Adversarial Gaussian Denoiser for Multiple-Level Image Denoising.

Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods.

Time-Domain Investigation of Switchable Filter Wide-Band Antenna for Microwave Breast Imaging.

This paper investigates the time-domain performance of a switchable filter impulse radio ultra-wideband (IR-UWB) antenna for microwave breast imaging applications. A miniaturized CPW-fed integrated filter antenna with switchable performance in the range of the Worldwide Interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN) bands could operate well within a 3.0 to 11 GHz frequency range. The time-domain performance of the filter antenna was investigated in comparison to that of the designed reference wideband antenna. By comparing both antennas' time-domain characteristics, it was seen that the switchable filter antenna had good time-domain resolution along with the frequency-domain operation. Additionally, the time-domain investigation revealed that the switchable filter wide-band antenna performed similarly to the reference wide band antenna. This antenna was also utilized for a tumor detection application, and it was seen that the switchable filter wide-band antenna could detect a miniaturized irregularly shaped tumor easily, which is quite promising. Such an antenna with a good time-domain resolution and tumor detection capability will be a good candidate and will find potential applications in microwave breast imaging.

Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications.

Unmanned aerial vehicles (UAVs) have become popular in surveillance, security, and remote monitoring. However, they also pose serious security threats to public privacy. The timely detection of a malicious drone is currently an open research issue for security provisioning companies. Recently, the problem has been addressed by a plethora of schemes. However, each plan has a limitation, such as extreme weather conditions and huge dataset requirements. In this paper, we propose a novel framework consisting of the hybrid handcrafted and deep feature to detect and localize malicious drones from their sound and image information. The respective datasets include sounds and occluded images of birds, airplanes, and thunderstorms, with variations in resolution and illumination. Various kernels of the support vector machine (SVM) are applied to classify the features. Experimental results validate the improved performance of the proposed scheme compared to other related methods.

Integrated LTE and Millimeter-Wave 5G MIMO Antenna System for 4G/5G Wireless Terminals.

This work demonstrates an integrated multiple-input multiple-output (MIMO) antenna solution for Long Term Evolution (LTE) and Millimeter-Wave (mm-wave) 5G wireless communication services. The proposed structure is comprised of a two-element LTE MIMO antenna, and a four-element 5G MIMO configuration with rectangular and circular defects in the ground plane. For experimental validation, the proposed structure is fabricated on a Rogers RO4350B substrate with 0.76 mm thickness. The overall substrate dimensions are 75 mm × 110 mm. The proposed structure is capable of operating at 5.29-6.12 GHz (LTE 46 and 47 bands) and 26-29.5 GHz (5G mm-wave) frequency bands. Additionally, the measured peak gain of 5.13 and 9.53 dB is attained respectively for the microwave and mm-wave antennas. Furthermore, the analysis of the MIMO performance metrics demonstrates good characteristics, and excellent field correlation performance across the operating bands. Furthermore, the analysis of the Specific Absorption Rate (SAR) and Power Density (PD) at the lower frequency band (5.9 GHz) and PD only at mm-Wave frequency band (28 GHz) verifies that the proposed antenna system satisfies the international human safety standards. Therefore, the proposed integrated MIMO antenna configuration ascertains to be a potential contender for the forthcoming communication applications.

EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach.

Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. This paper presents an advanced signal processing method using the deep neural network (DNN) for emotion recognition based on EEG signals. The spectral and temporal components of the raw EEG signal are first retained in the 2D Spectrogram before the extraction of features. The pre-trained AlexNet model is used to extract the raw features from the 2D Spectrogram for each channel. To reduce the feature dimensionality, spatial, and temporal based, bag of deep features (BoDF) model is proposed. A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. Lastly, the emotion of each subject is represented using the histogram of the vocabulary set collected from the raw-feature of a single channel. Features extracted from the proposed BoDF model have considerably smaller dimensions. The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. For optimal classification performance, we use a support vector machine (SVM) and k-nearest neighbor (k-NN) to classify the extracted features for the different emotional states of the two data sets. The BoDF model achieves 93.8% accuracy in the SEED data set and 77.4% accuracy in the DEAP data set, which is more accurate compared to other state-of-the-art methods of human emotion recognition.

The Smallest Form Factor UWB Antenna with Quintuple Rejection Bands for IoT Applications Utilizing RSRR and RCSRR.

