Maximizing Cooperative Diversity Energy Gain for Wireless Networks

Recent advancement in the field of wireless sensor networks (WSNs) has enabled its use in a variety of multimedia applications where the data to be handled are large that require more memory for storage and high bandwidth for transmission. As WSNs have limited capabilities in terms of computation, memory, energy and bandwidth, compression becomes necessary. The traditional compression methods consume more energy as well as memory which can be overcome by compressive sensing (CS) technique. CS is an emerging technique for efficiently acquiring and reconstructing the signal by processing the reduced number of samples specified by the Nyquist criterion. The objective of this paper is to implement CS for images using the proposed sensing matrix derived from the Toeplitz matrix and its variants. For reconstruction purpose, an existing greedy orthogonal matching pursuit algorithm is used. The measurements obtained from the framework are transmitted in real time using TelosB nodes under Contiki OS platform. The simulation results are compared with the experimental results, and the performance of the CS framework is evaluated in terms of peak signal-to-noise ratio, storage overhead, energy computation, computational time, transmission energy and end-to-end transmission latency. The results show that the performance of the proposed sensing matrix is better in terms of memory requirement, energy computation and computational complexity when compared with an existing Gaussian matrix.

In the Spark distributed framework, data communication problems (network transfer overhead, network IO bottlenecks) caused by data transfer across nodes/racks are a common cause of performance degradation due to the inconsistency between the task execution location and the data location. Additionally, in heterogeneous environments, Spark's task scheduling strategy cannot fully utilize the advantages of high‐performance nodes. To address the above issues, firstly, this paper proposes a cost‐aware task selection strategy. The strategy models the cost of tasks by considering the impact of data locality and heterogeneous factors on the efficiency of job execution. For scenarios where data locality needs to be reduced for scheduling tasks, the task scheduling problem is transformed into a minimum weighted bipartite graph matching problem, and a greedy matching algorithm is used to solve for the minimum processing cost option. For scenarios that maintain the current data localization level for scheduling tasks, select the task execution with the largest change in task processing cost due to data localization changes. Secondly, the problem is that Spark's delay scheduling algorithm causes resources in the cluster to be in an unnecessary waiting state and reduces cluster resource utilization. In this paper, we propose an adaptive adjustment strategy for delay waiting time based on benefit assessment. This policy improves the resource utilization of the cluster by evaluating the benefit of delay waiting of the scheduler and dynamically adjusting the delay time based on the result of the evaluation. Finally, we implement the proposed strategy in Spark 3.0.0 and evaluate its performance using some representative benchmarks. The experimental results show that, compared with other task scheduling algorithms, the strategy proposed in this paper can effectively improve the execution efficiency of jobs, reduce the execution time of jobs by 15.8%–31.9%, and at the same time reduce the network traffic and improve the CPU utilization.

