Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Management: Cost Optimal Scheduling with Resource Replication in Cloud Environment

Coping with uncertainty is a challenging and complex problem particularly in hybrid cloud environments—private cloud plus public cloud. For providing an efficient way of load balancing the paper proposes a time scheduler algorithm for the cloud computing. The scheduling is done through two phases. The first phase is direct assignment in the best case and worst condition in the second time in a time slicing phase. To describe the proper methodology the paper first gives a suitable introduction followed by literature review. Then a proposed method is described with the simulation part followed. VM Replicated depending upon the task duration time. Task is assign to replicated VM, divided on duration time of tasks, completion in minimal time. Cost Expansive is limited. Cloud Provider gets more VM cost beneficial. Lowest resources and efficient throughput Job submits to VM based on Cost optimal Algorithm. Here we added VM Replication; this paper gives advancement in the cloud computing in the matter of high processing speed. The process has a least calculation over load on the load
How Effective is Continuous Glucose Monitoring? A Comparison of Different Random-Effect Meta-Analysis Techniques

Meta-analysis is a statistical technique widely used to combine data from different studies to provide an estimate of the overall effect size of a particular intervention. To assess the impact of heterogeneity, there are several statistical methods currently used to pool the results of individual study results. When the included studies have significant variation, it is important to use a random-effects model. While the DerSimonian and Laird is the most frequently used random-effects meta-analysis method, numerous alternative methods exist, but the evidence on the impact of the use of these methods on the results is relatively limited. In this study, we consider twelve random-effect model methods by performing meta-analysis of the efficacy of continuous glucose monitoring (CGM) on glycemic control in diabetic patients. This methodology allowed us to illustrate the impact of the choice of the statistic technique on the results of the analysis. Results demonstrated that, different methods can result in different heterogeneity estimates. The broad implication of the findings of the study is more evident when using the results of meta-analyses in health economic modelling studies to inform reimbursement decision-making for healthcare interventions.
Reducing In-Vehicle Communication Overload and Enhancing Efficiency in Autonomous and Electrical Vehicles

The rise of autonomous driving and electric vehicles, driven by advancements in technology and new features, has led to an increased demand for faster in-vehicle communication. This increased communication load, coupled with issues like communication delays, data loss, tolerance problems, communication lock-ups, and electrical interference resulting from high busload, further compounded the system's complexity. In this context, this paper aims to examine the solutions for increased communication loads, and suggests a transition to new generation communication protocols, network structures and advanced Electronic Control Unit (ECU) structures. Specifically, the suggested solutions provide the advantage of using a smaller number of advanced ECUs. This in turn reduces the complexity of the network by reducing communication nodes, as well as simplifying energy management and wiring. The results of the study provide rational solutions to communication issues for the future success of sustainable, safer and more efficient autonomous and electric vehicles.
Threshold impact for CNN partitioning algorithm in the Versatile Video Coding standard

Versatile Video Coding (VVC) standard was published in July 2020 by the Joint Video Experts Team (JVET). The VVC coding scheme has been enhanced by introducing new coding technologies compared to HEVC codec scheme. The added tools improved the Rate-Distortion (RD) performance while increasing the coding complexity. The Quadtree with Nested Multi-Type Tree (QTMT) partitioning module is one of the most complex improvement. Therefore, we suggested a low complexity QTMT algorithm based on the Convolution Neural Network (CNN). The proposed method adopted bi-threshold values in order to determine the partitioning decision according to the predicted probabilities. In this paper, a study of the bi-threshold values impact on both coding efficiency and complexity reduction is presented using the VVC reference software VTM3.0 in order to develop a method that may automatically select the appropriate bi-threshold value.
9.6-THz Single Fibre Amplifier O-Band Coherent DWDM Transmission

We experimentally demonstrate greater than C-band coherent transmission over a bandwidth of 9.6-THz using simple back-ward-pumped bismuth-doped fibre amplifiers in the O-band. The signal-to-noise ratio (SNR) of coherent dense wavelength-division-multiplexed signals is evaluated after one and three 45-km spans of installed fibre at 30 and 40 GBd as a function of wavelength. The penalty at the zero-dispersion wavelength from nonlinear noise is measured and in the broadband transmission regime, the penalty is insignificant. The measured SNR is converted to achievable information rate, and the impact of the number of equaliser taps is evaluated, showing that chromatic dispersion compensation is not needed, demonstrating the potential for energy savings in receiver-side signal processing.
Automatic Component Identification Based on Time Series Classification for Intelligent Devices

