Use Of Additional Hardware Research Articles

EmoTunes: Your Emotional Soundtrack

The human face is an important organ of an individual’s body and it especially plays an important role in extraction of an individual‘s behavior and emotional state. Manually segregating the list of songs and generating an appropriate playlist based on an individual’s emotional features is a very tedious, time consuming, labor intensive and upheld task. Various algorithms have been proposed and developed for automating the playlist generation process.However the proposed existing algorithms in use are computationally slow, less accurate and sometimes even require use of additional hardware like EEG or sensors. This proposed system based on facial expression extracted will generate a playlist automatically thereby reducing the effort and time involved in rendering the process manually. Thus the proposed system tends to reduce the computational time involved in obtaining the results and the overall cost of the designed system, thereby increasing the overall accuracy of the system. Testing of the system is done on both user dependent (dynamic) and user independent (static) dataset. Facial expressions are captured using an inbuilt camera. The accuracy of the emotion detection algorithm used in the system for real time images is around 85-90%, while for static images it is around 98- 100%.The proposed algorithm on an average calculated estimation takes around 0.95-1.05 sec to generate an emotion based music playlist. Thus, it yields better accuracy in terms of performance and computational time and reduces the designing cost, compared to the algorithms used in the literature survey. Keywords—Audio Emotion Recognition, Music Information Retrieval, Emotion Extraction Module, Audio Feature Extraction Module, Artificial Neural Networks, Confusion Matrix, Viola and Jones Face Detection

Application-Aware Scheduling for IEEE 802.15.4e Time-Slotted Channel Hopping Using Software-Defined Wireless Sensor Network Slicing.

Given the improvements to network flexibility and programmability, software-defined wireless sensor networks (SDWSNs) have been paired with IEEE 802.15.4e time-slotted channel hopping (TSCH) to increase network efficiency through slicing. Nonetheless, ensuring the quality of service (QoS) level in a scalable SDWSN remains a significant difficulty. To solve this issue, we introduce the application-aware (AA) scheduling approach, which isolates different traffic types and adapts to QoS requirements dynamically. To the best of our knowledge, this approach is the first to support network scalability using shared timeslots without the use of additional hardware while maintaining the application's QoS level. The AA approach is deeply evaluated compared with both the application traffic isolation (ATI) approach and the application's QoS requirements using the IT-SDN framework and by varying the number of nodes up to 225. The evaluation process took into account up to four applications with varying QoS requirements in terms of delivery rate and delay. In comparison with the ATI approach, the proposed approach enhanced the delivery rate by up to 28% and decreased the delay by up to 57%. Furthermore, even with four applications running concurrently, the AA approach proved capable of meeting a 92% delivery rate requirement for up to 225 nodes and a 900 ms delay requirement for up to 144 nodes.

Simulation-based design optimization of the holographic wavefront sensor in closed-loop adaptive optics

Adaptive optics systems are used to compensate for wavefront distortions introduced by atmospheric turbulence. The distortions are corrected by an adaptable device, normally a deformable mirror. The control signal of the mirror is based on the measurement delivered by a wavefront sensor. Relevant characteristics of the wavefront sensor are the measurement accuracy, the achievable measurement speed and the robustness against scintillation. The modal holographic wavefront sensor can theoretically provide the highest bandwidth compared to other state of the art wavefront sensors and it is robust against scintillation effects. However, the measurement accuracy suffers from crosstalk effects between different aberration modes that are present in the wavefront. In this paper we evaluate whether the sensor can be used effectively in a closed-loop AO system under realistic turbulence conditions. We simulate realistic optical turbulence represented by more than 2500 aberration modes and take different signal-to-noise ratios into account. We determine the performance of a closed-loop AO system based on the holographic sensor. To counter the crosstalk effects, careful choice of the key design parameters of the sensor is necessary. Therefore, we apply an optimization method to find the best sensor design for maximizing the measurement accuracy. By modifying this method to take the changing effective turbulence conditions during closed-loop operation into account, we can improve the performance of the system, especially for demanding signal-to-noise-ratios, even more. Finally, we propose to implement multiple holographic wavefront sensors without the use of additional hardware, to perform multiple measurement at the same time. We show that the measurement accuracy of the sensor and with this the wavefront flatness can be increased significantly without reducing the bandwidth of the adaptive optics system.