Background Patient satisfaction and experience with care is being used as a surrogate marker of quality and value of healthcare delivery. No study has evaluated the association of patient satisfaction with healthcare utilization and cost, and quality of life in RA patients. Objectives To examine the association of RA patient satisfaction with healthcare utilization and cost, and quality of life. Methods 2010-2015 longitudinal files from the Medical Expenditure Panel Survey (MEPS), a nationally representative survey of the US civilian non-institutionalized population were used to identify patients with self-reported RA diagnosis. MEPS has overlapping panel design with participants interviewed up to 5 rounds over a 2- year period. In rounds 2 and 4, patient satisfaction is assessed by the four items about physician interaction – listened carefully, explained things well, showed respect, and spent enough time; and one item for overall satisfaction with quality of care received. A patient satisfaction score was constructed from standardized composite score of these items, and classified as most satisfied (patient satisfaction quartile 4) and less satisfied (patient satisfaction quartiles 1-3). The outcome measures were assessed in year 2: healthcare utilization (any emergency department visit, any inpatient stay, number of visits to office-based providers, outpatient department, and prescription drugs); total healthcare expenditures (measured in 2015 dollars), and quality of life (QoL, measure using 12-item Short Form healthy survey (SF-12)). Two statistical approaches were used: a standard logistic regression comparing most satisfied with less satisfied, and propensity score matching. Propensity scores were estimated using a logistic regression to model most satisfied quartile (vs. lower) as a function of covariates (satisfaction score in year 1 were matched 1 to 1 without replacement using a greedy matching algorithm. After matching, means of outcomes in year 2 were compared among matched pairs. Standard errors were estimated using bootstrapping method with 200 repetitions. Covariates included socio-demographic measures, health behaviors, health status (Charlson Comorbidity Index, general health, activity limitation, and pain affecting activities); RA measures (DMARDs, corticosteroids, and analgesics use); and the Year 1 outcome measures (utilization, total expenditure, QoL). Results 568 patients had a self-reported RA diagnosis. 1025 (65.4%) patients were analyzed (326 patients excluded for missing satisfaction response; 201 patients for missing information on other study variables; and 16 for not having data in all 5 rounds). The results using the propensity score matching approach are presented in the Table (202 most satisfied matched to 202 less satisfied patients). There were no differences in the healthcare utilization, cost and QoL among the two groups. Logistic regression approach yielded similar results. Conclusion Patient satisfaction score of RA patients was not associated with healthcare utilization and cost, and quality of life measures. Reference [1] Fenton JJ et al. Arch Intern Med. 2012;172(5):405-411 Disclosure of Interests None declared

PND124 INFLUENCE OF COMORBIDITIES ON HEALTHCARE EXPENDITURES AND PERCEIVED PHYSICAL AND MENTAL HEALTH STATUS AMONG ADULTS WITH MULTIPLE SCLEROSIS: A PROPENSITY SCORE MATCHED US NATIONAL-LEVEL STUDY

Background The current literature suggests that IBD patients have worse outcomes after emergency surgery when compared to elective surgery. We hypothesized that this association will be lost after matching cohorts of emergency and elective patients. We aimed to confirm the current emergency IBD surgery literature in a large cohort of unmatched IBD patients. We then aimed to determine if postoperative outcomes are worse in matched patients undergoing elective vs emergency colectomy. Methods We utilized the NSQIP PUF IBD Cohort 2005–2019 to assess 30-day outcomes in adult IBD patients undergoing emergency vs elective colectomy. Our primary outcome was any complication. Our secondary endpoints were readmission, reoperation, and mortality. Univariable and multivariable logistic regression models were used to examine the association between emergency surgery and complications in the unmatched cohort. Propensity-score matching was performed to match emergency patients to non-emergency patients on a 1:1 basis. A logistic regression model was used to assign a propensity score to each patient using emergency surgery as the outcome. All preoperative and perioperative characteristics were included in the model except those with > 20% missing data. The probability of emergency surgery based on these factors was used as the propensity score. A greedy matching algorithm was used to find the best controls matches for each case. A Love plot was used to show the covariate balance. Conditional logistic regression were used to evaluate the association between outcomes and emergency surgery on the matched sample. Results A total of 16,790 patients (15,849 elective, 941 emergency) were included in the unmatched cohort analysis. 30-day outcomes in 21 categories including length of stay, surgical site infection, sepsis, death and cardiovascular, renal and thrombotic complications were worse in the emergency cohort (Table 1). After matching, 632 sets were constructed. The majority of adverse outcomes demonstrated in the unmatched cohort lost significance. Only unplanned intubation (OR 2.6, p=.006), >48 hours on a ventilator (OR 2.8, p<.001), septic shock (OR 3.6, p<.001) and any complication (OR 1.3, p=.03) retained significance (Table 2). Conclusion In concordance with the current literature, we demonstrated increased rates of postoperative infection, cardiovascular and renal complications, reoperation and length of stay in unmatched groups of IBD patients undergoing emergency vs elective surgery. However, the majority of these complications were not significantly different after propensity matching. This suggests that emergency surgery is not associated with poorer outcomes in terms of length of stay, mortality and most morbidities as previously thought.

