The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

Compact solid-state sources of terahertz (THz) radiation are being sought for sensing, imaging, and spectroscopy applications across the physical and biological sciences. We demonstrate that coherent continuous-wave THz radiation of sizable power can be extracted from intrinsic Josephson junctions in the layered high-temperature superconductor Bi2Sr2CaCu2O8. In analogy to a laser cavity, the excitation of an electromagnetic cavity resonance inside the sample generates a macroscopic coherent state in which a large number of junctions are synchronized to oscillate in phase. The emission power is found to increase as the square of the number of junctions reaching values of 0.5 microwatt at frequencies up to 0.85 THz, and persists up to ∼50 kelvin. These results should stimulate the development of superconducting compact sources of THz radiation.

http://absimage.aps.org/image/MAR08/MWS_MAR08-2007-004672.pdf

Emission of Coherent THz-Radiation from Superconductors.

This paper presents an in-depth analysis toward understanding the business value components an organization can derive from adopting radio frequency identification (RFID). Although this subject is currently a hot topic, many organizations are slow in warming up to the idea of using RFID to conduct more effective and efficient business processes. We propose a framework for evaluating the business value of RFID technology, hoping that a better understanding of the business value of RFID will encourage more organizations to implement it. Emphasis is on delivering business value through refining business processes and expanding the business model. We illustrate these concepts drawing on the experience of five early adopters from the Taiwan healthcare industry and formulate this framework as a set of propositions based on relevant literature, cases from pioneers in the field and our intuition. These propositions will need to be validated through empirical evidence. r 2007 Elsevier B.V. All rights reserved.

/pdf/evaluating-the-business-value-of-rfid-evidence-from-five-44860d0jh3.pdf

Evaluating the business value of RFID: Evidence from five case studies

Big data and IoT-based applications in smart environments: A systematic review

Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions

The aim of radio-frequency (RF) sensing for assisted living is to deliver automatic support and monitoring for older people in their homes, impaired patients living independently, individuals in need of continuous support, and people suffering from chronic diseases that require them to stay in care-homes or at hospitals. RF sensing technologies have the potential to improve the quality of living of elderly people or disabled individuals in need of timely assistance. This paper provides a comprehensive review on three of the most innovative RF sensing technologies for activities of daily living in healthcare sector (namely active radar, passive radar, and wireless channel information and RFID sensing) and presents some of the open challenges that need to be addressed.

/pdf/rf-sensing-technologies-for-assisted-daily-living-in-4ikk015bn2.pdf

RF Sensing Technologies for Assisted Daily Living in Healthcare: A Comprehensive Review

Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.

https://www.mdpi.com/1424-8220/20/9/2653/pdf

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare.

Chaos-based encryption schemes have attracted many researchers around the world in the digital image security domain. Digital images can be secured using existing chaotic maps, multiple chaotic maps, and several other hybrid dynamic systems that enhance the non-linearity of digital images. The combined property of confusion and diffusion was introduced by Claude Shannon which can be employed for digital image security. In this paper, we proposed a novel system that is computationally less expensive and provided a higher level of security. The system is based on a shuffling process with fractals key along with three-dimensional Lorenz chaotic map. The shuffling process added the confusion property and the pixels of the standard image is shuffled. Three-dimensional Lorenz chaotic map is used for a diffusion process which distorted all pixels of the image. In the statistical security test, means square error (MSE) evaluated error value was greater than the average value of 10000 for all standard images. The value of peak signal to noise (PSNR) was 7.69(dB) for the test image. Moreover, the calculated correlation coefficient values for each direction of the encrypted images was less than zero with a number of pixel change rate (NPCR) higher than 99%. During the security test, the entropy values were more than 7.9 for each grey channel which is almost equal to the ideal value of 8 for an 8-bit system. Numerous security tests and low computational complexity tests validate the security, robustness, and real-time implementation of the presented scheme.

A Novel Hybrid Secure Image Encryption Based on Julia Set of Fractals and 3D Lorenz Chaotic Map

Radar is typically associated with defense and military applications, such as the detection and monitoring of ship and aircraft traffic in certain areas. For example, many of us have seen the antennas near the runways of airports while traveling, rotating to scan the surrounding space and discover airplanes approaching or leaving.

/pdf/radar-for-health-care-recognizing-human-activities-and-9avctdd8jh.pdf

Radar for Health Care: Recognizing Human Activities and Monitoring Vital Signs

COVID-19, caused by SARS-CoV-2, has resulted in a global pandemic recently. With no approved vaccination or treatment, governments around the world have issued guidance to their citizens to remain at home in efforts to control the spread of the disease. The goal of controlling the spread of the virus is to prevent strain on hospitals. In this paper, we focus on how non-invasive methods are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. Early detection of COVID-19 can allow for early isolation to prevent further spread. This study outlines the advantages and disadvantages and a breakdown of the methods applied in the current state-of-the-art approaches. In addition, the paper highlights some future research directions, which need to be explored further to produce innovative technologies to control this pandemic.

/pdf/a-review-of-the-state-of-the-art-in-non-contact-sensing-for-3qcy3blqr3.pdf

Syed Aziz Shah

Papers

RF Sensing Technologies for Assisted Daily Living in Healthcare: A Comprehensive Review

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare.

A Novel Hybrid Secure Image Encryption Based on Julia Set of Fractals and 3D Lorenz Chaotic Map

Radar for Health Care: Recognizing Human Activities and Monitoring Vital Signs

A Review of the State of the Art in Non-Contact Sensing for COVID-19