Radio Frequency Data Research Articles

Long Range (LoRa) and Alert Network System for Forest Fire Prediction

Forest fires are a regular occurrence throughout the year with an increasing intensity in the summer and fall periods. Forest fires pose a significant threat to ecosystems, property and human lives. Early detection and rapid response are critical to mitigate the devastating effects of these fires. This article presents a Long Range (LoRa) Alert Network System designed for the early prediction and timely notification of forest fires. The system leverages LoRa technology to create a robust and cost-effective wireless communication network in remote forested areas. These fires primarily stem from various natural and environmental phenomena and natural disasters. The timely dissemination of forest fire alerts was hampered, resulting in delays in fire management. In order to address this issue, efforts were made to enhance the capability for prompt forest fire detection. The challenges predominantly revolve around forested regions where data communication infrastructure is deficient. In the event of a forest fire, there exists a network barrier that impedes information transmission. Consequently, forest fire detection systems leveraging Mesh LoRa networks and image processing networks have been devised. An integrated module within the LoRa/GPS HAT has been explored as a potential solution to the fire predicament. The Flame Sensor Module, functioning as a fire detection sensor component, and the LoRa/GPS HAT, serving as a hardware medium for radio frequency data transmission communication, have been employed. The interconnection of these devices within a network facilitates the development of a prototype fire detection system. Combining a Flame Sensor Module with a LoRa/GPS HAT is indeed a viable approach to creating a prototype fire detection and alert system. By combining the Flame Sensor Module with the LoRa/GPS HAT, a scalable and cost-effective fire detection and alert system appropriate for distant and wooded locations with limited traditional communication infrastructure is created. This system can play a crucial role in early fire detection, potentially reducing the severity of forest fires and protecting both natural resources and human lives.

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning.

Ultrafast ultrasound imaging, characterized by high frame rates, generates low-quality images. Convolutional neural networks (CNNs) have demonstrated great potential to enhance image quality without compromising the frame rate. However, CNNs have been mostly trained on simulated or phantom images, leading to suboptimal performance on in vivo images. In this study, we present a method to enhance the quality of single plane wave (PW) acquisitions using a CNN trained on in vivo images. Our contribution is twofold. Firstly, we introduce a training loss function that accounts for the high dynamic range of the radio frequency data and uses the Kullback-Leibler divergence to preserve the probability distributions of the echogenicity values. Secondly, we conduct an extensive performance analysis on a large new in vivo dataset of 20,000 images, comparing the predicted images to the target images resulting from the coherent compounding of 87 PWs. Applying a volunteer-based dataset split, the peak signal-to-noise ratio and structural similarity index measure increase, respectively, from 16.466 ± 0.801 dB and 0.105 ± 0.060, calculated between the single PW and target images, to 20.292 ± 0.307 dB and 0.272 ± 0.040, between predicted and target images. Our results demonstrate significant improvements in image quality, effectively reducing artifacts.

H-Scan Discrimination for Tumor Microenvironmental Heterogeneity in Melanoma

Melanoma is a form of malignant skin cancer that exhibits significant inter-tumoral differences in the tumor microenvironment (TME) secondary to genetic mutations. The heterogeneity may be subtle but can complicate the treatment of metastatic melanoma, contributing to a high mortality rate. Therefore, developing an accurate and non-invasive procedure to discriminate microenvironmental heterogeneity to facilitate therapy selection is an important goal. In vivo murine melanoma models that recapitulate human disease using synchronous implanted YUMM 1.7 (Yale University Mouse Melanoma) and YUMMER 1.7 (Yale University Mouse Melanoma Exposed to Radiation) murine melanoma lines were investigated. Mice were treated with antibodies to modulate the immune response and longitudinally scanned with ultrasound (US). US radiofrequency data were processed using the H-scan analysis, attenuation estimation and B-mode processing to extract five US features. The measures were used to compare different TMEs (YUMMER vs. YUMM) and responses to immunomodulatory therapies with CD8 depletion or programmed cell death protein 1 (PD-1) inhibition. Multiparametric analysis produced a combined H-scan parameter, resolving significant differences (i) between untreated YUMMER and YUMM and (ii) between untreated, PD-1-treated and CD8-treated YUMMER. However, more importantly, the B-mode and attenuation measures failed to differentiate YUMMER and YUMM and to monitor treatment responses, indicating that H-scan is required to differentiate subtle differences within the TME. We anticipate that the H-scan analysis could discriminate heterogeneous melanoma metastases and guide diagnosis and treatment selection, potentially reducing the need for invasive biopsies or immunologic procedures.

