Class-Incremental Learning for Wireless Device Identification in IoT

Deep Learning (DL) has been utilized pervasively in the Internet of Things (IoT). One typical application of DL in IoT is device identification from wireless signals, namely Noncryptographic Device Identification (NDI). However, learning components in NDI systems have to evolve to adapt to operational variations, such a paradigm is termed as Incremental Learning (IL). Various IL algorithms have been proposed and many of them require dedicated space to store the increasing amount of historical data, and therefore, they are not suitable for IoT or mobile applications. However, conventional IL schemes can not provide satisfying performance when historical data are not available. In this paper, we address the IL problem in NDI from a new perspective, firstly, we provide a new metric to measure the degree of topological maturity of DNN models from the degree of conflict of class-specific fingerprints. We discover that an important cause for performance degradation in IL enabled NDI is owing to the conflict of devices’ fingerprints. Second, we also show that the conventional IL schemes can lead to low topological maturity of DNN models in NDI systems. Thirdly, we propose a new Channel Separation Enabled Incremental Learning (CSIL) scheme without using historical data, in which our strategy can automatically separate devices’ fingerprints in different learning stages and avoid potential conflict. Finally, We evaluated the effectiveness of the proposed framework using real data from ADS-B (Automatic Dependent Surveillance-Broadcast), an application of IoT in aviation. The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services

Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoT

Abnormality detection is essential to the performance of safety-critical and latency-constrained systems. However, as systems are becoming increasingly complicated with a large quantity of heterogeneous data, conventional statistical change point detection methods are becoming less effective and efficient. Although Deep Learning (DL) and Deep Neural Networks (DNNs) are increasingly employed to handle heterogeneous data, they still lack theoretic assurable performance and explainability. This paper integrates zero-bias DNN and Quickest Event Detection algorithms to provide a holistic framework for quick and reliable detection of both abnormalities and time-dependent abnormal events in Internet of Things (IoT).We first use the zero bias dense layer to increase the explainability of DNN. We provide a solution to convert zero-bias DNN classifiers into performance assured binary abnormality detectors. Using the converted abnormality detector, we then present a sequential quickest detection scheme which provides the theoretically assured lowest abnormal event detection delay under false alarm constraints. Finally, we demonstrate the effectiveness of the framework using both massive signal records from real-world aviation communication systems and simulated data

Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices

The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT makes it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary passively listens to the existing radio communications and then mimic the identity of legitimate devices to conduct malicious activities. Existing solutions employ cryptographic signatures to verify the trustworthiness of received information. In prevalent IoT, secret keys for cryptography can potentially be disclosed and disable the verification mechanism. Noncryptographic device verification is needed to ensure trustworthy IoT. In this article, we propose an enhanced deep learning framework for IoT device identification using physical-layer signals. Specifically, we enable our framework to report unseen IoT devices and introduce the zero-bias layer to deep neural networks to increase robustness and interpretability. We have evaluated the effectiveness of the proposed framework using real data from automatic dependent surveillance-broadcast (ADS-B), an application of IoT in aviation. The proposed framework has the potential to be applied to the accurate identification of IoT devices in a variety of IoT applications and services

Expression of Robo4 in the fibrovascular membranes from patients with proliferative diabetic retinopathy and its role in RF/6A and RPE cells

Purpose: Robo4, a member of the roundabout (Robo) family, acts as a neuronal guidance receptor and plays some role in vasculogenesis and angiogenesis. This study investigated the effect of Robo4 on the formation of fibrovascular membranes (FVMs) from patients with proliferative diabetic retinopathy and its roles in choroid-retina endothelial (RF/6A) and human retinal pigment epithelial (RPE) cells. Methods: RT-PCR and immunohistochemistry were used to determine the levels of mRNA and the presence and distribution of Robo4 in FVMs. Small interfering RNA (siRNA) technology was used to knock down Robo4 expression and to study its effects on RF/6A and RPE cells in vitro. Cell proliferation, migration, spreading, cycling, and apoptosis were assessed with MTT assay, Boyden chamber assay, immunocytochemistry, and flow cytometry. Tube formation by RF/6A on Matrigel was also analyzed. Results: The level of Robo4 mRNA was high in FVMs. Robo4 was expressed in the vessels and fibrous-like tissue co-immunostained for CD31 and GFAP, respectively. Robo4 siRNA knockdown inhibited cell proliferation and migration. Tube formation by RF/6A cells was also disturbed. Under hypoxic conditions, more apoptotic cells were evident among the knockdown cells than among the control cells (p < 0.01). Conclusions: Robo4 may play a role in the formation of FVMs. Silencing the expression of Robo4 in RF/6A and RPE cells inhibited their proliferation and reduced their tolerance of hypoxic conditions, suggesting physiologic functions of Robo4 in the cells of the retina.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000267136400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Biochemistry & Molecular BiologyOphthalmologySCI(E)PubMed15ARTICLE112-131057-10691