Advances in manufacturing technology have made it possible to replace some components anywhere to meet the needs of different functions with multi-function devices. However, remote servers are typically not informed when workers replace device components. In these scenarios, there is a mismatch in the data models between the device components and the servers. To this end, this study proposes an automatic component identification method based on time series classification for intelligent devices. First, the component identification problem is transformed into a time series classification problem. Then, some classifiers are trained based on the time series data generated by the models. In the next step, the classifiers match the collected data with the component models. Finally, the components are identified by a fusion decision, respectively. The experimental results show that the proposed method has a high component identification accuracy.
Research on Insulator Detection Method Based on Machine Vision

The detection of insulators is of critical significance because they are the main piece of hardware in overhead transmission lines and are crucial to the transmission and distribution process. However, in the actual test process, the complex environment and limited equipment measurement capabilities make it difficult for the previous target detection methods to meet the demands of insulators with rich and varied scales. For the purpose of above challenges in the field of insulator detection, a new insulator detection algorithm based on machine vision technology was proposed by using computer vision and image processing technology. Firstly, for enrich the insulator dataset, the mosaic data augmentation algorithm was used to improve the background complexity of the image to be detected. Secondly, in order to improve the problem of small dataset, a transfer learning strategy was added to the YOLOV4 network. Finally, the SE attention module was used to improve the performance of insulator identification. Experimental results show that the improved YOLOV4 network model has better results through data augmentation, transfer learning, and attention mechanism in the network, mAP reached 97%, compared with YOLOV4, mAP increased by 19%, and the network can better realize insulator detection.
Scalable biosensors using standard CMOS process

In this study, we present two biosensors, an Ion-Sensitive Field-Effect Transistor (ISFET) and a Capacitance sensor array, both fabricated via a standard 0. 5μm CMOS process. The integrated ISFET was comprehensively characterized and measured in a buffer solution across three pH variants (4, 7, and 10), revealing an impressive sensitivity of 35mV per pH unit for our CMOS-based ISFET. To counteract the prevalent issues of drift and mismatch, we have discussed applicable strategies to mitigate them. We utilize the ISFETs to monitor breast cancer cell growth showcasing their use for biomedical application. Moreover, we have introduced a fringe-based capacitance sensor array equipped with readout circuits, which measures the capacitive coupling of cells, thereby facilitating real-time monitoring of cells with high spatial and temporal resolution. Both the ISFET and the capacitance array are integrated on a CMOS substrate, opening avenues for large-scale biosensor integration.
Design of Antenna for 5G Applications at 80 GHz

In this paper, we will present a patch antenna operating in 80 GHz frequency for 5G applications. We will perform a parametric study considering the influence of the different geometrical (dimensions) and physical (substrate…) parameters to improve the antenna at 80GHz to see the impact of these parameters on the characteristics of the designed antenna. There are several specialized electromagnetic simulators for microwave design. Among these simulators, we will use CST for our study. Good results in terms of S11 (-34dB), gain (7dB), wide band in the order of 2 GHz and efficiency (60%) are achieved
Exploring Urban Residents’ Spatiotemporal Travel using Coupled Optimal Sequence Alignment and Frequent Pattern Mining

This article explores the method of combining sequence analysis and data mining to explore spatiotemporal behavior sequence patterns based on fine-grained spatiotemporal activity sequence data of urban residents. Obtain activity log data from 1000 households through a questionnaire survey. Utilize the most effective sequence alignment algorithm to mine sequence patterns in spatiotemporal behavior sequence data, and identify potentially useful behavior patterns for each type of sequence pattern through frequent pattern mining. The experimental results indicate that the combination of sequence alignment method and frequent pattern mining has been successfully applied in the analysis of urban residents’ behavior patterns, making up for the shortcomings of traditional data analysis methods. This provides a new perspective for the analysis of complex spatiotemporal behavior data.
Heartbeat Classification of Arrhythmia using Hybrid Features Extraction Techniques

An electrocardiogram (ECG) is the noninvasive method for arrhythmia recognition used to monitor the heart electrical activity. The hidden information present in ECG data and the irregularity of the heartbeat is difficult to determine. Hence, this hidden information can be used to detect abnormalities. This paper proposes hybrid feature extraction techniques for raw ECG heart beat classification. Many feature extraction techniques and machine learning algorithms are used f that combined features of time domain, frequency domain and time frequency domain gives the best accuracy. These features were combined to create a hybrid features with huge amount of data. In addition, some of the extracted features have been reduced using feature reduction techniques. The suggested method was accomplished to be able to acquire better features of the ECG data. This approach has the ability to recognize the discriminant features between heartbeats classes of the input signals using two types of classifiers, namely, the neural network methods(NN), and support vector machine (SVM-RBF) with tenfold cross-validation technique. The Association for Advancement of Medical Instrumentation analyzed five types of beat classes of arrhythmia such as: non-ectopic beats (N), supra-ventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F) and unclassifiable and paced beats (U). Our proposed method is able to classify the N, S, V, F and U arrhythmia classes with high accuracy (98.95%) but with (39) feature dimension.
High-Speed Waveguide-coupled Photodiode Arrays on Thin-film Lithium Niobate Platform