Deep Learning‐Based Bluetooth Low‐Energy 5.1 Multianchor Indoor Positioning with Attentional Data Filtering

Indoor positioning system (IPS) technologies have widespread applications in logistics, intelligent manufacturing, healthcare monitoring, etc. The recently released Bluetooth low‐energy (BLE) 5.1 specification enables in‐phase and quadrature‐phase (I/Q) data measurements. It allows angle of arrival estimation and becomes a natural choice for IPS implementation. Conventional BLE 5.1 IPSs use multiple anchors to provide massive redundancy to improve system robustness. It however demands effective approaches to leverage redundancy. Besides, interference due to various environmental factors can introduce severe errors to I/Q data and affect positioning accuracy. Facing these challenges, herein, a novel deep learning‐based multianchor BLE 5.1 IPS is proposed. The system aggregates measurements from multiple anchors and makes them available at regular time steps. Then, a novel attentional filtering network tailored to infer high‐quality I/Q sample data is developed and a spatial regularization loss incorporating spatial location relationships to strengthen the feature embedding discrimination is proposed. Two multianchor BLE 5.1 I/Q sample datasets are developed and released for public download. Numerical experiments are carried out to compare the proposed method with previous BLE 5.1 IPS methods and methods utilizing other radio frequency data. Results indicate that the proposed method consistently achieves submeter accuracy and significantly outperforms the state‐of‐the‐art approaches.

Multimodal image feature fusion for improving medical ultrasound image segmentation

Ultrasound imaging is the most commonly used technique to detect abnormalities of the thyroid and diagnose abnormal breasts. However, due to the inherent characteristics of ultrasound imaging, the contrast of ultrasound images is relatively low, and the tissue boundaries in the image are blurry. This makes it a challenging task to segment thyroid or breast lesions from ultrasound images. In this work, we explore the role of multimodal features at different levels of deep neural network models and their fusion mechanisms for multimodal images generated by ultrasound radio frequency (RF) data. The ultrasound RF data contain information related to the arrangement of scatterers in tissues and organs, which is only partially available in B-mode images. To solve this problem, we use ultrasound RF data to generate parametric images, including Nakagami and entropy images, to illustrate the abundant backscattered statistics contained in RF data. A channel-aware fusion module is proposed to adaptively fuse the features from images of different modalities. Experimental results show that the abundant information contained in RF data can be helpful to identify thyroid glands or breast lesions with low contrast and fuzzy boundaries in B-mode ultrasound images and improve the segmentation performance.

Multi-Frequency 3D Shear Wave Absolute Vibro-Elastography (S-WAVE) System for the Prostate.

This article describes a novel system for quantitative and volumetric measurement of tissue elasticity in the prostate using simultaneous multi-frequency tissue excitation. Elasticity is computed by using a local frequency estimator to measure the three-dimensional local wavelengths of steady-state shear waves within the prostate gland. The shear wave is created using a mechanical voice coil shaker which transmits simultaneous multi-frequency vibrations transperineally. Radio frequency data is streamed directly from a BK Medical 8848 transrectal ultrasound transducer to an external computer where tissue displacement due to the excitation is measured using a speckle tracking algorithm. Bandpass sampling is used that eliminates the need for an ultra-fast frame rate to track the tissue motion and allows for accurate reconstruction at a sampling frequency that is below the Nyquist rate. A roll motor with computer control is used to rotate the transducer and obtain 3D data. Two commercially available phantoms were used to validate both the accuracy of the elasticity measurements as well as the functional feasibility of using the system for in vivo prostate imaging. The phantom measurements were compared with 3D Magnetic Resonance Elastography (MRE), where a high correlation of 96% was achieved. In addition, the system has been used in two separate clinical studies as a method for cancer identification. Qualitative and quantitative results of 11 patients from these clinical studies are presented here. Furthermore, an AUC of 0.87±0.12 was achieved for malignant vs. benign classification using a binary support vector machine classifier trained with data from the latest clinical study with leave one patient out cross-validation.

Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography.

Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound's poor ability to capture information pertaining to the lateral direction, the existing displacement estimation techniques fail to generate an accurate lateral displacement or strain map. The attempts made in the literature to mitigate this well-known issue suffer from one of the following limitations: 1) Sampling size is substantially increased, rendering the method computationally and memory expensive. 2) The lateral displacement estimation entirely depends on the axial one, ignoring data fidelity and creating large errors. This paper proposes exploiting the effective Poisson's ratio (EPR)-based mechanical correspondence between the axial and lateral strains along with the RF data fidelity and displacement continuity to improve the lateral displacement and strain estimation accuracies. We call our techniques MechSOUL (Mechanically-constrained Second-Order Ultrasound eLastography) and L1 -MechSOUL ( L1 -norm-based MechSOUL), which optimize L2 - and L1 -norm-based penalty functions, respectively. Extensive validation experiments with simulated, phantom, and in vivo datasets demonstrate that MechSOUL and L1 -MechSOUL's lateral strain and EPR estimation abilities are substantially superior to those of the recently-published elastography techniques. We have published the MATLAB codes of MechSOUL and L1 -MechSOUL at https://code.sonography.ai.

Noninvasive Evaluation of Breast Tumor Response to Combined Ultrasound-Stimulated Microbubbles and Hyperthermia Therapy Using Quantitative Ultrasound-Based Texture Analysis Method.

Quantitative ultrasound (QUS) is a noninvasive imaging technique that can be used for assessing response to anticancer treatment. In the present study, tumor cell death response to the ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) treatment was monitored in vivo using QUS. Human breast cancer cell lines (MDA-MB-231) were grown in mice and were treated with HT (10, 30, 50, and 60 minutes) alone, or in combination with USMB. Treatment effects were examined using QUS with a center frequency of 25 MHz (bandwidth range: 16 to 32 MHz). Backscattered radiofrequency (RF) data were acquired from tumors subjected to treatment. Ultrasound parameters such as average acoustic concentration (AAC) and average scatterer diameter (ASD), were estimated 24 hours prior and posttreatment. Additionally, texture features: contrast (CON), correlation (COR), energy (ENE), and homogeneity (HOM) were extracted from QUS parametric maps. All estimated parameters were compared with histopathological findings. The findings of our study demonstrated a significant increase in QUS parameters in both treatment conditions: HT alone (starting from 30 minutes of heat exposure) and combined treatment of HT plus USMB finally reaching a maximum at 50 minutes of heat exposure. Increase in AAC for 50 minutes HT alone and USMB +50 minutes was found to be 5.19 ± 0.417% and 5.91 ± 1.11%, respectively, compared to the control group with AAC value of 1.00 ± 0.44%. Furthermore, between the treatment groups, ΔASD-ENE values for USMB +30 minutes HT significantly reduced, depicting 0.00062 ± 0.00096% compared to 30 minutes HT only group, showing 0.0058 ± 0.0013%. Further, results obtained from the histological analysis indicated greater cell death and reduced nucleus size in both HT alone and HT combined with USMB. The texture-based QUS parameters indicated a correlation with microstructural changes obtained from histological data. This work demonstrated the use of QUS to detect HT treatment effects in breast cancer tumors in vivo.

