Suspend feature for multiple devices of same type in bhyve

2020 ◽  
Author(s):  
Eric-Bogdan Postolache ◽  
Darius Mihai ◽  
Maria-Elena Mihailescu ◽  
Sergiu Weisz ◽  
Mihai Barbulescu ◽  
...  
Keyword(s):  
Multiple Devices

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

scholarly journals IoT Traffic: Modeling and Measurement Experiments

IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 140-162
Author(s):  
Hung Nguyen-An ◽  
Thomas Silverston ◽  
Taku Yamazaki ◽  
Takumi Miyoshi
Keyword(s):  
Smart Home ◽  
Large Scale ◽  
Traffic Modeling ◽  
The Novel ◽  
Network Behavior ◽  
Performance Accuracy ◽  
Traffic Generator ◽  
Multiple Devices ◽  
Iot Devices ◽  
The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

scholarly journals Lifetime of electrochromic optical transition cycling of ethyl viologen diperchlorate-based electrochromic devices

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Logan G. Kiefer ◽  
Christian J. Robert ◽  
Taylor D. Sparks
Keyword(s):  
Optical Transition ◽  
Low Cost ◽  
Cycling Performance ◽  
Power Measurement ◽  
Electrochromic Devices ◽  
Metal Oxide Films ◽  
Cycle Times ◽  
Vis Spectroscopy ◽  
Multiple Devices ◽  
Measurement Devices

AbstractElectrochromic materials and devices are enabling a variety of advanced technologies. Gel-based organic electrochromic molecules such as ethyl viologen diperchlorate are attractive options due to their simple device design and low cost processing options relative to the more expensive and complex transition metal oxide films. However, electrochromic devices are subject to extensive cycling in which failure and fatigue can eventually occur. This work presents the lifetime cycling performance of ethyl viologen diperchlorate-based electrochromic devices using two different anodic compounds, hydroquinone and ferrocene, which are cycled at different voltages, 3 V and 1.2 V, respectively. Multiple devices are cycled until failure with periodic device characterization via UV–Vis spectroscopy, electrical resistance and power measurement, and transition duration measurement. Devices with hydroquinone can transition quickly. Cycle times are $$\sim$$ ∼ 30 s in these samples, however, these samples also typically fail before 3000 cycles. On the other hand, devices using ferrocene transition more slowly (total cycle time $$\sim$$ ∼ 2 min), but have superior cycling performance with all samples surviving beyond 10,000 cycles while complying with ASTM E2141-14 standard.

scholarly journals UAV Forensics: DJI Mini 2 Case Study

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 49
Author(s):  
Miloš Stanković ◽  
Mohammad Meraj Mirza ◽  
Umit Karabiyik
Keyword(s):  
Law Enforcement ◽  
Federal Aviation Administration ◽  
Easy Access ◽  
Law Enforcement Agencies ◽  
The Past ◽  
Personally Identifiable Information ◽  
Multiple Devices ◽  
Forensic Tools ◽  
Recreational Use

Rapid technology advancements, especially in the past decade, have allowed off-the-shelf unmanned aerial vehicles (UAVs) that weigh less than 250 g to become available for recreational use by the general population. Many well-known manufacturers (e.g., DJI) are now focusing on this segment of UAVs, and the new DJI Mini 2 drone is one of many that falls under this category, which enables easy access to be purchased and used without any Part 107 certification and Remote ID registration. The versatility of drones and drone models is appealing for customers, but they pose many challenges to forensic tools and digital forensics investigators due to numerous hardware and software variations. In addition, different devices can be associated and used for controlling these drones (e.g., Android and iOS smartphones). Moreover, according to the Federal Aviation Administration (FAA), the adoption of Remote ID is not going to be required for people without the 107 certifications for this segment at least until 2023, which creates finding personally identifiable information a necessity in these types of investigations. In this research, we conducted a comprehensive investigation of DJI Mini 2 and its data stored across multiple devices (e.g., SD cards and mobile devices) that are associated with the drone. The aim of this paper is to (1) create several criminal-like scenarios, (2) acquire and analyze the created scenarios using leading forensics software (e.g., Cellebrite and Magnet Axiom) that are commonly used by law enforcement agencies, (3) and present findings associated with potential criminal activities.

scholarly journals Communication Failure Resilient Distributed Neural Network for Edge Devices

