Minimum Response Time Research Articles

Highly sensitive optical sensor for hydrogen gas based on a polymer microcylinder ring resonator

A highly sensitive platform is demonstrated for hydrogen gas (H2) sensing based on a polymer microcylinder ring resonator (PMRR) obtained by an optical fiber coated with an inner nanofilm of amorphous palladium (Pd) and an outer polymer layer of polydimethylsiloxane (PDMS) permeable to H2. The sensing scheme is based on monitoring the spectral shifts of high-quality optical resonances called whispering gallery modes (WGMs) that propagate in the vicinity of the outer rim of the PDMS layer without being affected by the absorption and scattering losses caused by the Pd nanofilm. WGMs are excited by a single-mode tapered optical fiber evanescently coupled to the PMRR. The observed reversible spectral shifts of the WGMs are induced by changes in the diameter of the PDMS layer caused by expansion or contraction of the Pd nanofilm exposed to varying concentrations of H2. Maximum spectral shift sensitivity of 140 pm/% H2, a minimum response time of 95 s, and minimum limit of detection of ∼60 ppm were measured for sensors prepared with different thicknesses of the amorphous Pd nanofilm and tested in the H2 concentration range up to 1%, having nitrogen gas (N2) as a carrier. Experiments were also conducted with Pd nanofilms annealed in air or N2 atmosphere after the deposition. In both cases, smaller sensitivities were observed due to the formation of larger grains within the film, resulting in slower diffusion and reduced solubility of H in the Pd layer. The impacts of oxygen gas and humidity on sensor performance were also studied.

RETRACTED ARTICLE: Modified adaptive neuro fuzzy inference system based load balancing for virtual machine with security in cloud computing environment

In a heterogeneous environment, computation over internet is provided by a popular paradigm called cloud computing. In a cloud heterogeneous environment, service providing has various difficulties. Based on service type, difficulties differ. On cloud server, high load is produced by huge amount of request from various users for accessing various applications. Security and balancing of load are major concerns. In cloud environment, NP-hard optimization problem corresponds to load balancing. Dynamic load balancing is handled by various methods. They are designed for enhancing workload distribution process between nodes. Overload avoidance, minimization of average response time, data processing time and optimum utilization of resources are the major aim of those methods. An optimal load balancing technique should improve the turnaround time and maximum CPU utilization. Because of its opaqueness nature of cloud, security is a biggest challenge. According to Forbes, with introduction of General Data Protection Regulation security in cloud continue to be an issue with cloud computing. In the existing system, a fuzzy based hybrid load balancing algorithm is utilized and the results provided are not satisfactory. There are opportunities for improving CPU utilization and turnaround time and in terms of security. In this proposed research work, dynamic load balancing in a heterogeneous environment is handled by Modified Adaptive Neuro Fuzzy Inference System (MANFIS). Parameters of MANFIS are optimized by introducing Fire-fly Algorithm. Security is imposed on user authentication by using the Enhanced Elliptic Curve Cryptography. This is a password-less mechanism to authenticate users. The proposed work attains satisfactory results by proper resource utilization. An experimental result shows that proposed work exhibits better performance by improving the turnaround time and maximizing the CPU utilization and providing secured access to data.

NetVision: On-Demand Video Processing in Wireless Networks

The vast adoption of mobile devices with cameras has greatly contributed to the proliferation of the creation and distribution of videos. For a variety of purposes, valuable information may be extracted from these videos. While the computational capability of mobile devices has greatly improved recently, video processing is still a demanding task for mobile devices. We design an on-demand video processing system, NetVision , that performs distributed video processing using deep learning across a wireless network of mobile and edge devices to answer queries while minimizing the query response time. However, the problem of minimal query response time for processing videos stored across a network is a strongly NP-hard problem. To deal with this, we design a greedy algorithm with bounded performance. To further deal with the dynamics of the transmission rate between mobile and edge devices, we design an adaptive algorithm. We built NetVision and deployed it on a small testbed. Based on the measurements of the testbed and by extensive simulations, we show that the greedy algorithm is close to the optimum and the adaptive algorithm performs better with more dynamic transmission rates. We then perform experiments on the small testbed to examine the realized system performance in both stationary networks and mobile networks.