Adaptive Fast Fault Location for Open-Switch Faults of Voltage Source Inverter

In this paper, the issue of open-switch faults of voltage source inverter (VSI) is studied, an adaptive fast voltage-based real-time open-switch fault location method is proposed. Firstly, for each switching period, the voltage deviation is calculated by the expected voltage and estimated actual voltage, they are obtained via the mixed logical dynamic model (MLDM) and measured currents, respectively. Then, the voltage deviation of each phase is employed as a fault variable. By this way, avoiding the use of additional hardware, and embedding easily in system. Furthermore, considering the impact of sampling error, parameter error, dead time, delay time, and transition time, the adaptive threshold is designed. Finally, simulations and experiments are executed to verify the effectiveness of the proposed method.

A New Mutual Authentication and Key Agreement Protocol for Mobile Client—Server Environment

Mobile devices are becoming an essential part of many users’ lives. Users exchange sometimes very sensitive data with remote servers. This raises a security problem in terms of the confidentiality and integrity of these data, and users’ privacy. Mutual authentication protocols allow a user and a server to confirm each other’s legitimacy and share a session key to encrypt subsequent communications. Several protocols have been proposed to achieve this goal. However, these have certain weaknesses, such as impersonation, lack of anonymity, the use of additional hardware, and the synchronization problem associated with the use of timestamps. In this paper, we propose a mutual authentication protocol based on elliptic curve cryptography for mobile client – server environments, which addresses the above problems. This protocol is intended to be lightweight as it is designed for resource constrained mobile devices. Moreover, we present a formal and informal analysis of the security of the proposed protocol. This latter has security attributes, such as session key security, perfect forward secrecy, user anonymity, resistance to impersonation, replay and insider attacks. Performance evaluation shows that we outperform similar protocols. Therefore, the proposed protocol is secure, efficient and suitable for mobile environments.

Open‐switch fault diagnosis in three‐level rectifiers based on selective calculation method for instant voltage deviation

This paper presents an open-switch fault detection, and identification method for a neutral-point clamped rectifier system. A new calculation method for phase-to-phase pole voltage deviations is proposed for reducing calculation errors. In this way, the more accurate diagnosis can be achieved. Both single-switch faults, and multiple-switch faults can be identified effectively by using fewer diagnostic variables, which makes the diagnostic method simpler, and more reliable. And all the variables can be simply calculated by signals which are available to the control system of the rectifier, avoiding the use of additional hardware. Moreover, different voltage thresholds are designed for different faulty feature sections to further reduce the diagnostic time. Additionally, a diagnostic result checking method is proposed to avoid the misdiagnosis when considering the effect of outer-switch faults. Finally, experiments are carried out, and the results show the robustness, and effectiveness of the proposed method.

Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning

The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact caused by cardiac induced movement contaminates the EEG during EEG-fMRI recordings. Removal of BCG in software has generally made use of linear decompositions of the corrupted EEG. This is not ideal as the BCG signal propagates in a manner which is non-linearly dependent on the electrocardiogram (ECG). In this paper, we present a novel method for BCG artifact suppression using recurrent neural networks (RNNs). EEG signals were recovered by training RNNs on the nonlinear mappings between ECG and the BCG corrupted EEG. We evaluated our model's performance against the commonly used Optimal Basis Set (OBS) method at the level of individual subjects, and investigated generalization across subjects. We show that our algorithm can generate larger average power reduction of the BCG at critical frequencies, while simultaneously improving task relevant EEG based classification. The presented deep learning architecture can be used to reduce BCG related artifacts in EEG-fMRI recordings. We present a deep learning approach that can be used to suppress the BCG artifact in EEG-fMRI without the use of additional hardware. This method may have scope to be combined with current hardware methods, operate in real-time and be used for direct modeling of the BCG.