Introduction: Patients with bicuspid aortic valves (BAV) and tricuspid aortic valves (TAV) with ascending aortic aneurysms (AAA) are at risk for aortic complications. Meticulous follow-up and assessment of risk factors can mitigate the adverse aortic events. Hypothesis: We hypothesized that BAV and TAV with AAA can be managed conservatively with acceptable survival, low aortic complication rate, and timely surgery. Methods: From 2016 to 2018, 188 patients were seen for an isolated AAA. 147 patients had two or more CT scans (631-patient-years) which allowed measurement of aortic growth. We used a 1:1 greedy matching algorithm in R to identify similar cohorts of BAV and TAV patients matched by age, sex, hypertension, family history of aortic disease, coronary artery disease, and hyperlipidemia. Univariate and multivariate analysis of the unmatched cohorts were performed. Echocardiogram data was evaluated for each patient. Results: Initial mean AAA diameter was 4.3±0.58 cm (Table 1). The mean time between scans was 4.2 ± 3.3 years. 20 patients had an ascending aortic aneurysm ≥ 5cm with mean diameter 5.2 ± 0.23 cm (range 5.0-6.0 cm). Five patients had an aortic event (1 BAV and 4 TAV). Three of these aortic events lead to patient deaths (1 BAV and 2 TAV), in addition to one death (TAV) unrelated to aortic pathology. From time of enrollment, survival at 10 years was 90 ± 32%. Our model suggests that given the absence of known high risk factors, BAV patients do not have an accelerated ascending or root growth rate in comparison to their TAV counterparts. BAV patients do have higher rates of aortic stenosis (p <.05), a factor that influenced the need for aortic repair. Conclusion: In conclusion, regardless of aortic valve type, the associated AAA has a similar natural history and low adverse event rate. In the absence of risk factors, conservative management of AAA can be accomplished with minimal risk to the patient.

Here we introduce the Optimal Tree Completion Problem, a general optimization problem that involves completing an unrooted binary tree (i.e., adding missing leaves) so as to minimize its distance from a reference tree on a superset of the leaves. More formally, given a pair of unrooted binary trees (T,t) where T has leaf set S and t has leaf set R, a subset of S, we wish to add all the leaves from S R to t so as to produce a new tree t' on leaf set S that has the minimum distance to T. We show that when the distance is defined by the Robinson-Foulds (RF) distance, an optimal solution can be found in polynomial time. We also present OCTAL, an algorithm that solves this RF Optimal Tree Completion Problem exactly in quadratic time. We report on a simulation study where we complete estimated gene trees using a reference tree that is based on a species tree estimated from a multi-locus dataset. OCTAL produces completed gene trees that are closer to the true gene trees than an existing heuristic approach, but the accuracy of the completed gene trees computed by OCTAL depends on how topologically similar the estimated species tree is to the true gene tree. Hence, under conditions with relatively low gene tree heterogeneity, OCTAL can be used to provide highly accurate completions of estimated gene trees. We close with a discussion of future research.

