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This paper presents an analytical study of the stable throughput for multiple broadcast sessions in a multi-hop wireless tandem network with random access. Intermediate nodes leverage on the broadcast nature of wireless medium access to perform inter-session network coding among different flows. This problem is challenging due to the interaction among nodes, and has been addressed so far only in the saturated mode where all nodes always have packet to send, which results in infinite packet delay. In this paper, we provide a novel model based on multi-class queueing networks to investigate the problem in unsaturated mode. We devise a theoretical framework for computing maximum stable throughput of network coding for a slotted ALOHA-based random access system. Using our formulation, we compare the performance of network coding and traditional routing. Our results show that network coding leads to high throughput gain over traditional routing. We also define a new metric, network unbalance ratio (NUR), that indicates the unbalance status of the utilization factors at different nodes. We show that although the throughput gain of the network coding compared to the traditional routing decreases when the number of nodes tends to infinity, NUR of the former outperforms the latter. We carry out simulations to confirm our theoretical analysis. [ABSTRACT FROM PUBLISHER]

A novel focusing approach based on the least-squares and total least-squares methods for the localization of wideband signals is described. New focusing matrices that are constant under multiplication by their Hermitian transpose are utilized to prevent the focusing loss. It is shown that the proposed technique's computational complexity is significantly lower than that for the rotational signal-subspace algorithm. In addition, the focusing gain provided by the new approach is larger than the focusing gain of the rotational signal-subspace method. The new algorithm also has a smaller resolution signal-to-noise ratio.

Welcome to a Special Issue of the IEEE Canadian Journal of Electrical and Computer Engineering (IEEE-CJECE), which presents articles in the research areas of the Journal’s Former Area Editor, Dr. Sameh Sourour, in his memorial. Sameh Sorour was with the School of Computing at Queen’s University, Kingston, ON, Canada, where he was leading research in mobile edge computing, edge learning and autonomous vehicles with funding from federal, provincial and industry sources. He received his B.Sc. and M.Sc. degrees from Alexandria University, Alexandria, Egypt, in 2002 and 2006, respectively, and the Ph.D. from the University of Toronto, Toronto, ON, Canada, in 2011. His Ph.D. thesis was nominated for the Governor General’s Gold Medal Award. After his graduation, he held a MITACS industrial postdoctoral fellowship with Siradel Canada and the University of Toronto. Prior to moving to Queen’s University in 2019, he held another postdoctoral fellowship at the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, a Lecturer position at the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, and an Assistant Professor position at the University of Idaho, Moscow, ID, USA. During his Ph.D. degree and postdoctoral fellowships, he led several research projects with industrial partners and government agencies, such as LG Korea, the European Space Agency, the Canadian National Institute for the Blind (CNIB), and Siradel France. Dr. Sorour was a Senior Member of the IEEE and an Editor for IEEE Communications Letters. Also, he was an Area Editor in the IEEE-CJECE. His research and educational interests lied in the broad areas of advanced computing, learning, and networking technologies for cyber-physical and autonomous systems. The Guest Editors of this issue are 1) Prof. Hossam Hassanein, Director of School of Computing, Queen’s University, where Dr. Sorour held his last academic title; 2) Prof. Shahrokh Valaee, the Ph.D. advisor of Dr. Sourour at the University of Toronto. [ABSTRACT FROM AUTHOR]

An indoor positioning and navigation system based on measurements of received signal strength in wireless local area network is proposed. In the system, the location determination problem is solved by applying compressive sensing, which offers recovery of sparse signals from a small number of noisy measurements by solving an ℓ-minimization problem. The refined estimate is then used with a map-adaptive Kalman filter for real-time tracking. A navigation module integrated with the tracking system guides users to pre-defined destinations with voice instructions. Experimental results with a system that was implemented on a PDA shows that the proposed tracking system is lightweight so that it can be used on a resource constrained platform while outperforming the widely used traditional positioning and tracking systems. A pilot study was carried out with 30 visually impaired subjects from the Canadian National Institute for the Blind. Testing results show that the proposed system can be used to guide visually impaired subjects to their desired destinations with a very high success rate. [ABSTRACT FROM AUTHOR]