Event-triggered and self-triggered H ∞ output tracking control for discrete-time linear parameter-varying systems with network-induced delays

In this paper, we address the event-triggered and self-triggered output tracking problem of discrete LPV systems with network-induced delay. Considering the time delay and external disturbance, we formulate the closed-loop event-triggered output tracking problem into a time-delayed discrete polytopic linear parameter-varying system with relative event-triggering mechanism. Then, by constructing the parameter-dependent Lyapunov–Krasovskii functional, we derive a sufficient condition such that the closed-loop system is global asymptotic stable and satisfies the output tracking performance. Further, we develop an approach to design the event-triggering mechanism and output tracking controller. The parameters of event-triggering mechanism and the gains of output tracking controller are obtained by solving linear matrix inequalities. Moreover, we extend the results of the event-triggered control to the self-triggered output tracking control. This not only mitigates the usage of network bandwidth but also avoids the use of additional hardware. Finally, the numerical simulation is included to illustrate the usefulness and effectiveness of the proposed approach.

Distorter Characterisation Using Mutual Inductance in Electromagnetic Tracking.

Electromagnetic tracking (EMT) is playing an increasingly important role in surgical navigation, medical robotics and virtual reality development as a positional and orientation reference. Though EMT is not restricted by line-of-sight requirements, measurement errors caused by magnetic distortions in the environment remain the technology’s principal shortcoming. The characterisation, reduction and compensation of these errors is a broadly researched topic, with many developed techniques relying on auxiliary tracking hardware including redundant sensor arrays, optical and inertial tracking systems. This paper describes a novel method of detecting static magnetic distortions using only the magnetic field transmitting array. An existing transmitter design is modified to enable simultaneous transmission and reception of the generated magnetic field. A mutual inductance model is developed for this transmitter design in which deviations from control measurements indicate the location, magnitude and material of the field distorter to an approximate degree. While not directly compensating for errors, this work enables users of EMT systems to optimise placement of the magnetic transmitter by characterising a distorter’s effect within the tracking volume without the use of additional hardware. The discrimination capabilities of this method may also allow researchers to apply material-specific compensation techniques to minimise position error in the clinical setting.

Experimental investigation of the impacts of total temperature non-uniformities on the flow and acoustic fields of a heated supersonic jet

In recent years, the noise produced by tactical aircraft has become a growing concern due to stricter community noise standards and the negative health effects it has on flight support personnel. This study proposes the examination of a new novel noise reduction method involving thermal non-uniformities in heated supersonic jets. Thermal non-uniformities have the advantage of increasing turbulent mixing in jets without the use of additional hardware and can most likely be implemented in afterburning engines with minimal modification. In the course of this study a thermally non-uniform Mach 1.5 heated jet with a centered and offset thermal non-uniformities will be examined. It will be shown that thermal non-uniformities reduce the far-field radiated noise up to ~2dB at peak frequencies and have a measurable impact on the directivity of radiated noise, including a narrowing of the Mach wave emission angle and a non-uniform azimuthal directivity. These changes in the acoustic field are directly related to global changes in the turbulence development observed in the jet. These effects include a shortened potential core, increased shear layer thickness, and a decreased mean flow in regions of peak Reynolds shear stress. These effects are captured by stereoscopic PIV and near and far-field microphone measurements.

Scalable and distributed detection analysis on wormhole links in wireless sensor networks for networked systems

Recent surge in cyber physical systems (CPSs) which integrate cyber and physical worlds, increases security risks at both the surfaces. The wireless sensor communication network is one of the major components of the critical infrastructure in all networked systems including networked control systems and CPSs. As these systems support range of time and data sensitive applications, varying from health care to emergency services, to transport, and to environmental monitoring services, core sensor network component faces potential threats due to varying attacks, including wormhole attack that can cause damage to the control system in terms of data leakage, data dropping, and delayed delivery. It is also difficult to detect, as it requires neither nodes in the network to be compromised nor adversaries to acquire valid network identity to instigate such attacks. Many existing solutions in the literature to detect wormholes are based on node’s static location information, synchronization of clocks, use of additional hardware, such as antennas and GPS, and neighborhood and traffic information, which may lead to large energy consumption. In this paper, a scalable and distributed scheme which uses sequential probability ratio test is proposed, to avoid single point failures and to handle high mobility, with no additional resource requirements. It is observed that wormholes are detected using few packets and detection is faster with increasing mobility. The system is highly customizable as system parameters can be chosen to balance the speed and accuracy of detection. System overheads in terms of communication, computation, and storage aspects are analyzed and presented.