Harmonic Motion Imaging-Guided Focused Ultrasound Ablation: Comparison of Three Focused Ultrasound Interference Filtering Methods

Harmonic motion imaging (HMI) is an acoustic radiation force-based elasticity imaging technique, which can be used to monitor changes in tissue mechanical properties caused by focused ultrasound (FUS)-induced thermal ablation. In conventional HMI, the amplitude-modulated FUS sequence and imaging pulse are transmitted simultaneously. With this method, the high-amplitude FUS signal must be separated from the imaging data for tissue displacement estimation. Frequency domain notch filtering and bandpass filtering were previously used to filter FUS interference from imaging data. However, FUS interference becomes more pronounced at the increased intensities and durations necessary for thermal ablation, rendering frequency domain filtering challenging. In this study, three methods for FUS interference filtering during HMI-guided FUS ablation (HMIgFUS) were compared. The methods were notch filtering; interleaved HMIgFUS, with interleaved FUS and imaging pulses; and FUS-net, a convolutional neural network-based U-Net autoencoder developed by our group for FUS interference filtering in radiofrequency data. FUS-net was applied here for the first time for the purpose of ablation displacement monitoring. The three filtering methods were tested during 20 HMIgFUS acquisitions in an ex vivo canine liver using a range of peak positive pressures from 11 to 18 MPa and durations ranging from 60 to 180 seconds. The B-mode mean squared errors (MSEs) and displacement amplitude contrast-to-noise ratios (CNRs) of the three methods were calculated and compared. The interleaved method for HMIgFUS was found to be significantly robust in avoiding FUS interference in all tested cases for FUS ablation monitoring, especially cases with high FUS pressure and long durations, as opposed to traditional notch filtering and FUS-net filtering. CNRs obtained from displacement amplitude maps in the interleaved data set were significantly higher in all cases than those obtained from the notch filtered and FUS-net data sets. There was not a significant trend in displacement CNR between the FUS-net and notch filtered data sets. However, B-mode MSE was found to be significantly higher when comparing the FUS-net and interleaved data sets as opposed to the notch filtered and interleaved data sets, suggesting further potential of FUS-net as an FUS interference filtering method. These findings indicate the robustness of interleaved HMIgFUS in avoiding FUS interference during HMIgFUS monitoring and the advantages, limitations and future potential of FUS-net and notch filtering.

UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey

Unmanned aerial vehicles (UAVs) have gained significant popularity across various domains, but their proliferation also raises concerns about security, public safety, and privacy. Consequently, the detection and tracking of UAVs have become crucial. Among the UAV-monitoring technologies, those suitable for urban Internet-of-Things (IoT) environments primarily include radio frequency (RF), acoustic, and visual technologies. In this article, we provide a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems. Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security.

The Effect of Spatial Velocity Gradients on Block-Matching Accuracy for Ultrasound Velocimetry

Block matching serves as the foundation for ultrasound velocimetry techniques such as blood speckle tracking and echo-particle image velocimetry. Any spatial velocity gradients (SVGs) inside a block-matching pair will result in tracking error, due to both the finite block size and the ultrasound point-spread-function. We assess, using an in silico sinusoidal flow phantom, the effect of SVG magnitude and beam-to-flow angle on block-matching bias and precision. Secondarily we assess the effect that SVGs have on velocimetry bias when using angled plane-wave compounding. The magnitude and angle of SVGs were varied by adjusting the wavelength and direction of a sinusoidal flow profile. Scatterers displaced by this flow profile were used for simulating ultrasound radio frequency data at discrete time points. After beamforming, the 2-D flow field was estimated using block matching. Two imaging sequences were tested, a single plane-wave and a three-angled plane-wave. Smaller sinusoidal flow wavelengths resulted in increased bias and reduced precision, revealing an inverse relationship between sinusoidal flow wavelength and tracking error, with median errors ranging from 69%-90% for the smallest flow wavelengths (highest SVGs) down to 3%-5% for the largest (lowest SVGs). The SVG angle was also important, in which lateral SVGs (with axially oriented flows) resulted in significant speckle decorrelation and high tracking errors in regions with high SVGs. Conversely, axial SVGs (laterally oriented flow) experienced higher bias in the peak velocity regions of the flow profile. Coherent compounding resulted in higher velocity errors than using a single transmission for lateral SVGs but not for axial SVGs. The highest SVGs that could be measured with ≤10% error was when the sinusoidal flow wavelength was less than 20 times the ultrasound pulse wavelength. The clinical significance is that the high SVGs present in high kinetic energy flows, such as severe carotid stenosis and aortic regurgitation, will limit the ability to accurately quantify the velocities in these flow structures.