In this paper, we present the smallest form factor microstrip-fed ultra-wideband antenna with quintuple rejection bands for use in wireless sensor networks, mobile handsets, and Internet of things (IoT). Five rejection bands have been achieved at the frequencies of 3.5, 4.5, 5.25, 5.7, and 8.2 GHz, inseminating four rectangular complementary split ring resonators (RCSRRs) on the radiating patch and placing two rectangular split-ring resonators (RSRR) near the feedline-patch junction of the conventional ultra-wideband (UWB) antenna. The design guidelines of the implemented notched bands are provided at the desired frequency bands and analyzed. The measured results demonstrate that the proposed antenna delivers a wide impedance bandwidth from 3 to 11 GHz with a nearly omnidirectional radiation pattern, high rejection in the multiple notched-bands, and good radiation efficiency over the entire frequency band except at the notched frequencies. Simulated and measured response match well specifically at the stop-bands.

Bandwidth Enhancement and Frequency Scanning Array Antenna Using Novel UWB Filter Integration Technique for OFDM UWB Radar Applications in Wireless Vital Signs Monitoring.

This paper presents the bandwidth enhancement and frequency scanning for fan beam array antenna utilizing novel technique of band-pass filter integration for wireless vital signs monitoring and vehicle navigation sensors. First, a fan beam array antenna comprising of a grounded coplanar waveguide (GCPW) radiating element, CPW fed line, and the grounded reflector is introduced which operate at a frequency band of 3.30 GHz and 3.50 GHz for WiMAX (World-wide Interoperability for Microwave Access) applications. An advantageous beam pattern is generated by the combination of a CPW feed network, non-parasitic grounded reflector, and non-planar GCPW array monopole antenna. Secondly, a miniaturized wide-band bandpass filter is developed using SCSRR (Semi-Complementary Split Ring Resonator) and DGS (Defective Ground Structures) operating at 3⁻8 GHz frequency band. Finally, the designed filter is integrated within the frequency scanning beam array antenna in a novel way to increase the impedance bandwidth as well as frequency scanning. The new frequency beam array antenna with integrated band-pass filter operate at 2.8 GHz to 6 GHz with a wide frequency scanning from the 50 to 125-degree range.

Resonator Based Switching Technique between Ultra Wide Band (UWB) and Single/Dual Continuously Tunable-Notch Behaviors in UWB Radar for Wireless Vital Signs Monitoring.

This paper presents a novel resonator that can switch and create three important behaviors within the same antenna using miniaturized capacitors. The resonator was integrated into conventional Ultra-Wide Band (UWB) antenna to achieve UWB and Single/Dual continuously tunable-notch behaviors. The Single/Dual notched was continuously tuned to our desired frequency band by changing the value of the capacitors. The antenna designed and fabricated to validate these behaviors had a compact size of 24 × 30.5 mm², including the ground plane. The radiation patterns were very clean due to the placement of the proposed resonator in the special ground plane. Moreover, the presented novel resonator and switching technique was compared with the recently proposed resonators and their switching techniques. The prototype for the antenna was also developed in order to validate its performance in wireless vital signs monitoring. The presented miniaturized resonator based antenna was utilized for tumor sensing and simulations were provided in this regard. Moreover, the deployment of the proposed resonator based UWB antenna sensor in Pipeline Integrity Monitoring system was also investigated and discussed.

A Compact Multiple Notched Ultra-Wide Band Antenna with an Analysis of the CSRR-TO-CSRR Coupling for Portable UWB Applications.

We present a compact ultra-wideband (UWB) antenna integrated with sharp notches with a detailed analysis of the mutual coupling of the multiple notch resonators. By utilizing complementary split ring resonators (CSRR) on the radiating semi-circular patch, we achieve the sharp notch-filtering of various bands within the UWB band without increasing the antenna size. The notched frequency bands include WiMAX, INSAT, and lower and upper WLAN. In order to estimate the frequency shifts of the notch due to the coupling of the nearby CSRRs, an analysis of the coupling among the multiple notch resonators is carried out and we construct the lumped-circuit equivalent model. The time domain analysis of the proposed antenna is performed to show its validity on the UWB application. The measured frequency response of the input port corresponds quite well with the calculations and simulations. The radiation pattern of the implemented quad-notched UWB antenna is nearly omnidirectional in the passband.

Optimizing Concussion Care Seeking: Using Machine Learning to Predict Delayed Concussion Reporting.

