Recently, ambient backscatter communications has been introduced as a cutting-edge technology which enables smart devices to communicate by utilizing ambient radio frequency (RF) signals without requiring active RF transmission. This technology is especially effective in addressing communication and energy efficiency problems for low-power communications systems such as sensor networks. It is expected to realize numerous Internet-of-Things (IoT) applications. Therefore, this paper aims to provide a contemporary and comprehensive literature review on fundamentals, applications, challenges, and research efforts/progress of ambient backscatter communications. In particular, we first present fundamentals of backscatter communications and briefly review bistatic backscatter communications systems. Then, the general architecture, advantages, and solutions to address existing issues and limitations of ambient backscatter communications systems are discussed. Additionally, emerging applications of ambient backscatter communications are highlighted. Finally, we outline some open issues and future research directions.Index Terms-Ambient backscatter, wireless networks, bistatic backscatter, RFID, wireless energy harvesting, backscatter communications, and passive communications.

BACKGROUND: Approximately 30% of fine‐needle aspiration (FNA) biopsies of thyroid nodules are indeterminate or nondiagnostic. Recent studies suggest microRNA (miRNA, miR) is differentially expressed in malignant tumors and may have a role in carcinogenesis, including thyroid cancer. The authors therefore tested the hypothesis that miRNA expression analysis would identify putative markers that could distinguish benign from malignant thyroid neoplasms that are often indeterminate on FNA biopsy. METHODS: A miRNA array was used to identify differentially expressed genes (5‐fold higher or lower) in pooled normal, malignant, and benign thyroid tissue samples. Real‐time quantitative polymerase chain reaction was used to confirm miRNA array expression data in 104 tissue samples (7 normal thyroid, 14 hyperplastic nodule, 12 follicular variant of papillary thyroid cancer, 8 papillary thyroid cancer, 15 follicular adenoma, 12 follicular carcinoma, 12 Hurthle cell adenoma, 20 Hurthle cell carcinoma, and 4 anaplastic carcinoma cases), and 125 indeterminate clinical FNA samples. The diagnostic accuracy of differentially expressed genes was determined by analyzing receiver operating characteristics. RESULTS: Ten miRNAs showed >5‐fold expression difference between benign and malignant thyroid neoplasms on miRNA array analysis. Four of the 10 miRNAs were validated to be significantly differentially expressed between benign and malignant thyroid neoplasms by quantitative polymerase chain reaction (P < .002): miR‐100, miR‐125b, miR‐138, and miR‐768‐3p were overexpressed in malignant samples of follicular origin (P < .001), and in Hurthle cell carcinoma samples alone (P < .01). Only miR‐125b was significantly overexpressed in follicular carcinoma samples (P < .05). The accuracy for distinguishing benign from malignant thyroid neoplasms was 79% overall, 98% for Hurthle cell neoplasms, and 71% for follicular neoplasms. The miR‐138 was overexpressed in the FNA samples (P = .04) that were malignant on final pathology with an accuracy of 75%. CONCLUSIONS: MicroRNA expression differs for normal, benign, and malignant thyroid tissue. Expression analysis of differentially expressed miRNA could help distinguish benign from malignant thyroid neoplasms that are indeterminate on thyroid FNA biopsy. Cancer 2011. © 2011 American Cancer Society.

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effective and can be widely adopted in practice to keep distance, encourage, and enforce social distancing in general. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II [1] surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacypreserving, scheduling, and incentive mechanisms) in implementing social distancing in practice.

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I [1], an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs.

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