Spectral-based quantitative ultrasound for assessing vision degrading myodesopsia

The presence of clinically significant vitreous opacities, referred to as vision degrading myodesopsia (VDM), reduces contrast sensitivity (CS), and visual quality of life. Quantitative ultrasound (QUS) methods operating on grayscale B-mode images were previously developed and validated for quantifying vitreous echodensities. Although a subsequent study comparing the QUS methods across clinical scanners demonstrated no change in QUS efficacy, the methods are sensitive to differences in system parameters and B-mode image formation unless the backscatter data is normalized. Technological advancements in clinical scanners allow for easy access to raw radiofrequency (RF) data for more advanced processing methods. Here, we develop QUS methods that exploit RF echo data to generate robust user- and system-independent parameters for quantifying vitreous echodensities based on the echo signal power spectrum (PS). However, the sparsity of echodensities in the vitreous body is incompatible with underlying assumptions of QUS methods based on measurements of the backscatter coefficient or envelope statistics. We, therefore, implement QUS methods that parameterize the PS and correlate these parameters with visual function as measured by CS. Results from this study are the first step toward developing and validating robust, user-, and system-independent QUS methods for evaluating VDM.

Single and multiple scattering quantitative ultrasound methods in the lung

Lung ultrasound images are qualitative and rely on subjective interpretation for diagnosis. Ideally, quantitative ultrasound (QUS) methods applied to lung ultrasound data would provide biomarkers associated with specific diseases in an absolute fashion. While QUS methods are successful in numerous organ systems in preclinical and clinical applications, their direct application to lung ultrasound data requires taking multiple scattering (MS) into account. This presentation shows QUS methods independently exploiting MS and single scattering (SS) components of radio-frequency (RF) data, acquired from lungs from rodent models of pulmonary edema and fibrosis. SS and MS components were obtained using singular value decomposition and eigenvalue thresholding. Spectral and envelope QUS parameters were computed from the SS component. MS-based QUS parameters were obtained by extracting the diffusion constant and tracking the SS intensity decay rate. Stepwise linear discriminant analyses using only three QUS parameters yielded strong correlations between wet-to-dry ratio in healthy and pulmonary edema rats. Strong correlations were also obtained between QUS parameters and modified histological Ashcroft scores in healthy and pulmonary fibrosis rats. These results demonstrate the value of QUS parameters obtained from SS and MS approaches for assessing lung diseases with high specificity and sensitivity. [Supported in part by R21HL154156 and W81XWH1810101.]

Shape estimation of flexible ultrasound arrays using spatial coherence: A preliminary study

A flexible ultrasound array can potentially provide a larger field-of-view, enhanced imaging resolution, and less operator dependency compared to conventional rigid transducer arrays. However, such transducer arrays require information about relative element positions for beamforming and reconstructing geometrically accurate sonograms. In this study, we assess the potential utility of using spatial coherence of backscattered radiofrequency data to estimate transducer array shape (inverse problem). The methodology is evaluated through 1) simulation of flexible arrays and 2) blinded in vivo experiments using commercial rigid transducer arrays on various anatomical targets (shoulder, forearm, scapular, posterior calf muscles, and abdomen) and multi-purpose ultrasound phantoms. The average Euclidean error of shape estimation is below 0.1 wavelengths for simulated arrays and below 1.4 wavelengths (median: 0.58 wavelengths) for real arrays. The complex wavelet structural similarity index between the B-mode images reconstructed with estimated and ground truth array shapes is above 99 % and 96 %, for simulations and experiments, respectively. These findings suggest that optimizing for spatial coherence may be an effective way to estimate the unknown shape of conformal ultrasound arrays.