Time division multiple access (TDMA) based medium access control (MAC) protocols can provide QoS with guaranteed access to the wireless channel. However, in multi-hop wireless networks, these protocols may introduce scheduling delay if, on the same path, an outbound link on a router is scheduled to transmit before an inbound link on that router. The total scheduling delay can be quite large since it accumulates at every hop on a path. This paper presents a method that finds conflict-free TDMA schedules with minimum scheduling delay. We show that the scheduling delay can be interpreted as a cost, in terms of transmission order of the links, collected over a cycle in the conflict graph. We use this observation to formulate an optimization, which finds a transmission order with the min-max delay across a set of multiple paths. The min-max delay optimization is NP-complete since the transmission order of links is a vector of binary integer variables. We devise an algorithm that finds the transmission order with the minimum delay on overlay tree topologies and use it with a modified Bellman-Ford algorithm, to find minimum delay schedules in polynomial time. The simulation results in 802.16 mesh networks confirm that the proposed algorithm can find effective min-max delay schedules. [ABSTRACT FROM AUTHOR]

We study the problem of using path diversification to provide low probability of packet loss (PPL) in wireless networks. Path diversification uses erasure codes and multiple paths in the network to transmit packets. The source uses Forward Error Correction (FEC) to encode each packet into multiple fragments and transmits the fragments to the destination using multiple disjoint paths. The source uses a load balancing algorithm to determine how many fragments should be transmitted on each path. The destination can reconstruct the packet if it receives a number of fragments equal to or higher than the number of fragments in the original packet. We study the load balancing algorithm in two general cases. In the first case, we assume that no knowledge of the performance along the paths is available at the source. In such a case, the source decomposes traffic uniformly among the paths; we call this case blind load balancing. We show that for low PPL, blind load balancing outperforms single-path transmission. In the second case, we assume that a feedback mechanism periodically provides the source with information about the performance along each path. With that information, the source can optimally distribute the fragments. We show how to distribute the fragments for minimized PPL, and maximized efficiency given a bound on PPL. We evaluate the performance of the scheme through numerical simulations. [ABSTRACT FROM AUTHOR]

This paper develops a new network provisioning and resource allocation scheme. We introduce the concept of the effective burstiness curve (EBC), which is defined as a percentile of the maximum burstiness curve. For a fixed service rate, EBC represents the size of a buffer for which the probability of buffer overflow is arbitrarily small. We show that EBC is a convex nonincreasing function of the service rate. We also introduce the empirical effective burstiness curve (EEBC), an estimator of EBC, which can be obtained with a water-filling algorithm. For discrete queue size, EEBC can be evaluated with a recursive algorithm. The technique is applied to MPEG4 encoded video traces. [ABSTRACT FROM AUTHOR]

In this paper, we propose a link layer design for mobile hotspots. We design a novel system architecture that enables high- speed Internet access in railway systems. The proposed design uses a number of repeaters placed along the track and multiple antennas installed on the roof of a vehicle. Each packet is decomposed into smaller fragments and relayed to the vehicle via adjacent repeaters. We also use erasure coding to add parity fragments to original data. This approach is called in formation raining since fragments are rained upon the vehicle from adjacent repeaters. We investigate two instances of information raining. In blind information raining, all repeaters awaken when they sense the presence of the vehicle. The fragments are then blindly transmitted via awakened repeaters. A vehicle station installed inside the train is responsible for aggregating a large enough number of fragments. In the throughput-optimized information raining, the vehicle station selects a bipartite matching between repeaters and roof-top antennas and activates only a subset of the repeaters. It also dictates the amount of transmission power of each activated repeater. Both the bipartite matching and power allocations are individually shown to be NP-complete. Matching heuristics based on the Hungarian algorithm and Gale-Shapley algorithm are proposed. A simplex-type algorithm is proposed as the power allocation heuristics. [ABSTRACT FROM AUTHOR]

In this letter, we propose a new robust MUD, called a detector, for non-Gaussian noise. We consider the Gaussian-mixture model for non-Gaussian or impulsive noise. Our technique outperforms the decorrelator and the minimax detectors in highly impulsive noise. The proposed method uses a parametric cost function, where the parameter α is selected using the difference between the asymptotic variance of estimation error of the a detector and that of the minimax detector. [ABSTRACT FROM AUTHOR]

Abstract: The contribution of this paper is two-fold. First, we introduce a generalized principal component (GPC) beamforming technique for reduced rank processing that allows a trade-off between interference and noise reduction via the introduction of a control parameter, . Three variants of the GPC beamformer corresponding to (called T1 beamformer), (called T2 beamformer) and (called T3 beamformer), which maximize the signal-to-interference ratio, the signal-to-interference plus noise ratio, and the signal-to-noise ratio at the array output respectively, are considered in detail. The second contribution of this paper is to compare the robustness between the reduced rank and full rank beamformers. We use analytical studies and computer simulations to show that the T2 and T3 beamformers are robust against calibration and/or pointing errors. [Copyright &y& Elsevier]