The study presents a demonstration of InP/InGaAs modified uni-traveling carrier (MUTC) photodiode (PD) arrays heterogeneously integrated on the thin-film lithium niobate platform. To divide the input optical power evenly, a 1×N multimode interferometer (MMI) is employed to feed the PDs through N waveguides. The PD arrays, consisting of 2, 3, and 4 PDs, exhibit 3-dB bandwidths of 82 GHz, 67 GHz, and 62 GHz, respectively. The measured output power of the 1 ×4 PD array reaches -2.3 dBm at 30 GHz.
RAVAS: Interference-Aware Model Selection and Resource Allocation for Live Edge Video Analytics

Numerous edge applications that rely on video analytics demand precise, low-latency processing of multiple video streams from cameras. When these cameras are mobile, such as when mounted on a car or a robot, the processing load on the shared edge GPU can vary considerably. Provisioning the edge with GPUs for the worst-case load can be expensive and, for many applications, not feasible. In this paper, we introduce RAVAS, a Real-time Adaptive stream Video Analytics System that enables efficient edge GPU sharing for processing streams from various mobile cameras. RAVAS uses Q-Learning to choose between a set of Deep Neural Network (DNN) models with varying accuracy and processing requirements based on the current GPU utilization and workload. RAVAS employs an innovative resource allocation strategy to mitigate interference during concurrent GPU execution. Compared to state-of-the-art approaches, our results show that RAVAS incurs 57% less compute overhead, achieves 41% improvement in latency, and 43% savings in total GPU usage for a single video stream. Processing multiple concurrent video streams results in up to 99% and 40% reductions in latency and overall GPU usage, respectively, while meeting the accuracy constraints.
Low-Loss Wavelength-Selected Tunable Optical Delay Lines for Microwave Photonic Signal Processing

We propose and demonstrate a programmable low-loss 16-channel silicon-based microwave photonic signal processor using elliptical microrings and delaylines. The ellipital microrings offers flat-top filtering response to select/combine the carrier wavelength of the input optical signal. The following sixteen-channel delaylines are realized by ultra-low-loss broadened waveguides and low-phase-error Mach-Zehnder interferometer for 3.2 ps time-delay step. The overall insertion loss of the processor including the fiber-to-chip coupling loss is 10 dB. This programmable silicon photonic processor is demonstrated successfully to verify several microwave photonic signal processing functionalities: tunable delay line, beam beamforming and tunable radio-frequency filtering.
Poster: Synchronizing Devices with Minimal Data Exchange and Applications for Graph-Based SLAM

For synchronization purposes, most applications require data to be transferred onto a cloud or shared with other edge devices. Transferring data between edge devices using wireless connectivity consumes power and bandwidth; therefore it is crucial to transmit a minimal amount of data. This poster presents an efficient method and preliminary analysis for minimizing data flow between edge devices and experimental results on the amount of transmitted data with radio use of $e$(-Nm), where n is the maximum difference between wake-up times and m is the number of devices that wake up at arbitrary time points. Besides the amount of data, our approach also focuses on the radio use of each device.
AgileShard: Turning the Sharded Blockchain into a Real-Time Transaction Processing System

Blockchain, as an emerging transaction processing system, suffers from low throughput and high latency. Sharded blockchains can significantly increase throughput by dividing nodes into groups (i.e., shards) to process disjoint transactions in parallel. However, the diverse latency requirements of transactions in current sharded blockchains are not well met. Three challenges prevent the sharded blockchain from becoming a real-time transaction processing system, namely, the static block size, the first-come-first-served transaction packing strategy, and the load imbalance. Therefore, this paper proposes 3 methods to help turn the sharded blockchain into a real-time transaction processing system. First, we propose an inter-shard dynamic block size negotiation method that enables shards to adaptively determine the globally optimal block size based on the deadlines of pending transactions. Then, we propose a DAG-based transaction packing method for reducing the number of deadline violations and improving parallelism. Finally, we propose a minimum-cost-flow-based shard reconfiguration method to address load imbalance. Under real datasets on Ethereum, experimental results show that AgileShard using the above three methods can effectively improve the transaction deadline satisfaction rate.
Template-Aware Live Migration of Virtual Machines

