Application of Wavelet Denoising to Improve OFDM‐based Signal Detection and Classification

The developmental emphasis on improving wireless access security through various OSI PHY layer mechanisms continues. This work investigates the exploitation of RF waveform features that are inherently unique to specific devices and that may be used for reliable device classification (manufacturer, model, or serial number). Emission classification is addressed here through detection, location, extraction, and exploitation of RF [fingerprints] to provide device‐specific identification. The most critical step in this process is burst detection which occurs prior to fingerprint extraction and classification. Previous variance trajectory (VT) work provided sensitivity analysis for burst detection capability and highlighted the need for more robust processing at lower signal‐to‐noise ratio (SNR). The work presented here introduces a dual‐tree complex wavelet transform (DT‐ℂWT) denoising process to augment and improve VT detection capability. The new method\u27s performance is evaluated using the instantaneous amplitude responses of experimentally collected 802.11a OFDM signals at various SNRs. The impact of detection error on signal classification performance is then illustrated using extracted RF fingerprints and multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. Relative to previous approaches, the DT‐ℂWT augmented process emerges as a better alternative at lower SNR and yields performance that is 34% closer (on average) to [perfect] burst location estimation performance. Abstract © 2009 John Wiley & Sons, Ltd

Impact of Noise Power Uncertainty on the Performance of Wideband Spectrum Segmentation

The objective of this work is to investigate the impact of noise uncertainty on the performance of a wideband spectrum segmentation technique. We define metrics to quantify the degradation due to noise uncertainty and evaluate the performance using simulations. Our simulation results show that the noise uncertainty has detrimental effects especially for low SNR users

A rare benign disorder mimicking metastasis on radiographic examination: a case report of osteopoikilosis

Osteopoikilosis (OPK) is a rare, autosomal dominant bone disorder, characterized by multiple, discrete round or ovoid radio densities scattered throughout the axial and appendicular skeleton. OPK is usually asymptomatic but rarely there may be slight articular pain and joint effusions. OPK is generally diagnosed incidentally on radiographic examinations and may mimic different bone pathologies, including bone metastases. Radionuclide bone scan has a critical role in distinguishing OPK from osteoblastic bone metastases. In this case report, we present a young man with right hip pain due to OPK, whose plain radiogram and computerized tomography findings thought cancer metastasis

Circulating NK cells and their subsets in Behçet's disease

Behçet's disease (BD) is an autoinflammatory, chronic relapsing/remitting disease of unknown aetiology with both innate and acquired immune cells implicated in disease pathogenesis. Peripheral blood natural killer (NK) cells and their CD56Dim /CD56Bright subsets were surface phenotyped using CD27 and CD16 surface markers in 60 BD patients compared to 60 healthy controls (HCs). Functional potential was assessed by production of interferon (IFN)-γ, granzyme B, perforin and the expression of degranulation marker CD107a. The effects of disease activity (BDActive versus BDQuiet ) and BD medication on NK cells were also investigated. Peripheral blood NK cells (P < 0·0001) and their constituent CD56Dim (P < 0·0001) and CD56Bright (P = 0·0015) subsets were depleted significantly in BD patients compared to HCs, and especially in those with active disease (BDActive ) (P < 0·0001). BD patients taking azathioprine also had significantly depleted NK cells compared to HCs (P < 0·0001). A stepwise multivariate linear regression model confirmed BD activity and azathioprine therapy as significant independent predictor variables of peripheral blood NK percentage (P < 0·001). In general, CD56Dim cells produced more perforin (P < 0·0001) and granzyme B (P < 0·01) expressed higher CD16 levels (P < 0·0001) compared to CD56Bright cells, confirming their increased cytotoxic potential with overall higher NK cell CD107a expression in BD compared to HCs (P < 0·01). Interestingly, IFN-γ production and CD27 expression were not significantly different between CD56Dim /CD56Bright subsets. In conclusion, both BD activity and azathioprine therapy have significant independent depletive effects on the peripheral blood NK cell compartment.Centre for Clinical and Diagnostic Oral Sciences, Institute of Dentistry, Barts and The London School of Medicine and Dentistry, Queen Mary University of London Wellcome Trust . Grant Number: 096954/Z/1

Detection of hand osteoarthritis from hand radiographs using convolutional neural networks with transfer learning

Erbay, Hasan/0000-0002-7555-541XWOS:000576688200001Osteoarthritis is the most common type of arthritis. Hand osteoarthritis leads to specific structural changes in the joints, such as asymmetric joint space narrowing and osteophytes (bone spurs). Conventional radiography has traditionally been the primary method of visualizing these structural changes and diagnosing osteoarthritis. We aimed to develop a computerized method that is capable of determining the structural changes seen in radiography of the hand and to assist practitioners in interpreting radiographic changes and diagnosing the disease. In this retrospective study, transfer-learning-based convolutional neural networks were trained on a randomly selected dataset containing 332 radiography images of hands from an original set of 420 and were validated with the remaining 88. Multilayer convolutional neural network models were designed based on a transfer learning method using pretrained AlexNet, GoogLeNet, and VGG-19 networks. The accuracies of the models were 93.2% for AlexNet, 94.3% for GoogLeNet, and 96.6% for VGG-19. The sensitivities of these models were 0.9167 for AlexNet, 0.9184 for GoogLeNet, and 0.9574 for VGG-19, while the specificity values were 0.9500, 0.9744, and 0.9756, respectively. The performance metrics, including accuracy, sensitivity, specificity, and precision, of our newly developed automated diagnosis methods are promising in the diagnosis of hand osteoarthritis. Our computer-aided detection systems may help physicians in interpreting hand radiography images, diagnosing osteoarthritis, and saving time