In this paper, a new information theoretic algorithm is proposed for signal enumeration in array processing. The approach is based on predictive description length (PDL) that is defined as the length of a predictive code for the set of observations. We assume that several models, with each model representing a certain number of sources, will compete. The PDL criterion is computed for the candidate models and is minimized over all models to select the best model and to determine the number of signals. In the proposed method, the correlation matrix is decomposed into two orthogonal components in the signal and noise subspaces. The maximum likelihood (ML) estimates of the angles-of-arrival are used to find the projection of the sample correlation matrix onto the signal and noise subspaces. The summation of the ML estimates of these matrices is the ML estimate of the correlation matrix. This method can detect both coherent and noncoherent signals. The proposed method can be used online and can be applied to time-varying systems and target tracking. [ABSTRACT FROM AUTHOR]

In this paper, a new algorithm for parametric localization of multiple incoherently distributed sources is presented. This algorithm is based on an approximation of the array covariance matrix using central and noncentral moments of the source angular power densities. Based on this approximation, a new computationally simple covariance fitting-based technique is proposed to estimate these moments. Then, the source parameters are obtained from the moment estimates. Compared with earlier algorithms, our technique has lower computational cost and obtains the parameter estimates in a closed form. In addition, it can be applied to scenarios with multiple sources that may have different angular power densities, while other known methods are not applicable to such scenarios. [ABSTRACT FROM AUTHOR]

Presents a study which proposed an algorithm for the estimation of central angle and angular extension of distributed sources in array signal processing. Data model; Description of the distributed source parameter estimator algorithm for both coherently distributed and incoherently distributed source models; Simulation results and conclusion.

An introduction is presented in which the editor discusses various reports within the issue on topics including a comprehensive tutorial on localization, anonymous indoor navigation system on handheld mobile devices for the blind, and localization for wireless video capsule endoscopy.

The popularity of online social networks (OSNs) makes them attractive platforms to advertise products. Previous work on marketing in OSNs utilized older diffusion models that do not capture the interactions of modern OSNs and hence there is a need to develop a model that accounts for the interactions that occur in current OSNs. In this paper, we introduce a new model for information flow in online social networks that captures the sharing behavior exercised by users when they pass information from one online social network to their social circles in another network. We, then, formulate a problem of maximizing the marketing reach where the diversity of users' other social networks is taken as a constraint. We also propose a greedy algorithm to solve the aforementioned optimization problem. Numerical results show that the proposed algorithm achieves better results than algorithms that are based on classical degree centrality metric and with comparable running time. [ABSTRACT FROM AUTHOR]

We propose a new collaborative beamforming (CB) solution robust (i.e., RCB) against major channel estimation impairments over dual-hop transmissions through a wireless sensor network (WSN) of K nodes. The source first sends its signal to the WSN. Then, each node forwards its received signal after multiplying it by a properly selected beamforming weight. The latter aims to minimize the received noise power while maintaining the desired power equal to unity. These weights depend on some channel state information (CSI) parameters. Hence, they have to be estimated locally at each node, thereby, resulting in channel estimation errors that could severely hinder CB performance. Exploiting an efficient asymptotic approximation at large K, we develop alternative RCB solutions that adapt to different implementation scenarios and wireless propagation environments ranging from monochromatic (i.e., scattering-free) to polychromatic (i.e., scattered) ones. Besides, in contrast to existing techniques, our new RCB solutions are distributed (i.e., DCB) in that they do not require any information exchange among nodes, thereby dramatically improving both WSN spectral and power efficiencies. Simulation results confirm that the proposed robust DCB (RDCB) techniques are much more robust in terms of achieved signal-to-noise ratio (SNR) against channel estimation errors than best representative CB benchmarks. [ABSTRACT FROM AUTHOR]

In this paper, a novel indoor localization system with channel state information (CSI) fingerprints is proposed, which learns the spatial and frequency features of CSI in the fifth-generation (5G) cellular network by a Siamese convolution neural network. In particular, considering that the CSI continuously collected by a moving target possesses the implicit spatial association, we locate the target by the successive CSI data gathered within a time interval which can be regarded as an information subspace of the fingerprint database. Therefore, the fingerprint localization can be modeled as a subspace matching problem and solved by the Siamese network-based similarity learning. In the proposed system, we design a structure of CSI fingerprint which includes the information from multiple base stations in spatial and frequency domains. Then, the proposed Siamese architecture extracts the CSI feature and estimates the location of the target by feature similarity comparison. Compared with the existing algorithms, it can increase the positioning accuracy significantly by the feature relevance among the CSI data collected at different positions. The field tests indicate that compared to other CSI fingerprint-based positioning methods, our proposed algorithm can effectively reduce the localization error. [ABSTRACT FROM AUTHOR]

