A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions

This paper presents a distributed algorithm applicable to a wide range of practical multi-robot applications. In such multi-robot applications, the user-defined objectives of the mission can be cast as a general optimization problem, without explicit guidelines of the subtasks per different robot. Owing to the unknown environment, unknown robot dynamics, sensor nonlinearities, etc., the analytic form of the optimization cost function is not available a priori. Therefore, standard gradient-descent-like algorithms are not applicable to these problems. To tackle this, we introduce a new algorithm that carefully designs each robot’s subcost function, the optimization of which can accomplish the overall team objective. Upon this transformation, we propose a distributed methodology based on the cognitive-based adaptive optimization (CAO) algorithm, that is able to approximate the evolution of each robot’s cost function and to adequately optimize its decision variables (robot actions). The latter can be achieved by online learning only the problem-specific characteristics that affect the accomplishment of mission objectives. The overall, low-complexity algorithm can straightforwardly incorporate any kind of operational constraint, is fault tolerant, and can appropriately tackle time-varying cost functions. A cornerstone of this approach is that it shares the same convergence characteristics as those of block coordinate descent algorithms. The proposed algorithm is evaluated in three heterogeneous simulation set-ups under multiple scenarios, against both general-purpose and problem-specific algorithms.

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On Time-Frequency Synchronization in LoRa System: From Analysis to Near-Optimal Algorithm

10.36227/techrxiv.13560158 ◽

2021 ◽

Author(s):

Vincent Savaux ◽

Christophe Delacourt ◽

Patrick Savelli

Keyword(s):

Cost Function ◽

Optimal Algorithm ◽

A Priori ◽

Low Complexity ◽

Frequency Offset ◽

Maximization Problem ◽

Frequency Synchronization ◽

Time Frequency ◽

Time Offset ◽

Ml Estimator

This paper deals with time and frequency synchronization in LoRa system based on the preamble symbols. A thorough analysis of the maximum likelihood (ML) estimator of the delay (time offset) and the frequency offset shows that the resulting cost function is not concave. As a consequence the a priori solution to the maximization problem consists in exhaustively searching over all the possible values of both the delay and the frequency offset. Furthermore, it is shown that these parameters are intertwined and therefore they must be jointly estimated, leading to an extremely complex solution. Alternatively, we show that it is possible to recover the concavity of the cost function, from which we suggest a low-complexity synchronization algorithm, whose steps are described in detail. Simulations results show that the suggested method reaches the same performance as the ML exhaustive search, while the complexity is drastically reduced, allowing for a real-time implementation of a LoRa receiver. <br>

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On Time-Frequency Synchronization in LoRa System: From Analysis to Near-Optimal Algorithm

10.36227/techrxiv.13560158.v1 ◽

2021 ◽

Author(s):

Vincent Savaux ◽

Christophe Delacourt ◽

Patrick Savelli

Keyword(s):

Cost Function ◽

Optimal Algorithm ◽

A Priori ◽

Low Complexity ◽

Frequency Offset ◽

Maximization Problem ◽

Frequency Synchronization ◽

Time Frequency ◽

Time Offset ◽

Ml Estimator

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Fault Tolerant Reliable Protocol (FTRP) Performance Evaluation in Wireless Sensor Networks: An Extensitive Study

Journal of Electronics and Sensors ◽

10.31829/2689-6958/jes2019-2(1)-107 ◽

2019 ◽

pp. 1-36

Keyword(s):

Wireless Sensor Networks ◽

Fault Tolerance ◽

Sensor Networks ◽

Fault Tolerant ◽

Cluster Head ◽

Biological Data ◽

Wireless Sensor ◽

Delivery Ratio ◽

Good Performer ◽

Wide Range

Fault Tolerant Reliable Protocol (FTRP) is proposed as a novel routing protocol designed for Wireless Sensor Networks (WSNs). FTRP offers fault tolerance reliability for packet exchange and support for dynamic network changes. The key concept used is the use of node logical clustering. The protocol delegates the routing ownership to the cluster heads where fault tolerance functionality is implemented. FTRP utilizes cluster head nodes along with cluster head groups to store packets in transient. In addition, FTRP utilizes broadcast, which reduces the message overhead as compared to classical flooding mechanisms. FTRP manipulates Time to Live values for the various routing messages to control message broadcast. FTRP utilizes jitter in messages transmission to reduce the effect of synchronized node states, which in turn reduces collisions. FTRP performance has been extensively through simulations against Ad-hoc On-demand Distance Vector (AODV) and Optimized Link State (OLSR) routing protocols. Packet Delivery Ratio (PDR), Aggregate Throughput and End-to-End delay (E-2-E) had been used as performance metrics. In terms of PDR and aggregate throughput, it is found that FTRP is an excellent performer in all mobility scenarios whether the network is sparse or dense. In stationary scenarios, FTRP performed well in sparse network; however, in dense network FTRP’s performance had degraded yet in an acceptable range. This degradation is attributed to synchronized nodes states. Reliably delivering a message comes to a cost, as in terms of E-2-E. results show that FTRP is considered a good performer in all mobility scenarios where the network is sparse. In sparse stationary scenario, FTRP is considered good performer, however in dense stationary scenarios FTRP’s E-2-E is not acceptable. There are times when receiving a network message is more important than other costs such as energy or delay. That makes FTRP suitable for wide range of WSNs applications, such as military applications by monitoring soldiers’ biological data and supplies while in battlefield and battle damage assessment. FTRP can also be used in health applications in addition to wide range of geo-fencing, environmental monitoring, resource monitoring, production lines monitoring, agriculture and animals tracking. FTRP should be avoided in dense stationary deployments such as, but not limited to, scenarios where high application response is critical and life endangering such as biohazards detection or within intensive care units.

