Cognitive radios have been proposed as a means to implement efficient reuse of the licensed spectrum. The key feature of a cognitive radio is its ability to recognize the primary (licensed) user and adapt its communication strategy to minimize the interference that it generates. We consider a communication scenario in which the primary and the cognitive user wish to communicate to different receivers, subject to mutual interference. Modeling the cognitive radio as a transmitter with side-information about the primary transmission, we characterize the largest rate at which the cognitive radio can reliably communicate under the constraint that (i) no interference is created for the primary user, and (ii) the primary encoder-decoder pair is oblivious to the presence of the cognitive radio.

Cognitive Radio: An Information-Theoretic Perspective

Speckle noise is an inherent but annoying property in the synthetic aperture radar (SAR) imaging. In this paper we investigate the influence of speckle on the classical convolutional neural network (CNN) for SAR target classification. Then a dual stage coupled CNN architecture, named despeckling and classification coupled CNNs (DCC-CNNs), is proposed to distinguish multiple categories of ground targets in SAR images with strong and varying speckle. It first applies the despeckling sub-network for noise reduction. After that, residual speckle features as well as target information would be learned by the classification sub-network in order to solve the noise robustness problem of CNN. Besides, a new quantitative measure is developed for the quality assessment of SAR target images. It takes into account structural properties of the speckled SAR image of the target of interest and consistency with visual perception. Finally, a series of comparative experiments and discussions are carried out to validate the proposed assessment criterion and DCC-CNNs. Using synthetic SAR images based on the public MSTAR datasets, results show that the overall classification accuracy for ten ground target classes could be higher than 82% at a variety of speckle noise levels.

Ground Target Classification in Noisy SAR Images Using Convolutional Neural Networks

The COVID-19 pandemic has wreaked havoc globally and still persists even after a year of its initial outbreak. Several reasons can be considered: people are in close contact with each other, i.e., at a short range (1 m), and the healthcare system is not sufficiently developed or does not have enough facilities to manage and fight the pandemic, even in developed countries such as the USA and the U.K. and countries in Europe. There is a great need in healthcare for remote monitoring of COVID-19 symptoms. In the past year, a number of IoT-based devices and wearables have been introduced by researchers, providing good results in terms of high accuracy in diagnosing patients in the prodromal phase and in monitoring the symptoms of patients, i.e., respiratory rate, heart rate, temperature, etc. In this systematic review, we analyzed these wearables and their need in the healthcare system. The research was conducted using three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between December 2019 and June 2021. This article was based on the PRISMA guidelines. Initially, 1100 articles were identified while searching the scientific literature regarding this topic. After screening, ultimately, 70 articles were fully evaluated and included in this review. These articles were divided into two categories. The first one belongs to the on-body sensors (wearables), their types and positions, and the use of AI technology with ehealth wearables in different scenarios from screening to contact tracing. In the second category, we discuss the problems and solutions with respect to utilizing these wearables globally. This systematic review provides an extensive overview of wearable systems for the remote management and automated assessment of COVID-19, taking into account the reliability and acceptability of the implemented technologies.

The Rise of Wearable Devices during the COVID-19 Pandemic: A Systematic Review.

Classical radar systems have been designed primarily for military operations. Of late many interesting ways of using radio-frequency spectrum for radar purpose have been under research. One such concept is commensal1 or passive radar, which uses signals of opportunity to detect targets without affecting the functionality of the parent system [1]–[7].

GSM-based commsense system to measure and estimate environmental changes

Ubiquitously deployed Internet of Things (IoT)-based automatic vehicle classification systems will catalyze data-driven traffic flow optimization in future smart cities and will transform the road infrastructure itself into a dynamically sensing cyber–physical system. Although a wide range of different traffic sensing systems has been proposed, the existing solutions are not yet able to simultaneously satisfy the multitude of requirements, e.g., accuracy, robustness, cost efficiency, and privacy preservation. In this article, we present a novel approach, which exploits radio fingerprints—multidimensional attenuation patterns of wireless signals—for accurate and robust vehicle detection and classification. The proposed system can be deployed in a highly cost-efficient manner as it relies on off-the-shelf embedded devices which are installed into existing delineator posts. In a comprehensive field evaluation campaign, the performance of the radio fingerprinting-based approach is analyzed within an experimental live deployment on a German highway, where it is able to achieve a binary classification success ratio of more than 99% and an overall accuracy of 93.83% for a classification task with seven different classes.