The access point (AP) deployment is a fundamental task for constructing an accurate localization system. Existing literature mainly deals with the AP placement problem using optimal geometry analysis since the target-AP geometry will affect the localization performance. However, some non-ideal phenomena in practical scenario, e.g., the existence of obstacles, array orientation and path loss, will degrade the accuracy of angle-of-arrival (AoA) estimation as well as the localization accuracy. In this article, we reformulate the AP planning incorporating these factors. We decompose the problem into two subproblems, namely AP selection problem and error minimization problem. The AP selection problem selects the minimum number of APs to satisfy a desired localization accuracy, aided by a refined orientation updating procedure. We design a centralized and a distributed error minimization algorithm to further decrease the localization error. The centralized algorithm shows superiority in time efficiency. Nevertheless, the case with large number of APs may lead to excessive computational cost. Accordingly, we further devise the distributed algorithm which is adaptive to large-scale deployment. Numerical studies in indoor environments with barriers are conducted to verify our proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, amplify-and-forward beamforming (AFB) is considered to establish a communication, through wireless sensor networks (WSNs) of $K$ sensor nodes, from a source to a receiver in the presence of both scattering and interference. All sources send their data to the WSN during the first time slot, while the nodes forward a properly weighted version of their received signals during the second slot. These weights are properly selected to maximize the desired power while completely canceling the interference signals. We show, however, that they depend on information locally unavailable at each node, making the zero-forcing beamformer (ZFB) unsuitable for WSNs, due to the prohibitive data exchange overhead and the power depletion it would require. To address this issue, we exploit the asymptotic expression at large $K$ of the ZFB weights that is locally computable at every node and, further, well-approximates their original counterparts. The performance of the resulting new distributed ZFB (DZFB) version is analyzed and compared with the conventional ZFB and two other distributed AFB benchmarks: the monochromatic (i.e., single-ray) AFB whose design neglects the presence of scattering and the bichromatic AFB which relies on an efficient two-ray channel approximation valid only for low angular spread (AS). We show that the proposed DZFB outperforms its monochromatic and bichromate counterparts while incurring much less overhead and power depletion than ZFB. We show also that it is able to provide optimal performance even in highly scattered environments as in the latter. [ABSTRACT FROM AUTHOR]

Owing to the multiple antennas equipped at modern Wi-Fi infrastructures, the angle-of-arrival (AoA) based indoor localization systems have successfully achieved the accuracy of tens of centimeters. However, the high accuracy is acquired at the cost of employing the additional resources in the domains of frequency, space or time, which requires complex processing and hinders the practical application. In this paper, we present the design and implementation of RcLoc, which takes full advantages of the flexible array orientations and receiver positions, based on limited resources. Particularly, RcLoc devises a receiver configuration scheme for guiding the system deployment. Optimized array orientation could effectively improve the AoA estimation accuracy and well-designed receiver positions contribute to the Cramer-Rao lower bound of localization error. In the stage of system realization, we further devise an array calibration method to calibrate the actual array and develop an improved AoA estimation algorithm, which make RcLoc robust to the array arrangement. We prototype RcLoc on commodity Wi-Fi devices without manual intervention. Comprehensive experiments in a multipath-rich indoor environment show that RcLoc achieves a median localization accuracy of 0.4 m, which provides useful insights for receiver deployment. [ABSTRACT FROM AUTHOR]

Medical datasets are often highly imbalanced with over-representation of prevalent conditions and poor representation of rare medical conditions. Due to privacy concerns, it is challenging to aggregate large datasets between health care institutions. We propose synthesizing pathology in medical images as a means to overcome these challenges. We implement a deep convolutional generative adversarial network (DCGAN) to create synthesized chest X-rays based upon a modest sized labeled dataset. We used a combination of real and synthesized images to train deep convolutional neural networks (DCNNs) to detect pathology across five classes of chest X-rays. The comparative study of DCNNs trained with the combination of real and synthesized images showed that these networks can outperform similar networks trained solely with real images in pathology classification. This improved performance is largely attributable to the balancing of the dataset using DCGAN synthesized images, where classes that are lacking in example images are preferentially augmented. [ABSTRACT FROM AUTHOR]