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1614
Author(s):  
Jonghun Jeong ◽  
Jong Sung Park ◽  
Hoeseok Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Performance ◽  
State Of The Art ◽  
Wearable Devices ◽  
Communication Failure ◽  
Canadian Institute ◽  
Multiple Devices ◽  
Knowledge Distillation ◽  
Partitioning Technique

Recently, the necessity to run high-performance neural networks (NN) is increasing even in resource-constrained embedded systems such as wearable devices. However, due to the high computational and memory requirements of the NN applications, it is typically infeasible to execute them on a single device. Instead, it has been proposed to run a single NN application cooperatively on top of multiple devices, a so-called distributed neural network. In the distributed neural network, workloads of a single big NN application are distributed over multiple tiny devices. While the computation overhead could effectively be alleviated by this approach, the existing distributed NN techniques, such as MoDNN, still suffer from large traffics between the devices and vulnerability to communication failures. In order to get rid of such big communication overheads, a knowledge distillation based distributed NN, called Network of Neural Networks (NoNN), was proposed, which partitions the filters in the final convolutional layer of the original NN into multiple independent subsets and derives smaller NNs out of each subset. However, NoNN also has limitations in that the partitioning result may be unbalanced and it considerably compromises the correlation between filters in the original NN, which may result in an unacceptable accuracy degradation in case of communication failure. In this paper, in order to overcome these issues, we propose to enhance the partitioning strategy of NoNN in two aspects. First, we enhance the redundancy of the filters that are used to derive multiple smaller NNs by means of averaging to increase the immunity of the distributed NN to communication failure. Second, we propose a novel partitioning technique, modified from Eigenvector-based partitioning, to preserve the correlation between filters as much as possible while keeping the consistent number of filters distributed to each device. Throughout extensive experiments with the CIFAR-100 (Canadian Institute For Advanced Research-100) dataset, it has been observed that the proposed approach maintains high inference accuracy (over 70%, 1.53× improvement over the state-of-the-art approach), on average, even when a half of eight devices in a distributed NN fail to deliver their partial inference results.

scholarly journals Configuring Appium for iOS Applications and Test Automation in Multiple Devices

2020 ◽  
Author(s):  
Raiyan Rahman Chowdhury ◽  
Syeda Sumbul Hossain ◽  
Yeasir Arafat ◽  
Bushrat Jahan Siddiqui
Keyword(s):  
Test Automation ◽  
Multiple Devices

Pilot Study of an Electronic Laboratory Notebook for Research and Development

2021 ◽  
Author(s):  
Wesley Odom ◽  
Keyword(s):  
Pilot Study ◽  
Research And Development ◽  
Materials Science ◽  
Laboratory Notebook ◽  
Measurement Systems ◽  
Promising Tool ◽  
Multidisciplinary Working ◽  
Proper Training ◽  
Multiple Devices ◽  
Primary Standards

The laboratory notebook is the fundamental record for research and development. The emergence of cloud-based digital tools to replace or augment the laboratory notebook has shown promise for groups that are multidisciplinary, working asynchronously, or in multiple locations. This paper details a recent pilot study conducted by Sandia National Laboratories (SNL) comparing an electronic lab notebook (ELN) with traditional paper lab notebooks (PLN), including members of SNL’s Primary Standards Laboratory (PSL). Partly motivated by a related pilot study conducted at the National Institute of Standards and Technology (NIST), the focus of the present study was on the integrability of an ELN within the unique constraints of a national lab, including security protocols that limit cloud capabilities and limited WIFI. The study used Microsoft OneNote and commercially available mobile computing hardware. The pilot included 18 participants from the PSL, biosciences, and materials science/engineering labs. In addition to OneNote, participants were provided one of two options for a computer to be used as their note taking device (including a stylus). Usability and gap analyses, as well as interviews with pilot participants were conducted by members from Sandia’s human factors group. Findings from this study indicate that ELNs may be particularly useful for teams where sharing of procedures and results is important. Participants believed that use of the ELN increased organization of their work and facilitated reporting much more than paper lab notebooks (PLNs). Other benefits included searchability and capability for access on multiple devices. Many of the identified drawbacks were specific to the unique constraints of working at a national lab, but some constraints are more general (e.g. use of ELNs in wet labs where hazardous materials may be of concern). Overall, it was found with proper training, collaboration on best practices, and technical support, that ELNs appear to be a promising tool for modernizing recording practices in research. Some examples from PSL will be highlighted, including R&D for qualifying measurement systems, calibration processes, and procedures.

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