3-D Transducer Mounted Shear Wave Absolute Vibro-Elastography: Proof of Concept.

Quantitative tissue stiffness characterization using ultrasound (US) has been shown to improve prostate cancer (PCa) detection in multiple studies. Shear wave absolute vibro-elastography (SWAVE) allows quantitative and volumetric assessment of tissue stiffness using external multifrequency excitation. This article presents a proof of concept of a first-of-a-kind 3-D hand-operated endorectal SWAVE system designed to be used during systematic prostate biopsy. The system is developed with a clinical US machine, requiring only an external exciter that can be mounted directly to the transducer. Subsector acquisition of radio frequency (RF) data allows imaging of shear waves with a high effective frame rate (up to 250 Hz). The system was characterized using eight different quality assurance phantoms. Due to the invasive nature of prostate imaging, at this early stage of development, validation of in vivo human tissue was instead carried out by intercostally scanning the livers of n = 7 healthy volunteers. The results are compared with 3-D magnetic resonance elastography (MRE) and an existing 3-D SWAVE system with a matrix array transducer (M-SWAVE). High correlations were found with MRE (99% in phantoms, 94% in liver data) and with M-SWAVE (99% in phantoms, 98% in liver data).

Segmentation of Arm Ultrasound Images in Breast Cancer-Related Lymphedema: A Database and Deep Learning Algorithm.

Breast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the origin of Breast Cancer-Related Lymphedema (BCRL), referring to a noticeable increase in excess arm volume. Ultrasound imaging is a preferred modality for the diagnosis and progression monitoring of BCRL because of its low cost, safety, and portability. As the affected and unaffected arms have similar appearances in B-mode ultrasound images, the thickness of the skin, subcutaneous fat, and muscle have been shown to be important biomarkers for this task. The segmentation masks are also helpful in monitoring the longitudinal changes in morphology and mechanical properties of each tissue layer. For the first time, a publicly available ultrasound dataset containing the Radio-Frequency (RF) data of 39 subjects as well as manual segmentation masks by two experts, are provided. Inter- and intra-observer reproducibility studies performed on the segmentation maps show a high Dice Score Coefficient (DSC) of 0.94±0.08 and 0.92±0.06, respectively. Gated Shape Convolutional Neural Network (GSCNN) is modified for precise automatic segmentation of tissue layers, and its generalization performance is improved by the CutMix augmentation strategy. We got an average DSC of 0.87±0.11 on the test set, which confirms the high performance of the method. Automatic segmentation methods can pave the way for convenient and accessible staging of BCRL, and our dataset can facilitate development and validation of those methods. Timely diagnosis and treatment of BCRL are of crucial importance to prevent irreversible damage.

Ultrasound multifrequency strategy to estimate the lung surface roughness, in silico and in vitro results

Lung ultrasound (LUS) is an important imaging modality to assess the state of the lung surface. Nevertheless, LUS is limited to the visual evaluation of imaging artifacts, especially the vertical ones. These artifacts are observed in pathologies characterized by a reduction of dimensions of air-spaces (alveoli). In contrast, there exist pathologies, such as chronic obstructive pulmonary disease (COPD), in which an enlargement of air-spaces can occur, which causes the lung surface to behave essentially as a perfect reflector, thus not allowing ultrasound penetration. This characteristic high reflectivity could be exploited to characterize the lung surface. Specifically, air-spaces of different sizes could cause the lung surface to have a different roughness, whose estimation could provide a way to assess the state of the lung surface. In this study, we present a quantitative multifrequency approach aiming at estimating the lung surface’s roughness by measuring image intensity variations along the lung surface as a function of frequency. This approach was tested both in silico and in vitro, and it showed promising results. For the in vitro experiments, radiofrequency (RF) data were acquired from a novel experimental model. The results showed consistency between in silico and in vitro experiments.