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A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽

10.3390/s21041031 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1031

Author(s):

Joseba Gorospe ◽

Rubén Mulero ◽

Olatz Arbelaitz ◽

Javier Muguerza ◽

Miguel Ángel Antón

Keyword(s):

Deep Learning ◽

Embedded Systems ◽

Embedded System ◽

General Purpose ◽

Learning Networks ◽

Performance Study ◽

Learning Techniques ◽

Wide Range ◽

Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

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A Systematic Review and Qualitative Synthesis Resulting in a Typology of Elementary Classroom Movement Integration Interventions

Sports Medicine - Open ◽

10.1186/s40798-019-0218-8 ◽

2020 ◽

Vol 6 (1) ◽

Cited By ~ 2

Author(s):

Spyridoula Vazou ◽

Collin A. Webster ◽

Gregory Stewart ◽

Priscila Candal ◽

Cate A. Egan ◽

...

Keyword(s):

Physical Activity ◽

Teacher Collaboration ◽

A Priori ◽

Real Life ◽

Dose Intensity ◽

Routine Practice ◽

Qualitative Synthesis ◽

Wide Range ◽

Meta Analyses ◽

Movement Integration

Abstract Background/Objective Movement integration (MI) involves infusing physical activity into normal classroom time. A wide range of MI interventions have succeeded in increasing children’s participation in physical activity. However, no previous research has attempted to unpack the various MI intervention approaches. Therefore, this study aimed to systematically review, qualitatively analyze, and develop a typology of MI interventions conducted in primary/elementary school settings. Subjects/Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to identify published MI interventions. Irrelevant records were removed first by title, then by abstract, and finally by full texts of articles, resulting in 72 studies being retained for qualitative analysis. A deductive approach, using previous MI research as an a priori analytic framework, alongside inductive techniques were used to analyze the data. Results Four types of MI interventions were identified and labeled based on their design: student-driven, teacher-driven, researcher-teacher collaboration, and researcher-driven. Each type was further refined based on the MI strategies (movement breaks, active lessons, other: opening activity, transitions, reward, awareness), the level of intrapersonal and institutional support (training, resources), and the delivery (dose, intensity, type, fidelity). Nearly half of the interventions were researcher-driven, which may undermine the sustainability of MI as a routine practice by teachers in schools. An imbalance is evident on the MI strategies, with transitions, opening and awareness activities, and rewards being limitedly studied. Delivery should be further examined with a strong focus on reporting fidelity. Conclusions There are distinct approaches that are most often employed to promote the use of MI and these approaches may often lack a minimum standard for reporting MI intervention details. This typology may be useful to effectively translate the evidence into practice in real-life settings to better understand and study MI interventions.

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An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

Applied Sciences ◽

10.3390/app11020850 ◽

2021 ◽

Vol 11 (2) ◽

pp. 850

Author(s):

Dokkyun Yi ◽

Sangmin Ji ◽

Jieun Park

Keyword(s):

Artificial Intelligence ◽

Cost Function ◽

Numerical Experiments ◽

Global Minimum ◽

Optimization Method ◽

Learning Method ◽

Adaptive Optimization ◽

The Cost ◽

Proof Of Convergence ◽

Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

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One in seven pathogenic variants can be challenging to detect by NGS: an analysis of 450,000 patients with implications for clinical sensitivity and genetic test implementation

Genetics in Medicine ◽

10.1038/s41436-021-01187-w ◽

2021 ◽

Author(s):

Stephen E. Lincoln ◽

Tina Hambuch ◽

Justin M. Zook ◽

Sara L. Bristow ◽

Kathryn Hatchell ◽

...