A system for real-time intervention in negative emotional contagion in a smart classroom deployed under edge computing service infrastructure

Literature has indicated that negative emotions may lead students to disengagement in teaching activities. Furthermore, the contagion of negative emotion is similar to infectious disease diffusion that drives more students into negative emotions. However, few methods have been brought forward to intervene in negative emotional contagion in real time, and most of them are limited to interventions of teachers, which are often not timely and even cause students to resist. Intervention in negative emotional contagion in classroom imposes several fundamental challenges on model and system design. In this paper, we address these issues from the following three aspects: (1) to design an emotional contagion model for classroom scene to locate the source of negative emotional contagion; (2) to develop deep learning-based visual emotion recognition system to recognize emotions of all students in the classroom; (3) to design and deploy the emotion recognition system as an edge computing-based service for minimizing response time to achieve multi-person emotional recognition and intervene in real time. We have applied the system to real-world classroom. Our results have shown that the system achieved two objectives: (1) reducing the number of students with negative emotions; (2) reducing response time to achieve real-time recognition and intervention. Meanwhile, this work provides a new perspective on research into emotional contagion in the classroom and smart education.

A longitudinal study of pupillary light reflex in 6- to 24-month children

Pupillary light reflex (PLR) is an involuntary response where the pupil size changes with luminance. Studies have shown that PLR response was altered in children with autism spectrum disorders (ASDs) and other neurological disorders. However, PLR in infants and toddlers is still understudied. We conducted a longitudinal study to investigate PLR in children of 6–24 months using a remote pupillography device. The participants are categorized into two groups. The ‘high risk’ (HR) group includes children with one or more siblings diagnosed with ASDs; whereas the ‘low risk’ (LR) group includes children without an ASD diagnosis in the family history. The participants’ PLR was measured every six months until the age of 24 months. The results indicated a significant age effect in multiple PLR parameters including resting pupil radius, minimal pupil radius, relative constriction, latency, and response time. In addition, the HR group had a significantly larger resting and minimal pupil size than the LR group. The experimental data acquired in this study revealed not only general age-related PLR changes in infants and toddlers, but also different PLRs in children with a higher risk of ASD.

Design and Testing of a Low Voltage Solid-State Circuit Breaker for a DC Distribution System

In this study, a low voltage solid-state circuit breaker (SSCB) was implemented for a DC distribution system using commercially available components. The design process of the high-side static switch was enabled through a voltage bias. Detailed functional testing of the current sensor, high-side switch, thermal ratings, analog to digital conversion (ADC) techniques, and response times of the SSCB was evaluated. The designed SSCB was capable of low-end lighting protection applications and tested at 50 V. A 15 A continuous current rating was obtained, and the minimum response time of the SSCB was nearly 290 times faster than that of conventional AC protection methods. The SSCB was implemented to fill the gap where traditional AC protection schemes have failed. DC distribution systems are capable of extreme faults that can destroy sensitive power electronic equipment. However, continued research and development of the SSCB is helping to revolutionize the power industry and change the current power distribution methods to better utilize clean renewable energy systems.

Enhancement of the Dynamic Computation-Offloading Service Selection Framework in Mobile Cloud Environment

In the era of cloud computing, any mobile device can augment its capabilities by using Cloud computation service. There are different services provided by different cloud service providers. The mobile device has to access the cloud service with minimum response time. So many a times, instead of a distant cloud, nearest cloudlet is chosen to access the service. But according to the mobility of the user, choosing the right service provider is a herculean task. Hence this paper suggests a framework to choose a cloudlet service provider in a multi-user computation offloading environment and accommodate the service that is adaptive based on the movement of the mobile device. This paper defines a framework which comprises of basically two components. The foremost one is Fuzzy KNN component which classifies the mobile device based on the access range of the device with a nearby cloudlet. The later component provides a dynamic service depending on the changes in the mobile device location. The framework exploits Fuzzy K nearest neighbour (KNN) and Hidden Markov Model to enhance the Dynamic computation-offloading service selection (EDCOSS) framework. The EDCOSS framework is analysed and tested in a simulation environment to verify the efficiency of the framework in terms of convergence of the algorithm towards computation cost with respect to different number of clients and communication channels.