Gathering a group of mobile agents is a fundamental task in the field of distributed and mobile systems. This can be made drastically more difficult to achieve when some agents are subject to faults, especially the Byzantine ones that are known as being the worst faults to handle. In this paper we study, from a deterministic point of view, the task of Byzantine gathering in a network modeled as a graph. In other words, despite the presence of Byzantine agents, all the other (good) agents, starting from {possibly} different nodes and applying the same deterministic algorithm, have to meet at the same node in finite time and stop moving. An adversary chooses the initial nodes of the agents (the number of agents may be larger than the number of nodes) and assigns a different positive integer (called label) to each of them. Initially, each agent knows its label. The agents move in synchronous rounds and can communicate with each other only when located at the same node. Within the team, f of the agents are Byzantine. A Byzantine agent acts in an unpredictable and arbitrary way. For example, it can choose an arbitrary port when it moves, can convey arbitrary information to other agents and can change its label in every round, in particular by forging the label of another agent or by creating a completely new one. Besides its label, which corresponds to a local knowledge, an agent is assigned some global knowledge denoted by GK that is common to all agents. In literature, the Byzantine gathering problem has been analyzed in arbitrary n-node graphs by considering the scenario when GK=(n,f) and the scenario when GK=f. In the first (resp. second) scenario, it has been shown that the minimum number of good agents guaranteeing deterministic gathering of all of them is f+1 (resp. f+2). However, for both these scenarios, all the existing deterministic algorithms, whether or not they are optimal in terms of required number of good agents, have the major disadvantage of having a time complexity that is exponential in n and L, where L is the value of the largest label belonging to a good agent. In this paper, we seek to design a deterministic solution for Byzantine gathering that makes a concession on the proportion of Byzantine agents within the team, but that offers a significantly lower complexity. We also seek to use a global knowledge whose the length of the binary representation (that we also call size) is small. In this respect, assuming that the agents are in a strong team i.e., a team in which the number of good agents is at least some prescribed value that is quadratic in f, we give positive and negative results. On the positive side, we show an algorithm that solves Byzantine gathering with all strong teams in all graphs of size at most n, for any integers n and f, in a time polynomial in n and the length |l_{min}| of the binary representation of the smallest label of a good agent. The algorithm works using a global knowledge of size O(log log log n), which is of optimal order of magnitude in our context to reach a time complexity that is polynomial in n and |l_{min}|. Indeed, on the negative side, we show that there is no deterministic algorithm solving Byzantine gathering with all strong teams, in all graphs of size at most n, in a time polynomial in n and |l_{min}| and using a global knowledge of size o(log log log n).

Optimal impulse control problems are, in general, difficult to solve. A current research goal is to isolate those problems that lead to tractable solutions. In this paper, we identify a special class of optimal impulse control problems which are easy to solve. Easy to solve means that solution algorithms are polynomial in time and therefore suitable to the on-line implementation in real-time problems. We do this by using a paradigm borrowed from the Operations Research field. As main result, we present a solution algorithm that converges to the exact solution in polynomial time. Our approach consists in approximating the optimal impulse control problem via a binary linear programming problem with a totally unimodular constraint matrix. Hence, solving the binary linear programming problem is equivalent to solving its linear relaxation. It turns out that any solution of the linear relaxation is a feasible solution for the optimal impulse control problem. Then, given the feasible solution, obtained solving the linear relaxation, we find the optimal solution via local search.

Obtaining a matching in a graph satisfying a certain objective is an important class of graph problems. Matching algorithms have received attention for several decades. However, while there are efficient algorithms to obtain a maximum weight matching, not much is known about the maximum weight maximum cardinality, and maximum cardinality maximum weight matching problems for general graphs. Our contribution in this work is to show that for bounded weight input graphs one can obtain an algorithm for both maximum weight maximum cardinality (for real weights), and maximum cardinality maximum weight matching (for integer weights) by modifying the input and running the existing maximum weight matching algorithm. Also, given the current state of the art in maximum weight matching algorithms, we show that, for bounded weight input graphs, both maximum weight maximum cardinality, and maximum cardinality maximum weight matching have algorithms of similar complexities to that of maximum weight matching. Subsequently, we also obtain approximation algorithms for maximum weight maximum cardinality, and maximum cardinality maximum weight matching.

We consider a finite buffer shared by multiple packet queues. Throughput can be considerably improved by partitioning the buffer space among the queues judiciously, especially under a high load regime. We formulate optimal buffer partitioning as a resource allocation problem, the solution of which is found through a greedy incremental algorithm in polynomial time. The rest of the work is devoted to applying the optimal buffer allocation strategy in different scenarios modeling a wireless downlink. First, the strategy is applied in a general parallel M/M/1/m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> system and a numerical study verifies that the strategy may boost the throughput considerably. Then, a multichannel extension of this system is considered when the users have different arrival rates and channels have different outage probabilities. Jointly optimal buffer space allocation and channel assignment problems in this scenario are shown to be separable. Lastly, buffer allocation is considered in a system where users need to be multiplexed and scheduled based on channel state. It is shown that this system can be modeled as a set of parallel M/G/1/m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> queues to which the optimum buffer allocation strategy is again applicable. The improvement brought by optimal buffer allocation to scheduling based solely on channel-state is explored. It is observed that buffer optimization can result in remarkable throughput increase on top of channel-based user selection.

