This review paper summarizes various approaches developed in the literature for antenna sensors with an emphasis on flexible solutions. The survey helps to recognize the limitations and advantages of this technology. Furthermore, it offers an overview of the main points for the development and design of flexible antenna sensors from the selection of the materials to the framing of the antenna including the different scenario applications. With regard to wearable antenna sensors deployment, a review of the textile materials that have been employed is also presented. Several examples related to human body applications of flexible antenna sensors such as the detection of NaCl and sugar solutions, blood and bodily variables such as temperature, strain, and finger postures are also presented. Future investigation directions and research challenges are proposed.

https://www.mdpi.com/1996-1944/13/17/3781/pdf

A Review of Flexible Wearable Antenna Sensors: Design, Fabrication Methods, and Applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.

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Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications.

The Internet ofMedical Things (IoMT) is global infrastructure consisting of the collection of medical devices and applications that are interconnected through information and communication technologies [1–3]. IoMT is also known as healthcare IoT. The IoMT uses accelerometer sensor, visual sensor, temperature sensor, carbon dioxide sensor, ECG/EEG/EMG sensor, pressure sensor, gyroscope sensor, blood oxygen saturation sensor, humidity sensor, respiration sensor, and bloodpressure sensor to observe and monitor the patient’s health in a continuous manner. The IoMT sense the patients’ health status and then transfer the clinical data to doctors and care holders with the help of remote cloud data centers [4, 5]. This data is most often used for disease diagnosis and clinical care. The useful information mined from the clinical database is used for preventing and protecting the patient health during situations of emergency. However, the main challenge in IoMT is how to manage with critical applications, where a number of connected devices generate a large amount of medical data [6, 7]. This large volume of data often called big data that cannot be processed by traditional data processing algorithms and applications. By intelligently investigating and collecting large amounts of medical data (big data), IoMT can enhance the decision making and early disease diagnosis. Hence, there is a need for scalable machine learning and intelligent algorithms that lead to more interoperable solutions and that can make effective decisions in emerging IoMT. Padmavathy et al. investigated the feasibility of using circular and rectangular slotted microstrip patch antenna as a strain sensor for structural health monitoring without these aforementioned problems. The qualitative results obtained show that developed design of microstrip patch antenna can successfully detect and characterize the structural and rail line cracks [8]. Weiping Zhang et al. have monitored the medical data in the Internet of things and studied focusing on data fusion and related routing technology. According to the particularity of the data in the medical Internet of things, a data fusion cluster—tree construction algorithm based on event-driven (DFCTA) is proposed. The fusion delay problem in the network is analyzed, and the minimum fusion delay method is proposed by calculation of the fusion waiting time of the nodes [9]. Guizhen He et al. have solved skeleton extraction problems in the tree point cloud model; branch geometric features and local properties of point cloud are utilized to optimize tree skeleton extraction. The experiment results show that, this method displays strong anti-interference and high precision characteristics at branch bifurcation and crossed ending parts of fine tree branches [10]. Murali Dhar M S et al. have proposed a new policy oriented secured service model for providing the security to the services in cloud. The proposed model is the combination of a trust aware policy scheduling algorithm and an effective and intelligent re-encryption scheme. This model assured the scalability, reliability, and the security for the stored e-commerce data and access services [11]. Ching-Hsue Cheng et al. have modeled a hybrid knowledge-based classification system to organize expert experiences, integrated linear and nonlinear attribute-selection methods, data discretization of smart expert method, rough * Gunasekaran Manogaran gmanogaran@ucdavis.edu

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Emerging trends, issues, and challenges in Internet of Medical Things and wireless networks

Spectrum energy detection in cognitive radio networks based on a novel adaptive threshold energy detection method

A compact ultrawideband (UWB) antenna based on a hexagonal split-ring resonator (HSRR) is presented in this paper for sensing the pH factor The modified HSRR is a new concept regarding the conventional square split-ring resonator (SSRR) Two HSRRs are interconnected with a strip line and a split in one HSRR is introduced to increase the electrical length and coupling effect The presented UWB antenna consists of three unit cells on top of the radiating patch element This combination of UWB antenna and HSRR gives double-negative characteristics which increase the sensitivity of the UWB antenna for the pH sensor The proposed ultrawideband antenna metamaterial sensor was designed and fabricated on FR-4 substrate The electrical length of the proposed metamaterial antenna sensor is 0238 × 0194 × 0016 λ, where λ is the lowest frequency of 3 GHz The fractional bandwidth and bandwidth dimension ratio were achieved with the metamaterial-inspired antenna as 14691% and 318305, respectively The operating frequency of this antenna sensor covers the bandwidth of 17 GHz, starting from 3 to 20 GHz with a realized gain of 388 dB The proposed HSRR-based ultrawideband antenna sensor is found to reach high gain and bandwidth while maintaining the smallest electrical size, a highly desired property for pH-sensing applications

A Compact Ultrawideband Antenna Based on Hexagonal Split-Ring Resonator for pH Sensor Application.

