In one general aspect, various embodiments of the present invention can include a motorized surgical cutting and fastening instrument having a drive shaft, a motor selectively engageable with the drive shaft, and a manual return mechanism configured to operably disengage the motor from the drive shaft and retract the drive shaft. In at least one embodiment, a surgeon, or other operator of the surgical instrument, can utilize the manual return mechanism to retract the drive shaft after it has been advanced, especially when the motor, or a power source supplying the motor, has failed or is otherwise unable to provide a force sufficient to retract the drive shaft.

Powered surgical cutting and stapling apparatus with manually retractable firing system

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Network functions virtualization (NFV) is a new network architecture framework where network function that traditionally used dedicated hardware (middleboxes or network appliances) are now implemented in software that runs on top of general purpose hardware such as high volume server. NFV emerges as an initiative from the industry (network operators, carriers, and manufacturers) in order to increase the deployment flexibility and integration of new network services with increased agility within operator’s networks and to obtain significant reductions in operating expenditures and capital expenditures. NFV promotes virtualizing network functions such as transcoders, firewalls, and load balancers, among others, which were carried out by specialized hardware devices and migrating them to software-based appliances. One of the main challenges for the deployment of NFV is the resource allocation of demanded network services in NFV-based network infrastructures. This challenge has been called the NFV resource allocation (NFV-RA) problem. This paper presents a comprehensive state of the art of NFV-RA by introducing a novel classification of the main approaches that pose solutions to solve it. This paper also presents the research challenges that are still subject of future investigation in the NFV-RA realm.

Resource Allocation in NFV: A Comprehensive Survey

Today’s telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users’ behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, machine learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing, and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude this paper proposing new possible research directions.

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An Overview on Application of Machine Learning Techniques in Optical Networks

In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

Probabilistic shaping based on constant composition distribution matching (CCDM) has received considerable attention as a way to increase the capacity of fiber optical communication systems. CCDM suffers from significant rate loss at short blocklengths and requires long blocklengths to achieve high shaping gain, which makes its implementation very challenging. In this paper, we propose to use enumerative sphere shaping (ESS) and investigate its performance for the nonlinear fiber optical channel. ESS has lower rate loss than CCDM at the same shaping rate, which makes it a suitable candidate to be implemented in real-time high-speed optical systems. In this paper, we first show that finite blocklength ESS and CCDM exhibit higher effective signal-to-noise ratio than their infinite blocklength counterparts. These results show that for the nonlinear fiber optical channel, large blocklengths should be avoided. We then show that for a 400 Gb/s dual-polarization 64-QAM WDM transmission system, ESS with short blocklengths outperforms both uniform signaling and CCDM. Gains in terms of both bit-metric decoding rate and bit-error rate are presented. ESS with a blocklength of 200 is shown to provide an extension reach of about 200 km in comparison with CCDM with the same blocklength. The obtained reach increase of ESS with a blocklength of 200 over uniform signaling is approximately 450 km (approximately 19%)

Introducing Enumerative Sphere Shaping for Optical Communication Systems with Short Blocklengths

Network function virtualization (NFV) provides an efﬁcient and ﬂexible way to deploy network services in the form of service function chains (SFCs) by adopting generalized equipment. However, software-based virtualized network functions (VNFs) bring new challenge for network operators in providing service availability guarantees. Traditionally, network-level protection mechanisms are considered separately from function-level VNF backup mechanisms. However, a SFC's availability cannot be guaranteed if only network- level protection mechanisms or only function-level VNF backup mechanisms are considered. In this paper, we propose a coordinated protection mechanism that adopts both backup path protection in the network and VNF replicas at nodes to guarantee a SFC's availability. The proposed mechanism determines the number of replicas required for each VNF in the SFC, and allocates the replicas to physical nodes on the working and backup paths while maintaining ordered dependency among VNFs. Simulation results show that the proposed algorithms contribute to reducing the SFC blocking and the cost of computing resources.

