Time-based Techniques Research Articles

Quantifying Digital Biomarkers for Well-Being: Stress, Anxiety, Positive and Negative Affect via Wearable Devices and Their Time-Based Predictions.

Wearable devices have become ubiquitous, collecting rich temporal data that offers valuable insights into human activities, health monitoring, and behavior analysis. Leveraging these data, researchers have developed innovative approaches to classify and predict time-based patterns and events in human life. Time-based techniques allow the capture of intricate temporal dependencies, which is the nature of the data coming from wearable devices. This paper focuses on predicting well-being factors, such as stress, anxiety, and positive and negative affect, on the Tesserae dataset collected from office workers. We examine the performance of different methodologies, including deep-learning architectures, LSTM, ensemble techniques, Random Forest (RF), and XGBoost, and compare their performances for time-based and non-time-based versions. In time-based versions, we investigate the effect of previous records of well-being factors on the upcoming ones. The overall results show that time-based LSTM performs the best among conventional (non-time-based) RF, XGBoost, and LSTM. The performance even increases when we consider a more extended previous period, in this case, 3 past-days rather than 1 past-day to predict the next day. Furthermore, we explore the corresponding biomarkers for each well-being factor using feature ranking. The obtained rankings are compatible with the psychological literature. In this work, we validated them based on device measurements rather than subjective survey responses.

Positioning by Multicell Fingerprinting in Urban NB-IoT Networks.

Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments.

A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model

Static risk analysis techniques (SRATs) use event graphs and risk analysis assessment models. Those techniques are not time-based techniques and hence are inadequate to model dynamic stochastic systems. This paper proposes a novel dynamic approach to model such stochastic systems using Dynamic Fault Trees (DFT). The proposed model is called Generic Dynamic Agent-Based Model (GDABM) for risk analysis. GDABM is built on top of the well-known Agent-Based Modeling and Simulation (ABMS) technique. GDABM can model the dynamic system agents in both nominal (failure-free) and degraded (failure) modes. GDABM shows the propagation of failure between system elements and provides complete information about the system’s configurations. In this paper, a complete detailed case study is provided to show the GDABM capabilities to model and study the risk analysis for such dynamic systems. In the case study, the GDABM models the risk analysis for a chemical reactor/operator and performs a complete risk analysis for the entire system. The GDABM managed to simulate the dynamic behavior of the system’s components successfully using Repast Simphony 2.0. Detailed agent behavioral modes and failure modes are provided with various scenarios, including different time stamps. The proposed GDABM is compared to a reference model. The reference model is referred to as the ABM model. GDABM has given very promising results. A comparison study was performed on three performance measures. The performance measures used are (1) Accuracy, (2) response time, and (3) execution time. GDABM has outperformed the reference model by 15% in terms of accuracy and by 27% in terms of response time. GDABM incurs a slightly higher execution time (13%) when compared to the ABM reference model. It can be concluded that GDABM can deliver accepted performance in terms of accuracy and response time without incurring much processing overhead.

A Robust and Accurate Indoor Localization Using Learning-Based Fusion of Wi-Fi RTT and RSSI.

Great attention has been paid to indoor localization due to its wide range of associated applications and services. Fingerprinting and time-based localization techniques are among the most popular approaches in the field due to their promising performance. However, fingerprinting techniques usually suffer from signal fluctuations and interference, which yields unstable localization performance. On the other hand, the accuracy of time-based techniques is highly affected by multipath propagation errors and non-line-of-sight transmissions. To combat these challenges, this paper presents a hybrid deep-learning-based indoor localization system called RRLoc which fuses fingerprinting and time-based techniques with a view of combining their advantages. RRLoc leverages a novel approach for fusing received signal strength indication (RSSI) and round-trip time (RTT) measurements and extracting high-level features using deep canonical correlation analysis. The extracted features are then used in training a localization model for facilitating the location estimation process. Different modules are incorporated to improve the deep model’s generalization against overtraining and noise. The experimental results obtained at two different indoor environments show that RRLoc improves localization accuracy by at least 267% and 496% compared to the state-of-the-art fingerprinting and ranging-based-multilateration techniques, respectively.

