QoS Analysis Models for Wireless Networks

Guaranteeing of QoS over wireless networks is a very challenging task. The first and critical step in addressing this problem is to build up a QoS analysis model which accurately characterizes the fading channel and time-varying capacity of the link, by quantifying parameters like Bit Error Rate (BER) or delay to protocols of higher layers. This paper presents a study of QoS models from a physical and data link layer point of view, more specifically, we introduce the Finite State Markov Chain (FSMC) model for fading channels at the physical layer and the Effective Capacity (EC) model at the data link layer. The FSMC can provide us with a quick estimate of current channel conditions like BER, while it is very hard using the physical channel model to get parameters like delay which needs an analysis of queueing behaviors; for that reason, a link-layer model – EC which provides a statistical QoS model on the unacceptable delay performance (i.e., the probability of delay exceeds a bound) is then studied. The characteristics, advantages and disadvantages of these two models will be studied and compared in this paper

Deep Learning to Quantify Pulmonary Edema in Chest Radiographs

Purpose: To develop a machine learning model to classify the severity grades of pulmonary edema on chest radiographs. Materials and Methods: In this retrospective study, 369,071 chest radiographs and associated radiology reports from 64,581 (mean age, 51.71; 54.51% women) patients from the MIMIC-CXR chest radiograph dataset were included. This dataset was split into patients with and without congestive heart failure (CHF). Pulmonary edema severity labels from the associated radiology reports were extracted from patients with CHF as four different ordinal levels: 0, no edema; 1, vascular congestion; 2, interstitial edema; and 3, alveolar edema. Deep learning models were developed using two approaches: a semi-supervised model using a variational autoencoder and a pre-trained supervised learning model using a dense neural network. Receiver operating characteristic curve analysis was performed on both models. Results: The area under the receiver operating characteristic curve (AUC) for differentiating alveolar edema from no edema was 0.99 for the semi-supervised model and 0.87 for the pre-trained models. Performance of the algorithm was inversely related to the difficulty in categorizing milder states of pulmonary edema (shown as AUCs for semi-supervised model and pre-trained model, respectively): 2 versus 0, 0.88 and 0.81; 1 versus 0, 0.79 and 0.66; 3 versus 1, 0.93 and 0.82; 2 versus 1, 0.69 and 0.73; and, 3 versus 2, 0.88 and 0.63. Conclusion: Deep learning models were trained on a large chest radiograph dataset and could grade the severity of pulmonary edema on chest radiographs with high performance.Comment: The two first authors contributed equall

Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment

We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiographs and free-text radiology reports exist, only limited numerical edema severity labels can be extracted from radiology reports. This is a significant challenge in learning such models for image classification. To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time. Our experimental results suggest that the joint image-text representation learning improves the performance of pulmonary edema assessment compared to a supervised model trained on images only. We also show the use of the text for explaining the image classification by the joint model. To the best of our knowledge, our approach is the first to leverage free-text radiology reports for improving the image model performance in this application. Our code is available at https://github.com/RayRuizhiLiao/joint_chestxray.Comment: The two first authors contributed equally. To be published in the proceedings of MICCAI 202

Detecting on-street parking spaces in smart cities: Performance evaluation of fixed and mobile sensing systems

As the number of vehicles continues to grow, parking spaces are at a premium in city streets. In addition, due to the lack of knowledge about street parking spaces, heuristic circling in the streets not only costs drivers’ time and fuel, but also increases city congestion. In the wake of the recent trend to build convenient, green and energy-efficient smart cities, common techniques adopted by high-profile smart parking systems are reviewed, and the performance of the various approaches are compared. A mobile sensing unit has been developed as an alternative to the fixed sensing approach. It is mounted on the passenger side of a car to measure the distance from the vehicle to the nearest roadside obstacle. By extracting parked vehicles’ features from the collected trace, a supervised learning algorithm has been developed to estimate roadside parking occupancy. Multiple road tests were conducted around Wheatley (Oxfordshire) and Guildford (Surrey) in the UK. In the case of accurate GPS readings, enhanced by a map matching technique, the accuracy of the system is above 90%. A quantity estimation model is derived to calculate the density of sensing units required to cover urban streets. The estimation is quantitatively compared to a fixed sensing solution. The results show that the mobile sensing approach can perform at the same level as fixed sensing solutions when accurate location information is available but substantially fewer sensors are needed compared to the fixed sensing system