Guaranteed-Availability Network Function Virtualization with Network Protection and VNF Replication

Probabilistic shaping based on constant composition distribution matching (CCDM) has received considerable attention as a way to increase the capacity of fiber optical communication systems. CCDM suffers from significant rate loss at short blocklengths and requires long blocklengths to achieve high shaping gain, which makes its implementation very challenging. In this paper, we propose to use enumerative sphere shaping (ESS) and investigate its performance for the nonlinear fiber optical channel. ESS has lower rate loss than CCDM at the same shaping rate, which makes it a suitable candidate to be implemented in real-time high-speed optical systems. In this paper, we first show that finite blocklength ESS and CCDM exhibit higher effective signal-to-noise ratio than their infinite blocklength counterparts. These results show that for the nonlinear fiber optical channel, large blocklengths should be avoided. We then show that for a 400 Gb/s dual-polarization 64-QAM WDM transmission system, ESS with short blocklengths outperforms both uniform signaling and CCDM. Gains in terms of both bit-metric decoding rate and bit-error rate are presented. ESS with a blocklength of 200 is shown to provide an extension reach of about 200 km in comparison with CCDM with the same blocklength. The obtained reach increase of ESS with a blocklength of 200 over uniform signaling is approximately 450 km (approximately $\text{19}\%$ ).

/pdf/introducing-enumerative-sphere-shaping-for-optical-1wcfpn2ctd.pdf

Introducing Enumerative Sphere Shaping for Optical Communication Systems With Short Blocklengths

Fiber nonlinearity has become a major limiting transmission impairment factor. In this paper, we discuss various nonlinearity mitigation techniques in electrical and optical domains. In electrical domain, multiple reduced complexity digital back-propagation algorithms were developed, including perturbation back-propagation. One drawback is that they can compensate for intra-channel self-phase modulation (SPM) effects only. The inter-channel fiber nonlinearity mitigation can be done in optical domain. For example, digital subcarrier multiplexing (SCM) can effectively mitigate both SPM and cross-phase modulation (XPM) effects. It can become even more beneficial for higher symbol rate systems (e.g., 64 Gbaud and 128 Gbaud) and has a capability to deliver longer reach than single carrier 32 Gbaud DP-16QAM signals and only slightly shorter reach (by up to 7%) compared to 32 Gbaud SCM systems. Another technique, the total intensity directed phase modulation, can provide per-span SPM and XPM compensation. However, this technique is limited to compensation of a few channels only. On the other hand, the proposed enhanced pre-dispersed spectral inversion can effectively compensate for SPM/XPM effects of multiple channels and become more practical by removing limitation to link symmetry even in nonuniform transmission links. Finally, subcarrier power pre-emphasis in optical superchannels can equalize performance of all subcarriers by mitigating intersubcarrier XPM and extend transmission reach by 20%.

Enabling Technologies for Fiber Nonlinearity Mitigation in High Capacity Transmission Systems

Computation of service function chains in multi-domain networks is critical for enabling the wide deployment of network function visualization for providing end-to-end network services. In this paper, we present a vertex-centric distributed orchestration framework for multi-domain networks, in which physical infrastructure information is maintained locally within each domain without infrastructure information sharing between domains, so that the management and control of multi-domain networks can be significantly simplified. We also propose a distributed computing algorithm for mapping a service function chain request in multi-domain networks. Our objective is to find all feasible mappings, from which we can further prune to obtain the optimal solution satisfying different constraints and policies. We implement the proposed algorithm in an open source vertex-centric distributed computing system and simulate two practical network topologies to evaluate the performance, including signaling delay, message overhead, and computation time. Simulation results demonstrate superior efficiency and scalability of the proposed algorithm.

Tadashi Ikeuchi

Papers

Introducing Enumerative Sphere Shaping for Optical Communication Systems with Short Blocklengths

Guaranteed-Availability Network Function Virtualization with Network Protection and VNF Replication

Introducing Enumerative Sphere Shaping for Optical Communication Systems With Short Blocklengths

Enabling Technologies for Fiber Nonlinearity Mitigation in High Capacity Transmission Systems

Vertex-centric computation of service function chains in multi-domain networks