The Channel as a Traffic Sensor: Vehicle Detection and Classification Based on Radio Fingerprinting

In this correspondence we introduce a novel instrumentation strategy which we term as application specific instrumentation (ASIN) and we test the feasibility of the proposed scheme in designing a simulation based breast cancer diagnosis system using ultrawideband (UWB) sensors. Most of the high end instrumentation facilities of the current generation are generic in the sense that they can be used in more than one usage. Hence, they perform in a analysis mode. Using these high end instruments information is extracted for a given object under test and based on these information certain decision is made. This generic nature of the instruments make them costly and the postprocessing requirements require the contribution from specialists. In the proposed ASIN scheme, the observations from the sensors are directly used to make the required decision without extracting any intermediate information. This in turn makes the system highly specialised for a given application. At the same time this reduce the cost and the demand for specialists. We have tested the feasibility of such a system in breast cancer diagnosis using UWB sensors. The analysis, though based on simulated data, shows that the system is feasible. Such a strategy will prove invaluable in making application specific instruments ubiquitous. More than this, the low-cost and reduced demand for specialists features of ASIN make it suitable for exploration in numerous usages in the developing nations.

Application specific instrumentation and its feasibility for UWB sensor based breast cancer diagnosis

This paper describes a novel way to use the existing communication infrastructure of LTE (Long Term Evolution) to monitor the change in the environment along with its feasibility analysis. This system is named LTE-CommSense by the authors and can be used in many environment sensing objectives namely monitoring of crop growth over a period of time, disaster monitoring, sea state monitoring, snow avalanche monitoring, security of large unmanned landscapes etc. This technology focuses on already known information from received signal e.g. reference symbols in the LTE communication signal frames. In the equalizer block of the receiver, the equalizer tap coefficients gets modified while training the equalizer using reference symbols. These modifications depend on the channel through which the received signal has passed through. Therefore the adaptive equalizer tap coefficients contain information related to the channel condition. We propose to use these coefficients to get an estimate of change in the channel properties. LTE spectrum and infrastructure will be utilized to get channel characteristics without affecting the existing communication system. After the channel information is received, phenomenological knowledge about the environment will be obtained using ASIN (Application Specific INstrumentation) phylosophy. We present a feasibility analysis of the novel scheme in this paper. We used LTE specific channel models from ITU (International Telecommunication Union) with AWGN noise as the dataset and nearest neighbour based classifier to validate this scheme.

LTE-CommSense system and its feasibility analysis

The authors demonstrated a vehicle detection and classification method based on long-term evolution (LTE) communication infrastructure-based environment-sensing instrument, termed as LTE-CommSense by the authors. This technology is a novel passive sensing system which focuses on the reference signals embedded in the sub-frames of LTE resource grid. It compares the received signal with the expected reference signal, extracts the evaluated channel state information (CSI) and analyses it to estimate the change in the environment. For vehicle detection and subsequent classification, authors' setup is similar to a passive radar in forward scattering radar (FSR) mode. Instead of performing the radio frequency (RF) signals directly, the authors take advantage of the processing that happens in a LTE receiver user equipment (UE). The authors tap into the channel estimation and equalisation block and extract the CSI value. CSI value reflects the property of the communication channel between communication base station (eNodeB) and UE. The authors use CSI values for with vehicle and without vehicle case in outdoor open road environment. Being a receiver-only system, there is no need for any transmission and related regulations. Therefore, this system is low cost, power-efficient and difficult to detect. Also, most of its processing will be done by the existing LTE communication receiver (UE). Here, the authors establish authors' claim by analysing field-collected data. Live LTE downlink (DL) signal is captured using modelled LTE UE using software defined radio (SDR). The detection analysis and classification performance show promising results and ascertain that LTE-CommSense is capable of detection and classification of different types of vehicles in outdoor road environment.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rsn.2018.5231

Vehicle detection and classification using LTE-CommSense

Indoor occupancy estimation is necessary for the efficient operation of smart buildings. The development of an efficient algorithm to estimate indoor occupancy will allow better control and optimiz...

Indoor occupancy estimation using the LTE-CommSense system

The feasibility of using reflected Ultra Wideband (UWB) waves in Hardware/Software (Hw/Sw) Co-design based Artificial Neural Network (ANN) framework as a non-destructive method for dielectric material characterization by estimating their relative dielectric constant, is discussed in this paper. The property of an electromagnetic wave changes owing to the effects of relative dielectric constant & conductivity of a dielectric material. Depending on the relative dielectric constant & conductivity of a dielectric material, the reflection or transmission signal changes in terms of it's amplitude and spread. This property can be utilized to estimate the relative dielectric constant of a dielectric material. First, software implementation was carried out for feasibility analysis. In the next step, Hw/Sw co-design implementation was proposed to overcome the limitations of software implementation of ANN. These approaches are discussed and validated using FDTD simulation.

Santu Sardar

Papers

Application specific instrumentation and its feasibility for UWB sensor based breast cancer diagnosis

LTE-CommSense system and its feasibility analysis

Vehicle detection and classification using LTE-CommSense

Indoor occupancy estimation using the LTE-CommSense system

UWB based dielectric material characterization using hardware/software co-design based ANN