A Proficient Approach for Load Balancing in Cloud Computing-Join Minimum Loaded Queue

Cloud load balancing is done to persist the services in the cloud environment along with quality of service (QoS) parameters. An efficient load balancing algorithm should be based on better optimization of these QoS parameters which results in efficient scheduling. Most of the load balancing algorithms which exist consider response time or resource utilization constraints but an efficient algorithm must consider both perspectives from the user side and cloud service provider side. This article presents a load balancing strategy that efficiently allocates tasks to virtualized resources to get maximum resource utilization in minimum response time. The proposed approach, join minimum loaded queue (JMLQ), is based on the existing join idle queue (JIQ) model that has been modified by replacing idle servers in the I-queues with servers having one task in execution list. The results of simulation in CloudSim verify that the proposed approach efficiently maximizes resource utilization by reducing the response time in comparison to its other variants.

Биосенсоры для исследования активности промоторов и шаперонов в клетках Bacillus subtilis

Plasmids to determine the activities of promoters in Bacillus subtilis cells have been constructed. Plasmids contain constitutive (PfbaA, PymdA_mut3, PymdA) and inducible (PxylA, PxylA-cre) promoters of different activities and genes under their control encoding reporter proteins with different thermostability and structure. The following main parameters were measured: maximal response, threshold concentration, minimal response time and the effect of catabolite repression for the inducible promoters. The activities of the constitutive promoters were compared. It was shown that the activity of the inducible PxylA promoter at a D(+)-xylose concentration of 1% was more than 100-fold higher and that of the PxylA-cre promoter was about 80-fold higher than the control level after 2 h of incubation. However, PxylA-cre is tightly closed and sensitive to glucose repression. The constructed lux sensors containing bacterial luciferases as reporter proteins were used to assess the influence of DnaKJE and trigger factor (TF) molecular chaperones on the level of synthesis of active enzymes. It was shown that the absence of both of the above chaperones in B. subtilis cells led to a significant decrease in the synthesis of the native thermolabile Photobacterium leiognathi luciferase, while the lack of TF increased the activity of the thermostable Photorhabdus luminescens luciferase by about 2 times as compared to wild type Bacillus subtilus 168. Erroneous translation under the streptomycin action and in the absence of both DnaKJE and TF chaperones significantly suppressed the synthesis of both luciferases. Bacillus subtilis, bacterial luciferase, biosensor, bioluminescence, promoter, chaperone, Trigger Factor, DnaKJ The work of I. Manukhov was supported by the Russian Science Foundation under the grant 20-16-00088. E. Gnuchikh's research on the design of lux-biosensors was partially funded by the Russian Foundation for Basic Research (project No. 20-34-70132).

A Tutorial and Review Discussion of Modulation, Control and Tuning of High-Performance DC-DC Converters Based on Small-Signal and Large-Signal Approaches

Many commercial controller implementations for dc-dc converters are based on pulse-width modulation (PWM) and small-signal analysis. Increasing switching frequencies, linked in part to wide bandgap devices, provide the opportunity to increase operating bandwidth and enhance performance. Fast processors and digital signal processing offer new computational techniques for power converter control. Conventional control techniques rarely make full use of operating capability. The objectives of this paper are to present an overview and link to literature on conventional modulation and control techniques for hard-switched dc-dc converters, identify performance limits associated with conventional small-signal-based design, discuss geometric control approaches, and compare strategies for control tuning. The discussion shows how current mode controls have alternative state feedback implementations, and describes unusual opportunities for large-signal control tuning. Considerations for minimum response time are described. Comparisons among tuning methods illustrate how geometric controls can achieve order of magnitude dynamic performance increases. The paper is intended as a baseline tutorial reference for future work on power converter control.