Patients Have Strong Preferences and Perceptions for Biceps Tenotomy Versus Tenodesis

To evaluate what factors are important in the patients' preference and their perception of a successful surgical outcome. A biceps-specific questionnaire was developed using a series of questions assessing current symptoms, previous knowledge of biceps tendon surgery, surgical outcome priorities, and patient demographics and administered to 100 patients with proximal biceps pathology after approval by the Institutional Review Board. The patients were asked which surgery they would prefer. A set of χ2 tests were used to test the association between categorical variables. All tests were 2-sided and considered significant at P < .05. A total of 100 patients enrolled in the study, with 49 female and 51 male patients at an average age of 49years (range, 19 to 79years). Of the 100 patients, 64 (64%) chose to have biceps tenodesis. Factors predictive of choosing a biceps tenodesis included female sex, and concern of cosmetic deformity and residual postoperative pain with a tenotomy (P < .05). Factors predictive of choosing a tenotomy included male sex, high level of current biciptal groove pain, and concerns regarding the use of additional hardware and longer recovery with a tenodesis (P < .05). Age, body mass index, occupation, income level, and concerns regarding postoperative strength and muscle cramping were not found to have a significant predictive effect toward either procedure. Patient age should not be used as the sole criterion when deciding between biceps tenotomy and tenodesis. Our results can be consolidated to 5 predictive, reliable questions that will assist orthopaedic surgeons in making individualized patient-specific decisions regarding proximal biceps tendon surgery by emphasizing what factors are most important to patients for a successful surgical outcome.

Wireless Sensor Networks Node Localization-A Performance Comparison of Shuffled Frog Leaping and Firefly Algorithm in LabVIEW

Wireless sensor networks (WSN) have become popular in many applications area including environmental monitoring, military and offshore oil & gas industries. In WSN the sensors are randomly deployed in the sensor field and hence estimation of the localization of each deployed node has drawn more attention by the recent researchers, It’s a unique problem to identify and maximizing the coverage where the sensors need to be placed in a position so that the sensing capability of the network is fully utilized to ensure high quality of service. In order to keep the cost of sensor networks to a minimum, the use of additional hardware like global positioning system (GPS) can be avoided by the use of effective algorithms that can be used for the same. In this paper we attempted to use both the shuffled frog leaping (SFLA) and firefly algorithms (FFA) to estimate the optimal location of randomly deployed sensors. The results were compared and published for the usefulness of further research. DOI: http://dx.doi.org/10.11591/telkomnika.v14i3.7861 Full Text: PDF

Real-Time IGBT Open-Circuit Fault Diagnosis in Three-Level Neutral-Point-Clamped Voltage-Source Rectifiers Based on Instant Voltage Error

This paper presents a real-time diagnostic algorithm for insulated-gate bipolar transistor open-circuit faults in three-level neutral-point-clamped rectifiers. This algorithm is based on the instant voltage error in the converter and requires only signals already available to the control system, avoiding the use of additional hardware. The algorithm is independent from the load and from the used control strategy and provides very fast detection and identification of the fault, with diagnostic times as low as two sample periods (in favorable conditions). Experimental results are presented for the operation of the rectifier with two distinct control and modulation strategies and in different conditions. Results show the algorithm's speed, effectiveness, and robustness.

An image-based method to synchronize cone-beam CT and optical surface tracking.

The integration of in‐room X‐ray imaging and optical surface tracking has gained increasing importance in the field of image guided radiotherapy (IGRT). An essential step for this integration consists of temporally synchronizing the acquisition of X‐ray projections and surface data. We present an image‐based method for the synchronization of cone‐beam computed tomography (CBCT) and optical surface systems, which does not require the use of additional hardware. The method is based on optically tracking the motion of a component of the CBCT/gantry unit, which rotates during the acquisition of the CBCT scan. A calibration procedure was implemented to relate the position of the rotating component identified by the optical system with the time elapsed since the beginning of the CBCT scan, thus obtaining the temporal correspondence between the acquisition of X‐ray projections and surface data. The accuracy of the proposed synchronization method was evaluated on a motorized moving phantom, performing eight simultaneous acquisitions with an Elekta Synergy CBCT machine and the AlignRT optical device. The median time difference between the sinusoidal peaks of phantom motion signals extracted from the synchronized CBCT and AlignRT systems ranged between ‐3.1 and 12.9 msec, with a maximum interquartile range of 14.4 msec. The method was also applied to clinical data acquired from seven lung cancer patients, demonstrating the potential of the proposed approach in estimating the individual and daily variations in respiratory parameters and motion correlation of internal and external structures. The presented synchronization method can be particularly useful for tumor tracking applications in extracranial radiation treatments, especially in the field of patient‐specific breathing models, based on the correlation between internal tumor motion and external surface surrogates.PACS number: 87