BACKGROUND: Early medical attention after concussion may minimize symptom duration and burden; however, many concussions are undiagnosed or have a delay in diagnosis after injury. Many concussion symptoms (eg, headache, dizziness) are not visible, meaning that early identification is often contingent on individuals reporting their injury to medical staff. A fundamental understanding of the types and levels of factors that explain when concussions are reported can help identify promising directions for intervention. PURPOSE: To identify individual and institutional factors that predict immediate (vs delayed) injury reporting. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: This study was a secondary analysis of data from the Concussion Assessment, Research and Education (CARE) Consortium study. The sample included 3213 collegiate athletes and military service academy cadets who were diagnosed with a concussion during the study period. Participants were from 27 civilian institutions and 3 military institutions in the United States. Machine learning techniques were used to build models predicting who would report an injury immediately after a concussive event (measured by an athletic trainer denoting the injury as being reported "immediately" or "at a delay"), including both individual athlete/cadet and institutional characteristics. RESULTS: In the sample as a whole, combining individual factors enabled prediction of reporting immediacy, with mean accuracies between 55.8% and 62.6%, depending on classifier type and sample subset; adding institutional factors improved reporting prediction accuracies by 1 to 6 percentage points. At the individual level, injury-related altered mental status and loss of consciousness were most predictive of immediate reporting, which may be the result of observable signs leading to the injury report being externally mediated. At the institutional level, important attributes included athletic department annual revenue and ratio of athletes to athletic trainers. CONCLUSION: Further study is needed on the pathways through which institutional decisions about resource allocation, including decisions about sports medicine staffing, may contribute to reporting immediacy. More broadly, the relatively low accuracy of the machine learning models tested suggests the importance of continued expansion in how reporting is understood and facilitated.

A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia.

Cardiovascular diseases (CVDs) are the primary cause of death. Every year, many people die due to heart attacks. The electrocardiogram (ECG) signal plays a vital role in diagnosing CVDs. ECG signals provide us with information about the heartbeat. ECGs can detect cardiac arrhythmia. In this article, a novel deep-learning-based approach is proposed to classify ECG signals as normal and into sixteen arrhythmia classes. The ECG signal is preprocessed and converted into a 2D signal using continuous wavelet transform (CWT). The time-frequency domain representation of the CWT is given to the deep convolutional neural network (D-CNN) with an attention block to extract the spatial features vector (SFV). The attention block is proposed to capture global features. For dimensionality reduction in SFV, a novel clump of features (CoF) framework is proposed. The k-fold cross-validation is applied to obtain the reduced feature vector (RFV), and the RFV is given to the classifier to classify the arrhythmia class. The proposed framework achieves 99.84% accuracy with 100% sensitivity and 99.6% specificity. The proposed algorithm outperforms the state-of-the-art accuracy, F1-score, and sensitivity techniques.

Phthalocyanine analogues: unexpectedly facile access to non-peripherally substituted octaalkyl tetrabenzotriazaporphyrins, tetrabenzodiazaporphyrins, tetrabenzomonoazaporphyrins and tetrabenzoporphyrins.

Controlled syntheses of phthalocyanine/benzoporphyrin hybrid structures have been achieved. We report a simple means for obtaining non-peripherally octaalkyl-substituted derivatives of tetrabenzotriazaporphyrin (TBTAP), tetrabenzodiazaporphyrin (TBDAP), tetrabenzomonoazaporphyrin (TBMAP) and tetrabenzoporphyrin (TBP) macrocycles by treating 3,6-dialkyl phthalonitriles with differing amounts of the Grignard reagent MeMgBr. This range of macrocyclic products is not obtained from corresponding reactions of a Grignard reagent with 4-substituted phthalonitriles, reported previously, or reaction of MeMgBr with a 4,5-dialkyl phthalonitrile. Attempts to form a meso-substituted TBTAP from 3,6-dialkyl phthalonitriles by reaction with benzyl and long-chain alkyl Grignard reagents unexpectedly gave only the parent macrocycle unsubstituted at the meso position. The synthetic protocols are by far the most straightforward and convenient means to access these interesting, but scarcely studied, classes of material. The new series of substituted macrocyclic compounds, obtained as the metal-free and magnesium- and copper(II)-metallated derivatives, show trends in the UV/Vis spectra consistent with those predicted elsewhere by Kobayashi. Characterisation of the new families allows further trends to be identified as meso-nitrogen atoms are sequentially replaced by methine bridges, for example, the compounds provide novel examples of macrocyclic structures that show columnar mesophase behaviour. Single-crystal X-ray structure determinations have been obtained for three magnesium-metallated derivatives bearing eight hexyl substituents and constitute the first set of structural data obtained for such a series.