Blocking flows were introduced by Dinic (Soviet Math Doklady 11: 1277–1280, 1970) to speed up the computation of maximum network flows. They have been used in algorithms for problems such as maximum cardinality matching of bipartite graphs Hopcroft and Karp (SIAM J Comput 2(4), 225–231, 1973) and general graphs Micali and Vazirani (in: Proceedings of the 21st Annual Symposium on Foundations of Computer Science, 17–27, 1980), maximum weight matching of general graphs Gabow and Tarjan (J ACM 38(4), 815–853, 1991), and many others. The blocking algorithm of Gabow and Tarjan (1991) for matching is based on depth-first search. We extend the depth-first search approach to find f-factors of general multigraphs. Here f is an arbitrary integral-valued function on vertices, an f-matching is a subgraph where every vertex x has degree $$\le f(x)$$ , an f-factor has equality in every degree bound. A set of blocking trails for an f-matching M is a maximal collection $$\mathcal{A}$$ of edge-disjoint augmenting trails such that $$M\bigoplus _{A\in {\mathcal{A}}} A $$ is a valid f-matching. Blocking trails are needed in efficient algorithms for maximum cardinality f-matching Huang and Pettie (Algorithmica 84(7): 1952–1992, 2022), maximum weight f-factors/matchings by scaling Duan et al. (In: Proceedings of the 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020), Vol. 168 of LIPIcs, 41:1-41:17, 2020; Gabow (A weight-scaling algorithm for f-factors of multigraphs. arXiv:2010.01102 , 2020), and approximate maximum weight f-factors and f-edge covers Huang and Pettie (2022). Since these algorithms find many sets of blocking trails, the time to find blocking trails is a dominant factor in the running time. Our blocking trail algorithm runs in linear time O(m). In independent work and using a different approach, Huang and Pettie (2022) present a blocking trails algorithm using time $$O(m\alpha (m,n))$$ . As examples of the time bounds for the above applications, an approximate maximum weight f-factor is found in time $$O(m\,\alpha (m,n))$$ using Huang and Pettie (2022), and our algorithm eliminates the factor $$\alpha (m,n)$$ . Similarly a maximum weight f-factor is found in time $$O(\sqrt{\Phi \log \Phi }\, m\,\alpha (m,n)\, \log (\Phi W))\,$$ using Huang and Pettie (2022) , ( $$\Phi =\sum _{v\in V} f(v)$$ , W the maximum edge weight) and our algorithm eliminates the $$\alpha (m,n)$$ factor, making the time within a factor $$\sqrt{\log {\Phi }}$$ of the bound for bipartite multigraphs. The technical difficulty for this work stems from the fact that a fixed vertex can occur many times in a given search. This does not occur in ordinary matching or in algorithms for maximum cardinality or maximum weight f-matching. These multiple occurrences can create a new variant of blossom, the “skew blossom”. Also they can make blossoms become “incomplete”, i.e., partially processed yet still relevant in future searches.