Development of an activatable far-red fluorescent probe for rapid visualization of hypochlorous acid in live cells and mice with neuroinflammation

Recent investigations have suggested that abnormally elevated levels of HOCl may be tightly related to the severity of neuroinflammation. Although some successes have been achieved, fluorescent probes with far-red fluorescence emission and capable of detecting HOCl with high specificity in pure aqueous solution are still urgently needed. Herein, a responsive far-red fluorescent probe, DCI-H, has been constructed to monitor HOCl activity in vivo and in vitro. DCI-H could rapidly respond to HOCl within 120 s and had a low detection limit for HOCl of 1.5 nM. Importantly, physiologically common interfering species, except for HOCl, did not cause a change in the fluorescence intensity of DCI-HOCl at 655 nm. The results of confocal imaging demonstrated the ability of DCI-H to visualize endogenous HOCl produced by MPO-catalyzed H2O2/Cl− and LPS stimulation. With the assistance of DCI-H, upregulation of HOCl levels was observed in the mice model of LPS-induced neuroinflammation. Thus, we believed that DCI-H provided a valuable tool for HOCl detection and diagnosis of inflammation-related diseases

Development of Motion Artifact Correction Solutions for the Cone-beam CT Images during Pancreatic Cancer Image-guided Radiotherapy

The cone-beam CT (CBCT) system based on the two-dimensional flat-panel detector technology is widely applied in patient location verification before radiotherapy. However, during the application of intraperitoneal tumor radiotherapy, severe shading and streaking artifacts caused by respiratory movement and intestinal peristalsis make it difficult to distinguish tumor areas from the CBCT images. Due to the non-rigid deformation of flexible organs such as the pancreas under the action of respiratory motion, it is hard to quantify deviation between the body surface motion monitoring results and the actual organ motion, and it is also difficult to monitor irregular motion represented by intestinal peristalsis. There is no effective solution to motion artifact correction in CBCT. Based on theory of biodynamics and common knowledge of human physiology, in this paper we propose a brand new radiotherapy image-guided cone-beam CT motion artifact correction method without motion monitoring or implantation of in-vivo markers. The proposed artifact correction strategy is designed based on the features of the artifact images and fusion of various CT image domain processing algorithms. The results suggest that the image quality of cone beam CT has been significantly improved after the application of this strategy in the clinical abdominal CBCT image processing. The average CT number error in typical soft tissue areas reduces from 90 HU to 30 HU, and the boundary of the intestinal cavity and surrounding soft tissue information are partially recovered. The proposed artifact correction strategy does not require respiratory gating or increase of projections, which can be integrated into existing workflows without marker implantation surgery. The motion-artifact-corrected CBCT images provide more accurate tumor localization information for image-guided radiotherapy of pancreatic carcinoma. The proposed method is proved practical and efficient for clinical application

Class-Incremental Learning for Wireless Device Identification in IoT

Deep Learning (DL) has been utilized pervasively in the Internet of Things (IoT). One typical application of DL in IoT is device identification from wireless signals, namely Noncryptographic Device Identification (NDI). However, learning components in NDI systems have to evolve to adapt to operational variations, such a paradigm is termed as Incremental Learning (IL). Various IL algorithms have been proposed and many of them require dedicated space to store the increasing amount of historical data, and therefore, they are not suitable for IoT or mobile applications. However, conventional IL schemes can not provide satisfying performance when historical data are not available. In this paper, we address the IL problem in NDI from a new perspective, firstly, we provide a new metric to measure the degree of topological maturity of DNN models from the degree of conflict of class-specific fingerprints. We discover that an important cause for performance degradation in IL enabled NDI is owing to the conflict of devices’ fingerprints. Second, we also show that the conventional IL schemes can lead to low topological maturity of DNN models in NDI systems. Thirdly, we propose a new Channel Separation Enabled Incremental Learning (CSIL) scheme without using historical data, in which our strategy can automatically separate devices’ fingerprints in different learning stages and avoid potential conflict. Finally, We evaluated the effectiveness of the proposed framework using real data from ADS-B (Automatic Dependent Surveillance-Broadcast), an application of IoT in aviation. The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services