Redundant Iaas Cloud Selection With Consideration Of Multi Criteria Decision Analysis

Due to the rapid growth of dependency over Mobile cloud system, the demand of high computational system increases rapidly. Cloud computing gives the flexibility to users to use high computation system at nominal cost and scalability in dynamic and on demand fashion. The term offloading has attracted the researcher to obtain the highly capable cloud system with certain limitations such as minimum processing and communication time, make span, minimum operational cost. Offloading of data and application have definite positive keynotes such as it can extend the battery life of IOT devices also it is suitable for critical events (events those require minimum response time). In today, numerous cloud services providers are offering customized services, they are dedicated to fulfill the demands of user with negotiable service level agreement. But due to the inherent uncertainty involved in human judgment and lack of learning capacity, a dynamic cloud selection and decision model is required to evaluate the user preferences. That can recommend an optimal and redundant cloud system from the available pool of cloud service providers. Resolving of uncertainties and ambiguity in human’s decision are solved through fuzzy set theory. In this paper, an optimal and redundant cloud selection model has been presented on the basis of multi criteria decision analysis under consideration. Weighted Sum Model, Fuzzy Analytic Hierarchy Process and Fuzzy Revised Analytic Hierarchy Process are evaluated on 10 different criterions. Overall the outranking result for the considered datasets is similar, while the computation power of AHP method is ideally superior with comparison to revised AHP method.

Improving Predictability of User-Affecting Metrics to Support Anomaly Detection in Cloud Services

Anomaly detection systems aim to detect and report attacks or unexpected behavior in networked systems. Previous work has shown that anomalies have an impact on system performance, and that performance signatures can be effectively used for implementing an IDS. In this paper, we present an analytical and an experimental study on the trade-off between anomaly detection based on performance signatures and system scalability. The proposed approach combines analytical modeling and load testing to find optimal configurations for the signature-based IDS. We apply a heavy-tail bi-modal modeling approach, where "long" jobs represent large resource consuming transactions, e.g., generated by DDoS attacks; the model was parametrized using results obtained from controlled experiments. For performance purposes, mean response time is the key metric to be minimized, whereas for security purposes, response time variance and classification accuracy must be taken into account. The key insights from our analysis are: (i) there is an optimal number of servers which minimizes the response time variance, (ii) the sweet-spot number of servers that minimizes response time variance and maximizes classification accuracy is typically smaller than or equal to the one that minimizes mean response time. Therefore, for security purposes, it may be worth slightly sacrificing performance to increase classification accuracy.

CRIME: Input-Dependent Collaborative Inference for Recurrent Neural Networks

The excellent accuracy of Recurrent Neural Networks (RNNs) for time-series and natural language processing comes at the cost of computational complexity. Therefore, the choice between edge and cloud computing for RNN inference, with the goal of minimizing response time or energy consumption, is not trivial. An edge approach must deal with the aforementioned complexity, while a cloud solution pays large time and energy costs for data transmission. Collaborative inference is a technique that tries to obtain the best of both worlds, by splitting the inference task among a network of collaborating devices. While already investigated for other types of neural networks, collaborative inference for RNNs poses completely new challenges, such as the strong influence of input length on processing time and energy, and is greatly unexplored. In this article, we introduce a Collaborative RNN Inference Mapping Engine (CRIME), which automatically selects the best inference device for each input. CRIME is flexible with respect to the connection topology among collaborating devices, and adapts to changes in the connections statuses and in the devices loads. With experiments on several RNNs and datasets, we show that CRIME can reduce the execution time (or end-node energy) by more than 25 percent compared to any single-device approach.