A density-aware probabilistic interest forwarding method for content-centric vehicular networks

Vehicular networks, compared to other wireless networks, face particular challenges due to the rapidly changing topology and intermittent connections. By eliminating the need to establish and maintain an end-to-end connection, Content-Centric Network (CCN) model has recently become an appropriate solution to meet the challenging demands of vehicular network communications. In this kind of network, the basic method of forwarding interest packets is flooding. This approach will result in excessive redundancy, serious contention, and collision to which it is referred as the broadcast storm problem. In this article, a probabilistic strategy is proposed to alleviate the impact of the broadcast storm on interest forwarding in content-centric vehicular networks. In this density-aware approach, each vehicle dynamically computes the probabilities based on the number of existing neighbors. A local density approximation method is presented, which uses the information provided by the newly modified Pending Interest Table (PIT) entries. Moreover, some time-based techniques are employed to give priority to potential forwarders. The simulation results indicate that the proposed work outperforms the basic CCN. With on average 40% lower network load and 65% fewer interests compared to the basic CCN, it shows only an overall 6% decrease in reachability.

Integration of Specular Reflector in a Dual Axis Solar Tracker

In the Philippines, the installation of solar panels on the rooftops of residential and commercial buildings has increased in popularity. Even if the country has high solar insolation, energy conversion is still inefficient since the solar panel arrays are not always facing in perpendicular to the sun. Technologies related to increasing the solar panel efficiency such as solar trackers are usually complex and expensive. This problem is addressed by designing a low cost dual axis solar tracker with the integration of specular reflector system. The dual axis tracker system can move the panel and the reflector assembly in a precise and controlled manner to ensure maximum output. This was achieved by using a LDR (Light Dependent Resistor) compass assembly, gear system, and stepper motor mechanism. To measure the solar panel output, a voltage sensing circuit was incorporated instead of using the existing optical and time-based techniques. The system performance was tested in both the controlled and the actual environments. Experimental data have shown that when the panel is perpendicular to the light source, the system yields maximum energy. The need for a tracking and planar reflector system is notably important when the light source moves away from the normal because this results to a significant drop of the panel’s average efficiency. The addition of the tracker alone had greater enhancement effect than with the specular reflector only. The tracker boosts the efficiency by 69.55% while the specular reflector boosts it by only 15.12%. Combining the tracker and the specular reflector yields the best result with a 92.33% boost on the average efficiency.

A 0.0021 mm21.82 mW 2.2 GHz PLL Using Time-Based Integral Control in 65 nm CMOS

Modern multicore processors employ multiple phase-locked loops (PLLs) to operate individual cores at a power-optimal frequency. This paper presents techniques to implement such PLLs in a small area. The area occupied by classical charge-pump-based analog PLLs is mostly due to the large loop filter capacitor needed to implement the integral control portion of type-II response. Digital PLLs (DPLL) can eliminate the capacitor by implementing the integral control in digital domain but their jitter performance is degraded by the quantization error introduced by DPLL building blocks such as a time-to-digital converter (TDC). We seek to combine the advantages of analog (no quantization error) and digital (small area) PLLs by implementing the integral control using time-based techniques. To this end, a ring oscillator-based integrator (ROI) is used to implement the integral control. ROI integrates its input and generates an output in the form of a pulse-width modulated (PWM) signal. While the ROI does not introduce quantization error, controlling the voltage controlled oscillator with the PWM signal introduces undesirable spurious tones. We propose to use a pseudo-differential ROI to mitigate these tones and achieve good jitter performance. Fabricated in 65 nm CMOS LP process, the prototype PLL occupies an active area of only 0.0021 mm2 and operates across a supply voltage range of 0.6 V to 1.2 V providing 0.4–2.6 GHz output frequencies. At 2.2 GHz output frequency, the PLL consumes 1.82 mW at 1 V supply voltage, and achieves 3.73 psrms integrated jitter. This translates to an FoMJ of −226.0 dB, which compares favorably with state-of-the-art designs while occupying the smallest reported active area.

