plate recognition (LPR). However, LPR systems still suffer from problems with the position of license plates which can result in improper fines. To identify vehicles, more information should be taken into account, such as vehicle manufacturer recognition (VMR). We propose a transfer method model using a single shot detector (SSD) to detect and recognize a vehicle manufacturer. The model is trained in using a public dataset and applying it in a real environment dataset provided by Bangkok Expressway and Metro Public Company Limited (BEM). We use a public dataset including vehicle make and model recognition dataset (VMMRDB) for training a model with one-thousand images. The model achieved 88.34% and 93.36% successful recognition in the public dataset and BEM dataset, respectively.

This paper presents a miniaturized electromagnetic cloaking scheme in the microwave frequency regime using cylindrically arranged dogbone metallic inclusions. Multipole scattering theory has been utilized to extract the true reason behind cloaking mechanism. It is found that the strong excitation of magnetic resonance along with the non resonant excitation of electric dipole moments significantly reduce far-field scattering from the structure. The invisibility mechanism is experimentally proved using backscattering measurements and are validated using full-wave simulation studies.

Since the coronavirus disease 2019 (COVID-19) outbreak has spread across the country, our research applies to remind the people to wear a face mask when we go outside because a facial image detection and classification method will be used to authentication and authorization. This paper has shown that our created models based on CNN can detect the face mask-wearing, glasses-wearing, and gender with comparison two models. We training model with mix public datasets such as WIDER FACE, AFW, and MAFA. Moreover, we use VGG-Face to pre-train the model for the advance detection rate.

The diversity of the internet can be fascinating with our everyday devices getting automated. However, such technology of connecting everything to the internet can actually be daunting. This data could easily be attacked by malicious users and hence hangs the usage of such technologies in a precarious condition. To prevent such attacks, we have used AES algorithm to secure data bits and also designed a two-stage encryption and decryption algorithm. This algorithm was successfully tested by transmitting data bits through a channel between two wireless sensor nodes for five test cases. The model is tested in such a way that it can be universally applicable to all IoT networks.

Hand writing and hand drawing are natural ways to take note. A pen and papers are used to make a note for a long time. In digital era, the notes are often converted into a durable and formal format for further use. Therefore, the conversion application was developed in many fields with many skill such as handwritten recognition, object recognition, object classification, and others. In this paper, we demonstrate a method to classify connected components as flowchart and text. We use the Online Handwritten Flowchart Dataset (OHFD) which contained 419 handwritten flowcharts to benchmark our methodology. The result shown our classification technique get F1-score 77.6%.

A modular approach to the complex robotic systems is desireable due to advantages such as ease of development, reconfigurability, and re-usability. Powering these dynamic structures while maintaining ease of assembly and versatile form-factor is a challenge. One promising solution is to exchange energy between modules which each have a small battery. Performing this exchange of energy using mechanical connections is not only unreliable but also limits the reconfigurability of the modules. In this paper, we present a bidirectional capacitive wireless power transfer scheme for energy balancing between different robot modules. A modified CMCDE topology is also presented to make the power transfer resilient to the changes in the robot dimensions. The concept is verified by demonstrating 20 W of power transfer between two modules at 13.56 MHz achieving a dc-dc efficiency of 79 % that is resilient to the changes in the track length.

An extraordinary transmission analog for strongly enhancing radiation from an electrically small radiator in the microwave regime is proposed in this paper. The electrically small radiated used here is a chip inductor loaded open coplanar monopole type antenna. EOT is achieved by implementing evanescent amplification using mu negative metamaterial placed symmetrically on both sides of the antenna. Near-field and far-field probing reveal that the mu negative split ring resonator not only amplifies the evanescent high spatial spectrum existing in the near-field of the antenna but also responsible for evanescent to propagation conversion.

Brain Computer Interface (BCI) is an emerging field of research, which helps neuromuscular disabled people to communicate and control external devices with the brain. Electroencephalogram (EEG) signals have been used by doctors to diagnose brain activities. EEG signals are very sensitive to the physical movement of most of the body parts. Electrooculogram (EOG) are the signals which are directly related to eye movements. In the field of BCI, artifacts refer to those signals which are not required but will be present in data because of the involuntary actions of human muscles. In the proposed research, BCI Competition III Dataset IV A is used, which consists of two classes of signals right hand and right foot, so ocular artifacts were a prime hindrance for this type of data. The research involves the use of the following feature detection methods: ICA, ICA-RLS, ICA-LMS, ICA-DWT, EMD-ICA. The first common step is using ICA to obtain independent components(ICs). Using ICA, ICs that contain ocular artifacts can be identified and then further exposed to another round of ICA or adaptive filtering that uses LMS, RLS. The adaptive filtering technique reduces the need for parallel EOG recordings by removing the ocular ICs using reference signals. Hybrid' methods have gained much advantage over the traditional ICA process, which leads to better detection and removal of ocular artifacts with these techniques. Further, the research proposes a system involving the use of SVM-PSO for the classification of motor imagery signals obtained after the removal of artifacts. ICA-RLS and CSP as pre-processing and feature extraction techniques provided the best classification accuracy of 92.94%.

Surveillance based on computer vision is the need of the current era. Most of the existing surveillance cameras which are being used by the Security Services and Military Forces works on the sensor-based analysis of conditions, it activates a signal indicating the presence of some movable creature and cannot accurately differentiate between a hare, a deer or a human. The current surveillance methods show high accuracy in presence of daylight but fail miserably in low light conditions because these sensors equipped with normal cameras are not able to capture images with the same accuracy in such conditions. Hence to overcome this problem, we propose a network that is a combination of Human and Face Detection systems to distinguish between different entities and enhance the accuracy of the results. This model works well in extreme conditions such as low-light, blurred images, fog, haze or when most of the body or face is covered.