One of the key challenges of edge computing is working with a limited amount of resources available at the edge, especially memory and bandwidth. Virtual Machine (VM) Templating is a technique to start multiple VM instances quickly from a shared pre-configured read-only image (or template). The new VM instances share the memory of the template in a copy-on-write (COW) manner. In edge computing platforms, VM templating can help to reduce the collective memory footprint and deployment time of multiple VMs. Live migration of VMs can also improve task placement on edge nodes for latency reduction, service availability, and cost-effectiveness. However, existing live migration techniques fail to maintain memory sharing among multiple templated VMs that are migrated to a common destination. Consequently, identical pages at the source are replicated several times at the destination, increasing memory pressure on the destination node, network traffic during migration, and total migration time. Lack of templating awareness can also trigger migration failure if the destination lacks sufficient memory to accommodate the increased memory footprint. To address this shortcoming of live migration, we introduce Template-aware Live Migration (TLM), which preserves preexisting COW memory sharing between templated VMs that are migrated to a common destination machine. Specifically, TLM ensures that multiple virtual pages from different VMs that are mapped to the same template page at the source are mapped to the same page at the destination. We implement TLM on the QEMU/KVM virtualization platform and demonstrate a significant reduction in memory footprint, shorter migration time, and reduced network traffic.
An Indirect Verification Approach for Dynamic Control of Wheeled Mobile Robots

It is difficult to obtain a dynamic model for commercial wheeled mobile robots (WMRs) to achieve advanced control access, because such dynamic models are complex in different situations and the commercial provider is unwilling or difficult to provide. This makes robot control in academic research based on dynamic models very difficult. In this paper, we first propose a dynamic model for WMRs based on the physical principles and then establish a model verification approach via a simulation tool Simscape and the kinematic model. In fact, a Simscape simulation model is established with reference to the real WMR system, which can not only be used for the parameter identification of the proposed theoretical dynamic model, but also can derive the corresponding control signals for the real system kinematic model. Thus, the mutual verification of the three models can be achieved through experiments of the simulation model and the real system. Several typical experimental results show that the proposed approach can achieve the dynamic model verification effectively. With this approach, the acquisition of dynamic models and in-depth research based on commercial WMRs will become more convenient.
Mineralogy, chemistry, and fluid-aided evolution of the Pea Ridge Fe oxide-(Y + REE) deposit, southeast Missouri, USA

The Kiruna-type Pea Ridge iron oxide-apatite (IOA) deposit is hosted by a sequence of 1.47 Ga rhyolite tuffs of the St. Francois Mountains, southeast Missouri, USA. It consists of a series of altered zones composed mainly of amphibole, magnetite, hematite, and quartz, together with the presence of several rare earth element (Y + REE)-rich breccia pipes. In many cases, the fluorapatite within these zones is rich in inclusions of monazite, iron oxide, and quartz inclusions, plus minor xenotime. Monazite and minor xenotime are also found intergrown as inclusions in the fluorapatite, as well as in surrounding recrystallized magnetite and hematite in the magnetite ore. Monazite and xenotime typically occur as inclusions within both oxides. Monazite-(Ce) and xenotime-(Y) are both relatively poor (<2 wt %) in ThO2 and UO2. No significant compositional differences exist in the (Y + REE) chemistry between monazite and xenotime inclusions in fluorapatite compared to grains intergrown with magnetite and hematite, suggesting that these two REE-rich minerals are cogenetic. Monazite-xenotime geothermometry and geochronology of monazite inclusions in fluorapatite provide evidence that formation/remobilization of the (Y + REE) phosphates took place at ca. 50° to 400°C, approximately 5 to 10 m.y. after emplacement of the main iron oxide-phosphate orebody. Evidence from field relationships and fluid inclusion chemistry, together with the massive recrystallization and remobilization of fluorapatite, monazite, xenotime, and iron oxides at Pea Ridge, suggest a subvolcanic origin coupled with a strong metasomatic reworking of the IOA deposit.
High-performance Super twisting sliding mode MPPT control for photovoltaic system under partial shading conditions

The purpose of this work is to offer an improved maximum power point tracking (MPPT) approach for photovoltaic (PV) systems using the super-twisting algorithm. The proposed approach has the ability to track the MPP under adverse environmental circumstances such as large fluctuations of insolation and partial shade. In addition, this method minimizes the chattering effect and increases efficiency. The effectiveness of the proposed method is tested under changing solar radiation and compared to conventional sliding mode control and to perturb and observe(P&O) technique. MATLAB/Simulink is used to simulate the PV-MPPT system. The results indicate that the developed method has a better dynamic response than conventional sliding mode control. It can be used in photovoltaic generation systems.