MAC design and analysis for MU-MIMO and full-duplex enabled wireless networks

IEEE 802.11 based wireless networks, especially the infrastructure based Wireless Local Area Networks (WLANs) or Wi-Fi networks, are hugely successful, and have become an indispensable part of our life at homes and working places. However, the rapid growth of wireless devices, and a shift of users' habit from web browsing to a wide variety of bandwidth-hungry applications (e.g., high-definition video, social network and cloud uploading), have made today's WLANs not only crowded, but also low at throughput. The latest IEEE WLAN amendment-802.11ac responds to these challenges by introducing a set of novel physical (PHY) and medium access control (MAC) features, including downlink multi-user Multiple-Input and Multiple-Output (MU-MIMO), higher modulation and coding scheme, channel bonding, frame aggregation, etc. Among them, downlink MU-MIMO is one of the most important features due to its potential to significantly improve the performance. It allows the access point (AP) to transmit frames to multiple stations (STAs) in parallel, which can substantially increase the network throughput, as well as to mitigate high collision rates. On the other side, recent research advances on the full-duplex (FD) transmission open another line to improve the performance of wireless network in dense areas. FD transmissions in wireless networks break the long-hold assumption that nodes can not transmit and receive simultaneously using the same frequency. This exciting progress not only promises a twofold capacity increase by allowing concurrent transmission and reception, but could also bring revolutionary changes to the MAC design by shaking the foundation of IEEE 802.11 medium access mechanism-Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). As CSMA/CA can become obsolete, and be replaced by the on-the-fly collision detection thanks to the FD's capability of simultaneous transmission and reception. However, these two lines of PHY advances (i.e., MU-MIMO and FD) do not directly translate to the useful performance gains at the MAC layer (e.g., the throughput increase). In order to take full benefits of MU-MIMO and FD, adaptations of the current IEEE 802.11 MAC protocols are needed. In this thesis, we first investigate the prominent MAC proposals in the literature, and then propose the required MAC adaptations to support MU-MIMO downlink and uplink transmissions separately. After that, we combine the downlink and uplink adaptations into a unified MU-MIMO MAC protocol to look into what influence would bring to the uplink or the downlink when both up/down-link traffic are present. We extensively evaluate the performance of the proposed protocol in saturated and non-saturated conditions to emulate the highly-densed scenario and the lightly-increased traffic scenario. Next, we model and evaluate the new IEEE 802.11ac standard MAC operations, and compare them with our proposals in a wireless mesh backhaul network. Afterwards, we utilize full-duplex technique and propose a full-duplex MAC (FD+) scheme to improve the performance of a wireless system. We analytically model FD transmissions and investigate what gains can be achieved at the MAC layer. Finally, we conclude our work and look into the future directions. Results from simulations and analytic models show that the significant performance gain can be achieved by extending traditional IEEE 802.11 MAC schemes to support MU-MIMO and FD transmissions.Les xarxes sense fils basades en l'estàndard IEEE 802.11, sobretot aquelles que funcionen en mode infraestructura, conegudes com a xarxes WLAN o Wi-Fi, han tingut molt d'èxit i s'han convertit en una part indispensable de la nostra vida tant a les llars com als llocs de treball. No obstant això, el ràpid creixement dels dispositius sense fils, i un canvi d'hàbits dels usuaris des de la navegació web cap a una àmplia varietat d'aplicacions que requereixen un gran ample de banda (per exemple, vídeo d'alta definició, les xarxes socials o la càrrega de continguts al núvol), han fet que les WLAN d'avui en dia tinguin un baix rendiment. La pròxima generació de WLANs, basades en el nou estàndard IEEE 802.11ac, respon a aquests reptes mitjançant la introducció d'una sèrie de novetats a nivell físic (PHY) i a nivell d'accés