Towards better understanding and reducing the effect of limb position on myoelectric upper-limb prostheses.

Myoelectric control of prosthetic devices tend to rely on classification schemes of extracted features of EMG data. Those features however, may be sensitive to arm position resulting in decreased performance in real-world applications. The effect of varying limb position in a pattern recognition system have been illustrated by documenting the change in classification accuracy as the user achieves particular limb configurations. We continue to investigate this limb position effect by observing its impact on classification accuracy as well as through an analysis of how each extracted feature of the raw EMG varies in each position. Finally, LDA classification schemes are applied both to demonstrate the effect varying limb position has on classification accuracy and to increase classification accuracy without the use of additional hardware or sensors such as accelerometers as has been done in the past. It is shown that high classification accuracy can be achieved by (1) training an LDA classifier with data from many positions, as well as (2) by utilizing an extra position LDA classifier which can weigh the grasp classifiers appropriately. The classification accuracies achieved by these methods approached that of a model relying on a perfect knowledge of arm position.

On image pre-processing for PIV of single- and two-phase flows over reflecting objects

A novel image pre-processing scheme for PIV of single- and two-phase flows over reflecting objects which does not require the use of additional hardware is discussed. The approach for single-phase flow consists of image normalization and intensity stretching followed by background subtraction. For two-phase flow, an additional masking step is added after the background subtraction. The effectiveness of the pre-processing scheme is shown for two examples: PIV of single-phase flow in spacer-filled channels and two-phase flow in these channels. The pre-processing scheme increased the displacement peak detectability significantly and produced high quality vector fields, without the use of additional hardware.

An algorithm for median filtering on the basis of merging of ordered columns

This paper presents algorithms for optimization of the median filtering of images. A median filtering algorithm on the basis of merging ordered columns of an image in the 3 × 3 window of a filter is developed, which allows us to perform the filtering on a personal computer in real time without use of additional hardware. This algorithm outperforms other median filtering algorithms in execution speed on a personal computer.

Statistical Method for Analog-to-Digital System Testing in Time and Spectral Domain

The article deals with an improved method for testing analog-digital system (ADS) as the system objects with the dynamic nonlinear and stochastic properties. A proposed technique makes it possible to utilize the statistical testing method to estimate the dynamic of ADS as in time, as in spectral domain, without the use of additional hardware or the need for additional measurements. The implementation of the statistical testing method that is described is based on a broadband test signal that simulates actual operating conditions. The technique makes it possible to avoid the standard problems associated with precise estimation of the spectrum of a broadband signal. It has been validated in tests of the dynamic parameters of 6 up to 1-bit high-speed.

Dynamically managing the communication-parallelism trade-off in future clustered processors

Clustered microarchitectures are an attractive alternative to large monolithic superscalar designs due to their potential for higher clock rates in the face of increasingly wire-delay-constrained process technologies. As increasing transistor counts allow an increase in the number of clusters, thereby allowing more aggressive use of instruction-level parallelism (ILP), the inter-cluster communication increases as data values get spread across a wider area. As a result of the emergence of this trade-off between communication and parallelism, a subset of the total on-chip clusters is optimal for performance. To match the hardware to the application's needs, we use a robust algorithm to dynamically tune the clustered architecture. The algorithm, which is based on program metrics gathered at periodic intervals, achieves an 11% performance improvement on average over the best statically defined architecture. We also show that the use of additional hardware and reconfiguration at basic block boundaries can achieve average improvements of 15%. Our results demonstrate that reconfiguration provides an effective solution to the communication and parallelism trade-off inherent in the communication-bound processors of the future.