Structural health monitoring (SHM) is the technique to identify the damage in the building or physical structures. Several structural health monitoring has been proposed in recent years. Increase in number of sensors and its wiring mechanism causes issue in signal processing demand, efficiency, and unreliability. In current state, already present sensors cannot be used for Structural Health Monitoring because of its high cost and low battery capacity, although none of these techniques are capable of monitoring the condition of complex physical structures. The measurement of strain can be done by using patch antenna as strain sensors. In currently available sensors only the wireless range. The main objective of this paper is to investigate the feasibility of using circular and rectangular slotted microstrip patch antenna as a strain sensor for structural health monitoring without these afore mentioned problems. These antenna sensors' (slotted rectangular and circular microstrip patch antennas) simulation results are confirmed. From the simulation results, the sensor provides information about crack orientation.

Design and development of microstrip patch antenna with circular and rectangular slot for structural health monitoring

Breast cancer is the most invasive cancer in women and second leading cause of mortality in women. Advances in detection and treatment have improved survival rates dramatically in patients. Now-a-days mammographic image based analysis is widely used to predict the breast cancer. The diagnosis is simple and survivals of the patient are high if the breast cancer is predicted at the early stage accurately. This paper heavily focuses on methodologies which provides accurate detection of cancerous tissues and thereby reduces the death rates. The algorithms are non-subsampled Shearlet transform (NSST) for image preprocessing, Adaptive clustering for image segmentation and finally adaptive neuro-fuzzy inference system (ANFIS) for image classification. NSST is an extension of wavelet transform in multidimensional and multidirectional case wherein the components are processed with the help of thresholding scheme. Adaptive clustering algorithm separates the pixels in the image into clusters based on both their intensity and sensitivity and thereby makes the segmentation much simpler and easier. ANFIS algorithm is the classifier which is a combination of both neural network adaptive capabilities and the fuzzy logic qualitative approach which is used to classify the normal and abnormal image accurately. The robustness of the proposed methodology is examined using classification accuracy, sensitivity and specificity.

Adaptive clustering based breast cancer detection with ANFIS classifier using mammographic images

Spectrum detecting is the essential and crucial mechanisms of cognitive radio (CR) to the invention the unemployed spectrum. CR system has been suggested as a conceivable resolution for enhancing the spectrum use by empowering unprincipled spectrum sharing. The principal prerequisite for enabling CR to utilize authorized range on an optional premise is not making interfering to primary users. The principal goal of CR is to use rare and limited natural resource efficiently with no obstruction to the primary users (PUs). This work presents an overview of CR architecture, discusses the characteristics and benefits of a CR. Energy identification, matched channel filter detection, and cyclostationary recognitions are most conventional techniques for spectrum sensing. The explanation behind picking energy detection procedure, it did not need any previous info from the primary user transmission. Additionally, the particular result of energy detection technique corrupts with a lower sign to noise ratio (SNR) level signal area. General detection performance of energy detection exceptionally depends upon noise, mainly while the SNR is low for PU. To consider this issue, this paper shows a remarkable, augmented threshold model for efficient energy detection procedure to improve the detection execution at low SNR level. The simulation results demonstrate the energy detection performance utilizing proposed system model is excellent than a fixed threshold at low SNR signal areas.

Classical energy detection method for spectrum detecting in cognitive radio networks by using robust augmented threshold technique

https://robots.iopscience.iop.org/article/10.1088/1757-899X/994/1/012033/pdf

Design and Simulation of MIMO Antennas for Mobile Communication

Cancer is the uncontrolled growth of abnormal cells; if not detected and treated at the early stage cancer would become fatal. Breast cancer remains the second leading cause of cancer induced deaths, despite the advances taken place in treatment methods. The incidence of breast cancer in India is on the rise that one in 22 women in India is likely to suffer from breast cancer during her lifetime. One in eight women is affected by this dangerous disease in America. Cancer always a major problem to the world however various methods are available. Some of methods for breast cancer detection are X-ray mammography, MRI and ultrasound. However, they have some limitations to detect the breast cancer but microwave imaging is a safer method as compared to the X-ray. In this Paper cancer tumour in the breast is detected by I-shaped dual C-slotted microstrip patch antenna is designed for 10 GHz. The proposed antenna has a bandwidth of 7.51 dB covering the frequency range 4.9–7.89 GHz, the return loss below − 37 dB and antenna gain of 2.45 dB. The flexible this patch antenna holds good efficiency of 87.1%.

D. S. Bhargava

Papers

Design and development of microstrip patch antenna with circular and rectangular slot for structural health monitoring

Adaptive clustering based breast cancer detection with ANFIS classifier using mammographic images

Classical energy detection method for spectrum detecting in cognitive radio networks by using robust augmented threshold technique

Design and Simulation of MIMO Antennas for Mobile Communication

Design of I-shaped dual C-slotted rectangular microstrip patch antenna (I-DCSRMPA) for breast cancer tumor detection