Tailoring oxygen sensing characteristics of Co3O4 nanostructures through Gd doping

Detection of oxygen plays an important role in food industry, medicine and controlling automotive exhaust. Here, we have developed a Co3O4 nanoparticle-based oxygen gas sensor. Effect of Gadolinium (Gd) doping on oxygen sensing is investigated using the variation in electrical resistance method which clearly reveals improvement in the sensitivity with the increase in Gadolinium doping in Co3O4. X-ray diffraction (XRD), Scanning electron microscopy (SEM), Transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) studies were systematically performed, to understand the effect of the morphology and crystallographic phase on the level of sensitivity with Gadolinium doping. XRD studies reveal a distortion in the Co3O4 lattice as the lattice parameters increase with increasing Gd doping. SEM and TEM show a change in particle shape and size with Gd doping. Presence of both Co+2 and Co+3 oxidation states were confirmed by XPS. Gd doping, up to 6%, improved the gas sensing response of Co3O4 nanoparticles which is attributed to the decrease in particle size and an increase in oxygen adsorption with Gd doping. Minimum response time of 4 s, 6 s, 10 s and 14 s were observed for pure Co3O4 in 1%, 2%, 3% and 4% oxygen environment respectively. Pure Co3O4 nanoparticles also showed minimum recovery time which was 6 s, 7 s, 9 s and 11 s in 1%, 2%, 3% and 4% oxygen environment respectively. All the prepared compositions were selective to oxygen at 240 °C.

Supporting group decision makers to locate temporary relief distribution centres after sudden-onset disasters: A case study of the 2015 Nepal earthquake

In the humanitarian response, multiple decision-makers (DMs) need to collaborate in various problems, such as locating temporary relief distribution centres (RDCs). Several studies have argued that maximising demand coverage, reducing logistics costs and minimising response time are among the critical objectives when locating RDCs after a sudden-onset disaster. However, these objectives are often conflicting and the trade-offs can considerably complicate the situation for finding a consensus.To address the challenge and support the DMs, we suggest investigating the stability of non-dominated alternatives derived from a multi-objective model based on Monte Carlo Simulations. Our approach supports determining what trade-offs actually matter to facilitate discussions in the presence of multiple stakeholders. To validate our proposal, we extend a location-allocation model and apply our approach to an actual data-set from the 2015 Nepal earthquake response. Our analyses show that with the relative importance of covering demands ≤0.4, the trade-offs between logistics costs and response time affects the numbers and locations of RDCs considerably. We show through a small experiment that the outputs of our approach can effectively support group decision-making to develop relief plans in disasters response.

Load balance based workflow job scheduling algorithm in distributed cloud

As the scale of the geo-distributed cloud increases and the workflow applications become more complex, the system operation is more likely to cause the waste of resources and excessive energy consumption. In this paper, a workflow job scheduling algorithm based on load balancing is proposed to efficiently utilize cloud resources. Firstly, the execution time of the jobs on the cloud is estimated based on the state of the cloud. Then, a queuing model is established for each cloud to minimize the total response time of the system. Finally, the job scheduling problem in a geo-distributed cloud can be transformed into the minimum system response time problem. Moreover, a workflow task scheduling algorithm based on the shortest path algorithm is proposed to minimize all task completion time and energy consumption. Firstly, the directed acyclic graph (DAG) of the tasks can be converted into the hypergraph according to the execution order of the tasks. Then, the k-path hypergraph partition is performed with the balance of the hypergraph. Finally, the Dijkstra shortest path algorithm is used to find the optimal task scheduling strategy which is performed on each hypergraph partition. The experimental results indicate that our proposed workflow scheduling method can effectively utilize cloud resources and reduce system energy consumption. Moreover, the applicability of the proposed effective scheduling strategy is shown in the scenarios of new media live video application.

Uncertain demand estimation with optimization of time and cost using Facebook disaster map in emergency relief operation

When a disaster disrupts a society within a moment without a little warning, the living people of those areas face the deprivation due to demolition. Facebook, one of the most popular social media plays a vital role in response to the victims. The authority of Facebook has launched safety check feature to know about the requirement after the disruption of disaster and based on the information, a intensity factor is defined for requirement of relief products in this research work. Estimating the amount of relief products through intensity measure, our research work has introduced a mathematical model for initiation of humanitarian logistic operation plan. The research has focused on two objective functions through the mathematical model which are minimization of total cost and response time. The model with two objective functions is converted to a equivalent compromise model with neutrosophic compromise programming approach. Deterministic and non-deterministic both algorithms are implemented in the solution process of the compromise mathematical model. In deterministic approach, mathematical model is varified with different methods to obtain compromise results. For non-deterministic approach, a genetic algorithm is proposed to solve the model. The model is experienced with a numerical example and hereby statistical investigation is performed considering different dimension varying the parameters of the model.