The Acousto Ultrasonic Structural Health Monitoring Array Module (AUSAM+) for Damage Detection in Structures

An area of considerable interest within the aerospace community is the use of structurally-integrated transducers for detecting and monitoring damage in aircraft structures. Such Structural Health Monitoring (SHM) systems offer the possibility to provide non-destructive inspections on-demand and consequently provide a basis for condition-based maintenance on airframes. This approach potentially offers maintenance cost savings and possible improvements in performance when compared to current time-based techniques for detecting and monitoring structural damage. A wide-area damage detection technique under extensive investigation by researchers, on small-scale coupons to full-scale test articles, is acousto-ultrasonics (AU) using structurally-integrated piezoelectric transducers. For any SHM technique to be successfully applied to an operational aircraft in the field the SHM hardware needs to be fit for purpose i.e. easy to use, compact, portable, light, electrically and mechanically robust and provide reliable and accurate measurements. Additionally, in order for researchers to extend, demonstrate and validate AU based SHM techniques under various aerospace structural scenarios, instrumentation is required that is functionally flexible, expandable and relatively inexpensive. In order to facilitate the development, validation and implementation of AU based SHM in the aerospace community the Australian Defence Science and Technology Group (DST Group) has developed a compact device for AU excitation and interrogation, called the Acousto Ultrasonic Structural health monitoring Array Module (AUSAM+). The module, which has the footprint of a typical current generation smart phone, provides autonomous control of four send and receive piezoelectric elements, which can operate in pitch-catch or pulse-echo modes and can undertake electro-mechanical impedance measurements for transducer and structural diagnostics. Other key features include an ability to (1) accommodate larger transducer arrays by operating synchronously with other units, via an optical link; (2) cater for fibre optic sensing of acoustic waves with four intensity-based optical inputs; (3) measure temperature and strain; (4) be triggered externally; and (5) allow the users to easily access the full hardware functionality via a Matlab or Python hardware object, thus providing enormous flexibility for the creation of custom interfaces. This paper provides an overview of the system and its capabilities and demonstrates the efficacy of the system via a simple laboratory AU study on a flat plate.

Mitigating jamming attacks in mobile cognitive networks through time hopping

5G wireless networks will support massive connectivity mainly due to device-to-device communications. An enabling technology for device-to-device links is the dynamical spectrum access. The devices, which are equipped with cognitive radios, are to be allowed to reuse spectrum occupied by cellular links. The dynamical spectrum availability makes cognitive users switch between channels. Switching leads to energy consumption, latency, and communication overhead in general. The performance degrades even more when the network is under jamming attack. This type of attack is one of the most detrimental attacks. Addressing jamming while maintaining a desired quality of service is a challenge. While existing anti-jamming mechanisms assume stationary users, in this paper, we propose and evaluate countermeasures for mobile cognitive users. We propose two time-based techniques, which, unlike other existing frequency-based techniques, do not assume accessibility to multiple channels and hence do not rely on switching to countermeasure jamming. We achieve analytical solutions of jamming, switching, and error probabilities. Based on our findings, the proposed techniques out perform other existing frequency-based techniques. Copyright © 2016 John Wiley & Sons, Ltd.

Survey of WiFi positioning using time-based techniques

Estimating the position of mobile devices with high accuracy in indoor environments is of interest across a wide range of applications. Many methods and technologies have been proposed to solve the problem but, to date, there is no “silver bullet”. This paper surveys research conducted on indoor positioning using time-based approaches in conjunction with the IEEE 802.11 wireless local area network standard (WiFi). Location solutions using this approach are particularly attractive due to the wide deployment of WiFi and because prior mapping is not needed. This paper provides an overview of the IEEE 802.11 standards and summarizes the key research challenges in 802.11 time-based positioning. The paper categorizes and describes the many proposals published to date, evaluating their implementation complexity and positioning accuracy. Finally, the paper summarizes the state-of-the-art and makes suggestions for future research directions.

Optimised Migrate Virtual Machine Rejuvenation

Server virtualization is an essential component in virtualized software infrastructure such as cloud computing. Virtual machines are generated through a software called virtual machine monitor (VMM) running on physical servers. The risks of software aging caused by aging-related bugs affect both VM and VMM. As a result, service reliability degrades may generate huge financial losses to companies. This paper presents an analytic model using stochastic reward nets for timebased rejuvenation techniques of VMM and VM. We propose to manipulate the VM behavior while the VMM rejuvenation is according to the load on the system. Using a previous Petri net model of virtualized server, we performed an algorithm in order to optimize rejuvenation technique and achieve high availability. So we perform Migrate-VM rejuvenation or Warm-VM rejuvenation while there are current jobs in the system. Although Migrate-VM rejuvenation is better than Warm-VM rejuvenation in steady state availability, it can’t be always performed as it depends on the capacity of the other host. When the queue is empty and the virtual machine has no current jobs to serve, we propose to combine both VMM rejuvenation and VM rejuvenation. We show that the proposed technique can enhance the availability of VMs.