Massive multiple-input multiple-output (MIMO) technology is expected to achieve significant gains in both signal-to-noise-plus-interference ratio (SINR) and throughput for 5G cellular wireless networks. Efficient and highly accurate channel state information (CSI) acquisition at the base stations (BS) is essential to achieve the potential benefits of massive MIMO systems. However, the inadequate number of orthogonal pilot sequences used for CSI estimation leads to erroneous channel estimation as it causes interference between pilot sequences. This phenomenon is coined pilot contamination and severely limits the system performance. Therefore, we address in this paper the pilot contamination problem in massive MIMO Cloud-Radio Access Networks systems. Leveraging on a real telecom operator data delivered by Call Detail Records (CDR), our objective is to maximize the average uplink achievable rate by reducing the effect of pilot contamination in massive MIMO Cloud-Radio Access Networks (C-RAN). As such a problem is a non-linear integer problem that has no solution in polynomial time, we develop a two-stage solution to solve it. First, a coalition game is proposed where Remote Radio Heads (RRHs) gather into clusters with random user-pilot allocation. Then, two greedy heuristics are applied to match each user in the cluster with a given pilot. The goal is to further improve the average uplink rate achieved in the first stage. Simulation results show that our heuristic solutions for pilot contamination mitigation outperform the traditional pilot allocation solution and a state-of-the-art pilot allocation scheme based on large-scale fading in terms of average uplink achievable rate and SINR.

During the past decade, many efforts have been made to use palmprints as a biometric modality. However, most of the existing palmprint recognition systems are based on encoding and matching creases, which are not as reliable as ridges. This affects the use of palmprints in large-scale person identification applications where the biometric modality needs to be distinctive as well as insensitive to changes in age and skin conditions. Recently, several ridge-based palmprint matching algorithms have been proposed to fill the gap. Major contributions of these systems include reliable orientation field estimation in the presence of creases and the use of multiple features in matching, while the matching algorithms adopted in these systems simply follow the matching algorithms for fingerprints. However, palmprints differ from fingerprints in several aspects: 1) Palmprints are much larger and thus contain a large number of minutiae, 2) palms are more deformable than fingertips, and 3) the quality and discrimination power of different regions in palmprints vary significantly. As a result, these matchers are unable to appropriately handle the distortion and noise, despite heavy computational cost. Motivated by the matching strategies of human palmprint experts, we developed a novel palmprint recognition system. The main contributions are as follows: 1) Statistics of major features in palmprints are quantitatively studied, 2) a segment-based matching and fusion algorithm is proposed to deal with the skin distortion and the varying discrimination power of different palmprint regions, and 3) to reduce the computational complexity, an orientation field-based registration algorithm is designed for registering the palmprints into the same coordinate system before matching and a cascade filter is built to reject the nonmated gallery palmprints in early stage. The proposed matcher is tested by matching 840 query palmprints against a gallery set of 13,736 palmprints. Experimental results show that the proposed matcher outperforms the existing matchers a lot both in matching accuracy and speed.

The escalating demand for high-quality video streaming poses a major challenge for communication networks today. Catering to these bandwidth-hungry video streaming services places a huge burden on the limited spectral resources of communication networks, limiting the resources available for other services as well. Large volumes of video traffic can lead to severe network congestion, particularly during live streaming events, which require sending the same content to a large number of users simultaneously. For such applications, multicast transmission can effectively combat network congestion while meeting the demands of all the users by serving groups of users requesting the same content over shared spectral resources. Streaming services can further benefit from multi-connectivity, which allows users to receive content from multiple base stations simultaneously. Integrating multi-connectivity within multicast streaming can improve the system resource utilization while also providing seamless connectivity to multicast users. Toward this end, this work studied the impact of using multi-connectivity (MC) alongside wireless multicast for meeting the resource requirements of video streaming. Our findings show that MC substantially enhances the performance of multicast streaming, particularly benefiting cell-edge users who often experience poor channel conditions. We especially considered the number of users that can be simultaneously served by multi-connected multicast systems. It was observed that about 60% of the users that are left unserved under single-connectivity multicast are successfully served using the same resources by employing multi-connectivity in multicast transmissions. We prove that the optimal resource allocation problem for MC multicast is NP-hard. As a solution, we present a greedy approximation algorithm with an approximation factor of (1−1/e). Furthermore, we establish that no other polynomial-time algorithm can offer a superior approximation. To generate realistic video traffic patterns in our simulations, we made use of traces from actual videos. Our results clearly demonstrate that multi-connectivity leads to significant enhancements in the performance of multicast streaming.