A recursive learning technique for improving information processing through message classification in IoT–cloud storage

Processing and accessing distributed information is a prominent requirement for Internet of Things (IoT) in supporting business and consumer applications to improve accessibility. As the volume of information is being stored and processed is hefty, message classification is challenging in a mobile environment. This also results in prolonged processing delays and backlogs. In order to bridge the gap between message classification and request processing, this paper proposes a classification technique that operates on the basis of request-prioritized recursive learning for ease of message identification and service mapping. This learning technique predicts the type of information and its attributes through intensive learning and services them based on priority to minimize retrieval time. The priority of message servicing relies on the error obtained during the learning process. Despite an increasing number of user requests, attributes associated with each are bundled independently to provide an instant response. Prioritization accounts for the minimum number of error states while learning a message with different attributes to curtail prolonged response time. Error in the learning process is evaluated through a numerical analysis for different learning scenarios of the classified messages. The proposed learning-based access and retrieval technique were analyzed using the metrics of request backlogs, response time, caching delay, and the rate of utilization. The results of experiments verified the effectiveness of the proposed technique in terms of minimizing response time, backlogs, and caching delays, and improving utilization.

Dynamic Load Balancing of Cloud Data Center Traffic Based on Software-Defined Networking

Cloud computing has grown rapidly in the last decade, where many tenants are using various cloud applications. Cloud computing users expect and demand to run their applications with the highest performance, reliability, and best quality of service. So, load balancing techniques are essential components and widely used in the cloud data center network to manage users’ requests and distribute applications traffic among available data center resources. Software-defined networking is a highly flexible network architecture that automates network configuration using a centralized controller to overcome the traditional network limitations and manual configuration for every network device. This paper proposes a dynamic load balancing technique using software-defined networking for the cloud data center that can handle different application types in real-time based on traffic type and the required quality of service. The proposed method aims to bridge the gap between application requirements and the resources in the cloud data center by choosing the best path for data transmission and selecting the best performance server to process the users' requests according to traffic type for efficient resources’ utilization, minimizing response time, and maximizing throughput. The simulation results show that the proposed method enhances the performance of the running applications’ and utilizes the data center resources efficiently compared with the current load balancing techniques.

Parallel Offloading in Green and Sustainable Mobile Edge Computing for Delay-Constrained IoT System

Currently, the Internet of Things (IoT) solutions are playing an important role in numerous areas, especially in smart homes and buildings, health-care, vehicles, and energy. It will continue to expand in various fields in the future. However, some issues limit the further development of IoT technologies. First, the battery-powered feature increases the maintenance cost of replacing batteries for IoT devices. Second, existing Cloud-IoT frameworks are not able to cope with emerging delay-constrained applications in the IoT system due to its centralized mode of operation and the considerable communication delay. Existing studies neither satisfy the demand for the quick response in time-constraint IoT applications nor fundamentally solving the problem of energy sustainability. Therefore, this paper studies the problem of energy sustainability and timeliness in IoT system. Based on Energy Harvesting Technologies (EHT), the Green and Sustainable Mobile Edge Computing (GS-MEC) framework is proposed to make IoT devices self-powered by utilizing the green energy in the IoT environment. In this framework, we formulate the problem of minimizing response time and packet losses of tasks under the limitation of energy queue stability to improve the timeliness and reliability of task processing. Additionally, the dynamic parallel computing offloading and energy management (DPCOEM) algorithm is designed to solve the problem based on the Lyapunov optimization technology. Finally, theoretical analysis demonstrates the effectiveness of the proposed algorithm, and the numerical result of simulation shows that the average performance of the proposed algorithm is an order of magnitude better than state-of-the-art algorithms.