TD-AMS PROCESSING FOR VLSI IMPLEMENTATION OF LDPC DECODER

An Efficient analog to digital interface (TDC/DTC) is presented. In particular, we explore time-based techniques for data conversion, which can potentially achieve significant reductions in power consumption while keeping silicon chip area will be very small. On the basis of a generic mixed-signal system the scaling difficulties of analog and mixed-signal circuits based on a signal representation in the voltage domain are discussed for nanometer CMOS technologies. Easy to control and seamlessly embedded, were also low latency occur. Mainly applicant for LDPC implementation which is used for error correcting and image processing will be done. In gate level verilog hardware description language used for coding digital circuits using tool Xilinx ISE 10.1i and target family Spartan 3E,Device XC3S500, speed -5,package:FG320.The synthesized for the proposed digital circuits.

Long-Term Performance of Bridge Elements Using Integrated Deterioration Method Incorporating Elman Neural Network

Currently, probabilistic deterioration modeling techniques have been employed in most state-of-the-art Bridge Management Systems (BMSs) to predict future bridge condition ratings. As confirmed by many researchers, the reliability of the probabilistic deterioration models rely heavily on the sufficient amount of condition data together with their well-distributed historical deterioration patterns over time. However, inspection records are usually insufficient in most bridge agencies. As a result, a typical standalone probabilistic model (e.g. state-based or time-based model) is not promising for forecasting a reliable bridge long-term performance. To minimise the shortcomings of lacking condition data, an integrated method using a combination of state- and time-based techniques has recently been developed and has demonstrated an improved performance as compared to the standalone techniques. However, certain shortcomings still remain in the integrated method which necessities further improvement. In this study, the core component of the state-based modeling is replaced by an Elman Neural Networks (ENN). The integrated method incorporated with ENN is more effective in predicting long-term bridge performance as compared to the typical deterioration modeling techniques. As part of comprehensive case studies, this paper presents the deterioration prediction of 52 bridge elements with material types of Steel (S), Timber (T) and Other (O). These elements are selected from 94 bridges (totaling 4,115 inspection records). The enhanced reliability of the proposed integrated method incorporating ENN is confirmed.

Direct Electrochemistry of Uric Acid at Chemically Assembled Carboxylated Single-Walled Carbon Nanotubes Netlike Electrode

Carboxylated single-walled carbon nanotubes (SWCNT) chemically assembled on gold substrate was employed as netlike electrode to investigate the charge-transfer process and electrode process kinetics using uric acid as an example. The electrochemical behavior of uric acid in carboxylated SWCNT system was investigated using cyclic voltammetry, chronoamperometry, and single potential time-based techniques. The properties of raw SWCNT electrode were also studied for comparison purpose. Uric acid has better electrochemical behavior whereas ascorbic acid has no effective reaction on the carboxylated SWCNT electrode. Cyclic voltammograms indicate that the assembled carboxylated SWCNT increases more active sites on electrode surface and slows down the electron transfer between the gold electrode and uric acid in solution. The charge-transfer coefficient (alpha) for uric acid and the rate constant (k) for the catalytic reaction were calculated as 0.52 and 0.43 s(-1), respectively. The diffusion coefficient of 0.5 mM uric acid was 7.5 x 10(-6) cm2 x s(-1). The results indicate that electrode process in the carboxylated SWCNT electrode system is governed by the surface adsorption-controlled electrochemical process.

A new amperometric biosensor for hydrogen peroxide determination based on HRP-nanogold-PTH-nanogold-modified carbon paste electrodes

A new strategy for immobilization of horseradish peroxidase (HRP) has been developed by self-assembling gold nanoparticles to multiporous polythionine (PTH) film modified carbon paste interface. A thionine film was initially electropolymerized onto carbon paste interface in a mildly acidic thionine solution at a bias voltage of −1.0 to 1.5 V. This process is accompanied by the hydrogen evolution reaction, and the released hydrogen gas made the PTH film with multiporous structure. The multiporous PTH film provided a biocompatible microenvironment for gold nanoparticles and enzyme molecules, and greatly amplified the coverage of HRP molecules on the electrode surface. Voltammetric and time-based amperometric techniques were employed to characterize the properties of the derived biosensor. The performance and factors influencing the performance of the biosensor were also proposed. The immobilized HRP displayed a catalytic property to the reduction of H2O2. The H2O2 biosensor achieved 95% of the steady-state current within 2 s, and exhibited a linear range of 9.6×10−6 to 1.2×10−3 M H2O2 with a detection limit of 7.5×10−7 M (S/N = 3). Furthermore, the biosensor remained at about 90% of its original sensitivity after 2 weeks’ storage.