Distributed Batteryless Access Control for Data and Energy Integrated Networks: Modeling and Performance Analysis

Radio-frequency (RF) signals are capable of simultaneously transferring data and energy from a hybrid access point (HAP) toward battery-powered and batteryless wireless devices. Battery-powered and batteryless wireless devices with the capability of RF energy harvesting need a distributed access control protocol with collision avoidance to achieve higher energy efficiency. We study the performance of a data and energy integrated network (DEIN) that adopts an enhanced carrier sensing multiple access with collision avoidance (CSMA/CA) protocol. Each device in this network can switch to RF energy harvesting mode or data reception mode according to HAP’s instruction, and freezes its backoff counter when energy storage is insufficient. By invoking a three-dimensional (3D) Markov chain, we model the operating behaviors of batteryless wireless devices and an HAP in a DEIN. Apart from backoff operations of devices, the 3D Markov chain also depicts their dynamic energy changes, including RF energy harvesting and energy consumption. Wireless devices consume energy harvested from the HAP’s downlink transmissions for powering their data upload and random backoff. With the aid of the 3D Markov chain, the upload throughput of devices can be obtained in semi-closed-form. Moreover, a decoupling method is proposed to approximate throughput performance with low complexity. The accuracy of our theoretical model is validated by simulation results. By characterizing the impact of various parameters on throughput performance, a design guideline for a DEIN with a distributed batteryless access protocol is provided.

GPU-Free Specific Emitter Identification Using Signal Feature Embedded Broad Learning

Emerging wireless networks may suffer severe security threats due to the ubiquitous access of massive wireless devices. Specific emitter identification (SEI) is considered as one of the important techniques to protect wireless networks, which aims to identifying legal or illegal devices through the radio frequency (RF) fingerprints contained in RF signals. Existing SEI methods are implemented with either traditional machine learning or deep learning. The former relies on manual feature extraction which is usually inefficient, while the latter relies on the powerful GPU computing power but with limited applications and high cost. To solve these problems, in this paper, we propose a GPU-free SEI method using a signal feature embedded broad learning network (SFEBLN), for efficient emitter identification based on a single-layer forward propagation network on the CPU platform. With this method, the original RF data is first pre-processed through external signal processing nodes, and then processed to generate mapped feature nodes and enhancement nodes by nonlinear transformation. Next, we design the internal signal processing nodes to extract effective features from the processed RF signals. The final input layer consists of mapped feature nodes, enhancement nodes and internal signal processing nodes. Then, the network weight parameters are obtained by solving the pseudo inverse problem. Experiments are conducted over the CPU platform and the results show that our proposed SEI method using SFEBLN achieves a superior identification performance and robustness under various scenarios.

RFID-based logistics big data asset evaluation and data mining research

Abstract With the rapid rise of e-commerce platforms, in view of the sharp increase in the amount of data in the logistics system, the timely update and processing of relevant logistics information data have assumed a particular relevance. In this paper, we fully draw on the excellent performance of radio frequency identification (RFID) technology and data mining technology, begin by using RFID technology to authenticate logistics commodities, move on to extracting relevant feature information and finally carry out a detailed comparison between k-nearest neighbour algorithm, support vector machine (SVM) algorithm, logistic regression (LR) algorithm and improved LR algorithm. The algorithm provides a solution method for asset information collection channel and data mining classification algorithm. It meets the needs of different customers, and provides a variety of working modes, which helps to improve the time and operation efficiency of data processing algorithms. The results show that the SVM algorithm only achieves 93.8% accuracy when iterating 50 times for the classification results of the classification samples containing the objective functions of x 1 and x 2. The improved LR algorithm stochastic gradient descent algorithm has a classification accuracy of 94.6% after 50 iterations. The RFID-based logistics big data asset evaluation and data mining research identification algorithm has obvious advantages, and the accuracy rate reaches 97.3%.