Keyword(s):

Diagnostic Yield ◽

Copy Number Variants ◽

Low Complexity ◽

Clinical Genetic ◽

Clinical Sensitivity ◽

Pathogenic Variants ◽

Wide Range ◽

Large Indels ◽

Next Generation Sequencing Ngs ◽

The Impact

Abstract Purpose To evaluate the impact of technically challenging variants on the implementation, validation, and diagnostic yield of commonly used clinical genetic tests. Such variants include large indels, small copy-number variants (CNVs), complex alterations, and variants in low-complexity or segmentally duplicated regions. Methods An interlaboratory pilot study used synthetic specimens to assess detection of challenging variant types by various next-generation sequencing (NGS)–based workflows. One well-performing workflow was further validated and used in clinician-ordered testing of more than 450,000 patients. Results In the interlaboratory study, only 2 of 13 challenging variants were detected by all 10 workflows, and just 3 workflows detected all 13. Limitations were also observed among 11 less-challenging indels. In clinical testing, 21.6% of patients carried one or more pathogenic variants, of which 13.8% (17,561) were classified as technically challenging. These variants were of diverse types, affecting 556 of 1,217 genes across hereditary cancer, cardiovascular, neurological, pediatric, reproductive carrier screening, and other indicated tests. Conclusion The analytic and clinical sensitivity of NGS workflows can vary considerably, particularly for prevalent, technically challenging variants. This can have important implications for the design and validation of tests (by laboratories) and the selection of tests (by clinicians) for a wide range of clinical indications.

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An information theoretic approach to link prediction in multiplex networks

Scientific Reports ◽

10.1038/s41598-021-92427-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Seyed Hossein Jafari ◽

Amir Mahdi Abdolhosseini-Qomi ◽

Masoud Asadpour ◽

Maseud Rahgozar ◽

Naser Yazdani

Keyword(s):

Real World ◽

Link Prediction ◽

Large Scale ◽

Similarity Measures ◽

Prediction Method ◽

General Purpose ◽

Fast Method ◽

Theoretic Approach ◽

Multiplex Networks ◽

Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

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Applications of 3D printing in critical care medicine: A scoping review

Anaesthesia and Intensive Care ◽

10.1177/0310057x20976655 ◽

2021 ◽

pp. 0310057X2097665

Author(s):

Natasha Abeysekera ◽

Kirsty A Whitmore ◽

Ashvini Abeysekera ◽

George Pang ◽

Kevin B Laupland

Keyword(s):

Critical Care ◽

Scoping Review ◽

A Priori ◽

Three Dimensional ◽

Critical Care Medicine ◽

Future Research ◽

Care Practice ◽

Three Dimensional Printing ◽

Wide Range ◽

Dimensional Printing

Although a wide range of medical applications for three-dimensional printing technology have been recognised, little has been described about its utility in critical care medicine. The aim of this review was to identify three-dimensional printing applications related to critical care practice. A scoping review of the literature was conducted via a systematic search of three databases. A priori specified themes included airway management, procedural support, and simulation and medical education. The search identified 1544 articles, of which 65 were included. Ranging across many applications, most were published since 2016 in non – critical care discipline-specific journals. Most studies related to the application of three-dimensional printed models of simulation and reported good fidelity; however, several studies reported that the models poorly represented human tissue characteristics. Randomised controlled trials found some models were equivalent to commercial airway-related skills trainers. Several studies relating to the use of three-dimensional printing model simulations for spinal and neuraxial procedures reported a high degree of realism, including ultrasonography applications three-dimensional printing technologies. This scoping review identified several novel applications for three-dimensional printing in critical care medicine. Three-dimensional printing technologies have been under-utilised in critical care and provide opportunities for future research.

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A priori assessment of a smart-navigated unmanned aerial vehicle disaster cargo fleet

SIMULATION ◽

10.1177/0037549720921447 ◽

2020 ◽

Vol 96 (8) ◽

pp. 641-653

Author(s):

Jonathan Larson ◽

Paul Isihara ◽

Gabriel Flores ◽

Edwin Townsend ◽

Danilo R. Diedrichs ◽

...

Keyword(s):

Disaster Response ◽

Best Practice ◽

A Priori ◽

Geographic Distance ◽

Field Tests ◽

General Purpose ◽

Assessment Model ◽

Sensitivity Analyses ◽

Modeling And Analysis ◽

Cargo Delivery

The United Nations Office for the Coordination of Humanitarian Affairs has asserted that risks in deployment of unmanned aerial vehicles (UAVs) within disaster response must be reduced by careful development of best-practice standards before implementing such systems. With recent humanitarian field tests of cargo UAVs as indication that implementation may soon become reality, a priori assessment of a smart-navigated (autonomous) UAV disaster cargo fleet via simulation modeling and analysis is vital to the best-practice development process. Logistical problems with ground transport of relief supplies in Puerto Rico after Hurricane Maria (2017) pose a compelling use scenario for UAV disaster cargo delivery. In this context, we introduce a General Purpose Assessment Model (GPAM) that can estimate the potential effectiveness of a cargo UAV fleet for any given response region. We evaluate this model using the following standards: (i) realistic specifications; (ii) stable output for various realistic specifications; and (iii) support of humanitarian goals. To this end, we discuss data from humanitarian cargo delivery field tests and feedback from practitioners, perform sensitivity analyses, and demonstrate the advantage of using humanitarian rather than geographic distance in making fleet delivery assignments. We conclude with several major challenges faced by those who wish to implement smart-navigated UAV cargo fleets in disaster response, and the need for further GPAM development. This paper proposes the GPAM as a useful simulation tool to encourage and guide steps toward humanitarian use of UAVs for cargo delivery. The model’s flexibility can allow organizations to quickly and effectively determine how best to respond to disasters.

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