Utilization of Time-Based Techniques in Research and Teaching

This article describes how computer animations, combined with cinematic techniques, have served as a teaching and research tool in architecture. Animations were used in the following areas: case studies of precedents in architecture using the animated modeling as a form of tectonic analysis; conceptualization of space through the study of film, specifically three approaches using film in the early stages of design: cinematic structure, film as site, and transcoding transparency; and mapping the urban fabric combining various time-based media.

Electrochemical behavior of needle-like and forest-like single-walled carbon nanotube electrodes

The electrochemical behavior of needle-like and forest-like single-walled carbon nanotube (SWCNT) electrodes, wet-chemically assembled on a gold substrate, were investigated by cyclic voltammetry and single potential time-based techniques. The results indicate that a SWCNT electrode system exhibits quasi-reversible behavior, and SWCNT electrodes exhibit better electrochemical behavior than bare gold electrodes. A forest-like SWCNT electrode possesses the best electrochemical behavior. This is explained using Randle’s equivalent circuit unit during the electron-transfer process between the redox centre and the SWCNT electrode surface, and an alternative electron-transfer mechanism was suggested for the observed electrochemical properties.

Morphology-controlled SWCNT/polymeric microsphere arrays by a wet chemicalself-assembly technique and their application for sensors

Large-scale morphology-controlled SWCNT/polymeric microsphere arrays can be obtainedby a wet chemical self-assembly technique. The loading of SWCNTs, the length ofSWCNTs, and the size and nature of polymeric microspheres can easily be controlled.Similar results can also be reached using this method for MWCNTs. In both types ofCNTs, they form an interesting interactive ‘net’ structure on spheres and sphere joints. TheSWCNT/PS-modified Au electrode was used for detection of uric acid by cyclicvoltammetry and single-potential time-based techniques. The preliminary results showthat the modified electrode presents good sensitivity and stability to uric acid.

Application of sampling methodologies to network traffic characterization

The relative performance of different data collection methods in the assessment of various traffic parameters is significant when the amount of data generated by a complete trace of a traffic interval is computationally overwhelming, and even capturing summary statistics for all traffic is impractical. This paper presents a study of the performance of various methods of sampling in answering questions related to wide area network traffic characterization. Using a packet trace from a network environment that aggregates traffic from a large number of sources, we simulate various sampling approaches, including time-driven and event-driven methods, with both random and deterministic selection patterns, at a variety of granularities. Using several metrics which indicate the similarity between two distributions, we then compare the sampled traces to the parent population. Our results revealed that the time-triggered techniques did not perform as well as the packet-triggered ones. Furthermore, the performance differences within each class (packet-based or time-based techniques) are small.

Towards Versatile Speech Communication with Computers

Speech communication with computers is a vital new subject of research which should markedly contribute to versatile man-machine interaction. A comprehensive long-range program must be undertaken if the day is to come when the computer will satisfactorily duplicate human speech communication processes and engage in purposeful, meaningful dialogue with man. NASA has undertaken a comprehensive program of research on the major aspects of computer handling of speech. Included are studies of what minimal requirements speech input/output systems must satisfy and how useful certain limited speech recognizers and synthesizers may be. Studies of automatic speech recognition sponsored by NASA include the LISPER limited speech recognition system, fundamental time-based techniques for speech parameter extraction, experimental comparison of speech parameter extractors, orthogonalized damped sinusoidal analysis of speech, speech perception models, and recognition algorithms. Continuous speech processing studies are being directed towards basic prosodic/prosodemic studies and methods for the segmentation of speech. Speech synthesis studies are being delayed until significant success in recognition is attained. Successful systems for man-machine speech dialogue must rely on extensive continued studies of requisite linguistic models and human communication processes. The broad-based approaches taken in the NASA program should lead to truly versatile speech communication with computers.