One of the most challenging issues in Mobile Social Networks (MSNs) is to design a messages forwarding method that achieves high delivery and low communication overhead. Single-copy forwarding methods boasting minimum communication overhead are well-known in MSNs. However, attaining a reasonable delivery ratio by using a single-copy method is an open problem. A common way to resolve the problem is social-aware forwarding, but this faces two main weaknesses: one is that they either are unaware of community detection, or use supervised learning strategies, other is that they generally use the relay-destination contact probability for predicting future contacts without considering the contact time. In this paper, we propose community-aware forwarding (CAF) as a new single-copy forwarding method using Hidden Semi-Markov Model (HSMM) to find communities by utilizing the similarity of node contact patterns in different sojourn cycles. In this study, we train HSMM as an unsupervised algorithm to compute the node community transition and then propose a novel forwarding method that utilizes message expiration in order to make relay selection. Evaluation results confirm our superior CAF performance over the popular solutions investigated in terms of message delivery and latency. [ABSTRACT FROM AUTHOR]

Rationale: Spirometry and plethysmography are the gold standard pulmonary function tests (PFT) for diagnosis and management of lung disease. Due to the inaccessibility of plethysmography, spirometry is often used alone but this leads to missed or misdiagnoses as spirometry cannot identify restrictive disease without plethysmography. We aimed to develop a deep learning model to improve interpretation of spirometry alone., Methods: We built a multilayer perceptron model using full PFTs from 748 patients, interpreted according to international guidelines. Inputs included spirometry (forced vital capacity, forced expiratory volume in 1 s, forced mid-expiratory flow 25-75 ), plethysmography (total lung capacity, residual volume) and biometrics (sex, age, height). The model was developed with 2582 PFTs from 477 patients, randomly divided into training (80%), validation (10%) and test (10%) sets, and refined using 1245 previously unseen PFTs from 271 patients, split 50/50 as validation (136 patients) and test (135 patients) sets. Only one test per patient was used for each of 10 experiments conducted for each input combination. The final model was compared with interpretation of 82 spirometry tests by 6 trained pulmonologists and a decision tree., Results: Accuracies from the first 477 patients were similar when inputs included biometrics+spirometry+plethysmography (95%±3%) vs biometrics+spirometry (90%±2%). Model refinement with the next 271 patients improved accuracies with biometrics+pirometry (95%±2%) but no change for biometrics+spirometry+plethysmography (95%±2%). The final model significantly outperformed (94.67%±2.63%, p<0.01 for both) interpretation of 82 spirometry tests by the decision tree (75.61%±0.00%) and pulmonologists (66.67%±14.63%)., Conclusions: Deep learning improves the diagnostic acumen of spirometry and classifies lung physiology better than pulmonologists with accuracies comparable to full PFTs., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Dropout is a well-known regularization method by sampling a sub-network from a larger deep neural network and training different sub-networks on different subsets of the data. Inspired by the dropout concept, we propose EDropout as an energy-based framework for pruning neural networks in classification tasks. In this approach, a set of binary pruning state vectors (population) represents a set of corresponding sub-networks from an arbitrary original neural network. An energy loss function assigns a scalar energy loss value to each pruning state. The energy-based model (EBM) stochastically evolves the population to find states with lower energy loss. The best pruning state is then selected and applied to the original network. Similar to dropout, the kept weights are updated using backpropagation in a probabilistic model. The EBM again searches for better pruning states and the cycle continuous. This procedure is a switching between the energy model, which manages the pruning states, and the probabilistic model, which updates the kept weights, in each iteration. The population can dynamically converge to a pruning state. This can be interpreted as dropout leading to pruning the network. From an implementation perspective, unlike most of the pruning methods, EDropout can prune neural networks without manually modifying the network architecture code. We have evaluated the proposed method on different flavors of ResNets, AlexNet, l 1 pruning, ThinNet, ChannelNet, and SqueezeNet on the Kuzushiji, Fashion, CIFAR-10, CIFAR-100, Flowers, and ImageNet data sets, and compared the pruning rate and classification performance of the models. The networks trained with EDropout on average achieved a pruning rate of more than 50% of the trainable parameters with approximately < 5% and < 1% drop of Top-1 and Top-5 classification accuracy, respectively.

This paper deals with the problem of multicast/broadcast throughput in multi-channel multi-radio wireless mesh networks that suffer from the resource constraints. We provide a formulation to capture the utilization of the network resources and derive analytical relationships for the network's throughput in terms of the node utilization, the channel utilization, and the number of transmissions. Our model relies on the on-demand quality of service multicast/broadcast sessions, where each admitted session creates a unique tree with a specific bandwidth. As an advantage, the derived relationships are independent of the type of tree built for each session and can be used for different protocols. The proposed formulation considers the channel assignment strategy and reflects both the wireless broadcast advantage and the interference constraint. We also offer a comprehensive discussion to evaluate the effects of load-balancing and number of transmissions on the network's throughput. Numerical results confirm the accuracy of the presented analysis.