al medi (MAC), incloent l'enllaç de baixada Multi-user Multiple-Input Multiple-Output (MU-MIMO), un nou esquema de modulació i codificació, canals amb major amplada de banda i l'agregació de trames. Entre ells, l'enllaç de baixada MU-MIMO és una de les novetats més importants per a millorar significativament el rendiment de les xarxes WLAN, ja que permet que el punt d'accés (AP) pugui transmetre trames a múltiples estacions (STA) en paral•lel, cosa que pot augmentar substancialment el rendiment de la xarxa, així com mitigar les altes taxes de col•lisió. D'altra banda, els avenços recents en investigació sobre la transmissió full-duplex (FD) obren una altra línia per millorar el rendiment de les xarxes WLAN amb molts usuaris. Transmissions full-duplex en xarxes sense fils trenquen la suposició que diu que els nodes no poden transmetre i rebre simultàniament usant la mateixa freqüència. Aquest emocionant avenç no només promet un augment de capacitat en permetre la transmissió i recepció simultània, sinó que també podria portar canvis revolucionaris en el disseny MAC sacsejant els fonaments del mecanisme Carrier Sense Multiple Access amb prevenció de col•lisions (CSMA/CA). Així, el CSMA/CA pot arribar a ser obsolet i ser reemplaçat per un sistema de detecció de col•lisions gràcies a la capacitat de transmissió i recepció simultània dels nodes que suportin full-duplex. No obstant això, aquestes dues línies d'avenços PHY (és a dir, MU-MIMO i FD) no es tradueixen directament en guanys de rendiment útils a la capa MAC (per exemple, l'augment de rendiment). Per tal de tenir beneficis per fer servir MU-MIMO i FD, es necessita adaptar els actuals protocols MAC. En aquesta tesi, primer examinem les propostes de protocols MAC existents a la literatura i, a continuació, mostrem els nostres esforços en la introducció de les adaptacions necessàries per donar suport MAC MU-MIMO a l'enllaç descendent i a l'enllaç ascendent per separat. Després d'això, combinem les adaptacions per a l'enllaç descendent i ascendent en un protocol MU-MIMO MAC unificat i avaluem el seu rendiment tant en condicions saturades com no saturades. A continuació, anem a modelar i avaluar el funcionament del nou IEEE 802.11ac MAC, i els comparem amb les nostres propostes en una xarxa mallada sense fils (xarxa back-haul). A continuació, es proposa un MAC que suporta full-duplex (FD+) per millorar les prestacions dels sistemes sense fils, on analíticament modelem transmissions FD i investiguem quins guanys es poden aconseguir a la capa MAC. Finalment, presentem les conclusions del nostre treball i plantegem les direccions futures en aquest camp de recerca

Multimodal Representation Learning for Medical Image Analysis

My thesis develops machine learning methods that exploit multimodal clinical data to improve medical image analysis. Medical images capture rich information of a patient’s physiological and disease status, central in clinical practice and research. Computational models, such as artificial neural networks, enable automatic and quantitative medical image analysis, which may offer timely diagnosis in low-resource settings, advance precision medicine, and facilitate large-scale clinical research. Developing such image models demands large training data. Although digital medical images have become increasingly available, limited structured image labels for the image model training have remained a bottleneck. To overcome this challenge, I have built machine learning algorithms for medical image model development by exploiting other clinical data. Clinical data is often multimodal, including images, text (e.g., radiology reports, clinical notes), and numerical signals (e.g., vital signs, laboratory measurements). These multimodal sources of information reflect different yet correlated manifestations of a subject’s underlying physiological processes. I propose machine learning methods that take advantage of the correlations between medical images and other clinical data to yield accurate computer vision models. I use mutual information to capture the correlations and develop novel algorithms for multimodal representation learning by leveraging local data features. The experiments described in this thesis demonstrate the advances of the multimodal learning approaches in the application of chest x-ray analysis.Ph.D