International Society of Nephrology Global Kidney Health Atlas: structures, organization, and services for the management of kidney failure in South Asia.

Information about disease burden and the available infrastructure and workforce to care for patients with kidney disease was collected for the second edition of the International Society of Nephrology Global Kidney Health Atlas. This paper presents findings for the 8 countries in the South Asia region. The World Bank categorizes Afghanistan and Nepal as low-income; Bangladesh, Bhutan, India, and Pakistan as lower-middle-income; and Sri Lanka and the Maldives as upper-middle-income countries. The prevalence of chronic kidney disease (CKD) in South Asia ranged from 5.01% to 13.24%. Long-term hemodialysis and long-term peritoneal dialysis are available in all countries, but Afghanistan lacks peritoneal dialysis services. Kidney transplantation was available in all countries except Bhutan and Maldives. Hemodialysis was the dominant modality of long-term dialysis, peritoneal dialysis was more expensive than hemodialysis, and kidney transplantation overwhelmingly depended on living donors. Bhutan provided public funding for kidney replacement therapy (dialysis and transplantation); Sri Lanka, India, Pakistan, and Bangladesh had variable funding mechanisms; and Afghanistan relied solely on out-of-pocket expenditure. There were shortages of health care personnel across the entire region. Reporting was variable: Afghanistan and Sri Lanka have dialysis registries but publish no reports, whereas Bangladesh has a transplant registry. South Asia has a large, but poorly documented burden of CKD. Diabetes and hypertension are the major causes of CKD throughout the region with a higher prevalence of infectious causes in Afghanistan and a high burden of CKD of an unknown cause in Sri Lanka and parts of India. The extent and quality of care delivery is suboptimal and variable. Sustainable strategies need to be developed to address the growing burden of CKD in the region.

Capturing and monitoring global differences in untreated and treated end-stage kidney disease, kidney replacement therapy modality, and outcomes.

A large gap between the number of people with end-stage kidney disease (ESKD) who received kidney replacement therapy (KRT) and those who needed it has been recently identified, and it is estimated that approximately one-half to three-quarters of all people with ESKD in the world may have died prematurely because they could not receive KRT. This estimate is aligned with a previous report that estimated that >3 million people in the world died each year because they could not access KRT. This review discusses the reasons for the differences in treated and untreated ESKD and KRT modalities and outcomes and presents strategies to close the global KRT gap by establishing robust health information systems to guide resource allocation to areas of need, inform KRT service planning, enable policy development, and monitor KRT health outcomes.

Supportive care for end-stage kidney disease: an integral part of kidney services across a range of income settings around the world.

A key component of treatment for all people with advanced kidney disease is supportive care, which aims to improve quality of life and can be provided alongside therapies intended to prolong life, such as dialysis. This article addresses the key considerations of supportive care as part of integrated end-stage kidney disease care, with particular attention paid to programs in low- and middle-income countries. Supportive care should be an integrated component of care for patients with advanced chronic kidney disease, patients receiving kidney replacement therapy (KRT), and patients receiving non-KRT conservative care. Five themes are identified: improving information on prognosis and support, developing context-specific evidence, establishing appropriate metrics for monitoring care, clearly communicating the role of supportive care, and integrating supportive care into existing health care infrastructures. This report explores some general aspects of these 5 domains, before exploring their consequences in 4 health care situations/settings: in people approaching end-stage kidney disease in high-income countries and in low- and middle-income countries, and in people discontinuing KRT in high-income countries and in low- and middle-income countries.

A remarkable side-product from the synthesis of an octaalkylphthalocyanine: formation of a tetrabenzotriazaporphyrin.

1,4,8,11,15,18,22,25-Octahexyl-29H,31H-tetrabenzo[b,g,l,q][5,10,15]triazaporphyrin has been isolated as the main side product in the preparation of 1,4,8,11,15,18,22,25-octahexylphthalocyanine via a lithium pentyloxide induced cyclotetramerisation of 3,6-dihexylphthalonitrile.