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Fault diagnosis methods based on blind source separation (BSS) for rolling element bearings are necessary tools to prevent any unexpected accidents. In the field application, the actual signal acquisition is usually hindered by certain restrictions, such as the limited number of signal channels. The purpose of this study is to fulfill the weakness of the existed BSS method.

To deal with this problem, this paper proposes a blind source extraction (BSE) method for bearing fault diagnosis based on empirical mode decomposition (EMD) and temporal correlation. First, a single-channel undetermined BSS problem is transformed into a determined BSS problem using the EMD algorithm. Then, the desired fault signal is extracted from selected intrinsic mode functions with a multi-shift correlation method.

Experimental results prove the extracted fault signal can be easily identified through the envelope spectrum. The application of the proposed method is validated using simulated signals and rolling element bearing signals of the train axle.

This paper proposes an underdetermined BSE method based on the EMD and the temporal correlation method for rolling element bearings. A simulated signal and two bearing fault signal from the train rolling element bearings show that the proposed method can well extract the bearing fault signal. Note that the proposed method can extract the periodic fault signal for bearing fault diagnosis. Thus, it should be helpful in the diagnosis of other rotating machinery, such as gears or blades.

The purpose of this paper is to present a symmetrical method to extract smooth signal from linear mixtures in the frequency domain; with experimentations, the method is thereafter evaluated.

The Second‐order Frequency Identification (SOFI) algorithm has been presented to retrieve baseband signals which have inactive bands and different bandwidths in the frequency domain. However, the SOFI method will bring about accumulative error, therefore an improved method is proposed in this paper by using symmetric extraction mode. In contrast to the SOFI algorithm, the ISOFI method can extract simultaneously the signal with the highest degree of smoothness and the signal with the lowest degree of smoothness. This means that the signals are not extracted one by one; instead, they are extracted in parallel. Experimental results in both noise‐free and noisy scenarios verified that the proposed method has a significant improvement compared with the SOFI algorithm.

An improved SOFI (ISOFI) method is proposed to reduce the accumulated error encountered in the SOFI algorithm. In the proposed method, the symmetric mode is utilized to extract in parallel the signals with different smooth degrees. Experimental results demonstrated that the ISOFI has a higher accuracy and lower accumulated error compared to the original algorithm.

The paper demonstrates use of the symmetric extraction mode to overcome the disadvantage of accumulated errors existing in the SOFI algorithm.

The natural control, feedback, stimuli and protection of these subsequent principles founded this project. Via properly conducted experiments, a multilayer computer rehabilitation system was created that integrated natural interaction assisted by electroencephalogram (EEG), which enabled the movements in the virtual environment and real wheelchair. For blind wheelchair operator patients, this paper involved of expounding the proper methodology. For educating the value of life and independence of blind wheelchair users, outcomes have proven that virtual reality (VR) with EEG signals has that potential.

Individuals face numerous challenges with many disorders, particularly when multiple dysfunctions are diagnosed and especially for visually effected wheelchair users. This scenario, in reality, creates in a degree of incapacity on the part of the wheelchair user in terms of performing simple activities. Based on their specific medical needs, confined patients are treated in a modified method. Independent navigation is secured for individuals with vision and motor disabilities. There is a necessity for communication which justifies the use of VR in this navigation situation. For the effective integration of locomotion besides, it must be under natural guidance. EEG, which uses random brain impulses, has made significant progress in the field of health. The custom of an automated audio announcement system modified to have the help of VR and EEG for the training of locomotion and individualized interaction of wheelchair users with visual disability is demonstrated in this study through an experiment. Enabling the patients who were otherwise deemed incapacitated to participate in social activities, as the aim was to have efficient connections.

To protect their life straightaway and to report all these disputes, the military system should have high speed, more precise portable prototype device for nursing the soldier health, recognition of solider location and report about health sharing system to the concerned system. Field programmable gate array (FPGA)-based soldier’s health observing and position gratitude system is proposed in this paper. Reliant on heart rate which is centered on EEG signals, the soldier’s health is observed on systematic bases. By emerging Verilog hardware description language (HDL) programming language and executing on Artix-7 development FPGA board of part name XC7ACSG100t the whole work is approved in a Vivado Design Suite. Classification of different abnormalities and cloud storage of EEG along with the type of abnormalities, artifact elimination, abnormalities identification based on feature extraction, exist in the segment of suggested architecture. Irregularity circumstances are noticed through developed prototype system and alert the physically challenged (PHC) individual via an audio announcement. An actual method for eradicating motion artifacts from EEG signals that have anomalies in the PHC person’s brain has been established, and the established system is a portable device that can deliver differences in brain signal variation intensity. Primarily the EEG signals can be taken and the undesirable artifact can be detached, later structures can be mined by discrete wavelet transform these are the two stages through which artifact deletion can be completed. The anomalies in signal can be noticed and recognized by using machine learning algorithms known as multirate support vector machine classifiers when the features have been extracted using a combination of hidden Markov model (HMM) and Gaussian mixture model (GMM). Intended for capable declaration about action taken by a blind person, these result signals are protected in storage devices and conveyed to the controller. Pretending daily motion schedules allows the pretentious EEG signals to be caught. Aimed at the validation of planned system, the database can be used and continued with numerous recorded signals of EEG. The projected strategy executes better in terms of re-storing theta, delta, alpha and beta complexes of the original EEG with less alteration and a higher signal to noise ratio (SNR) value of the EEG signal, which illustrates in the quantitative analysis. The projected method used Verilog HDL and MATLAB software for both formation and authorization of results to yield improved results. Since from the achieved results, it is initiated that 32% enhancement in SNR, 14% in mean squared error (MSE) and 65% enhancement in recognition of anomalies, hence design is effectively certified and proved for standard EEG signals data sets on FPGA.

The proposed system can be used in military applications as it is high speed and excellent precise in terms of identification of abnormality, the developed system is portable and very precise. FPGA-based soldier’s health observing and position gratitude system is proposed in this paper. Reliant on heart rate which is centered on EEG signals the soldier health is observed in systematic bases. The proposed system is developed using Verilog HDL programming language and executing on Artix-7 development FPGA board of part name XC7ACSG100t and synthesised using in Vivado Design Suite software tool.

This paper aims to design a vision-based non-contact real-time accurate heart rate (HR) measurement framework for home nursing assistant.

The study applied Second-Order Blind Signal Identification (SOBI) algorithm to extract remote HR signal and analyzed it with Fast Fourier Transform (FFT). Multiple regions of interest are chosen and analyzed to obtain a more accurate result.

An accurate non-contact hear rate (HR) measurement framework is proposed and proved to be efficient.

The contributions of this HR measurement framework are as follows: accurate measurement of HR, real-time performance, robust under various scenes such as conversation, lightweight computation which is suitable and necessary for home nursing assistance. This framework is designed to be flexibly used in various real-life scenes such as domestic health assistance and affectively intelligent agents and is proved to be robust under such scenes.

Independent component analysis (ICA) is a widely-used blind source separation technique. ICA has been applied to many applications. ICA is usually utilized as a black box, without understanding its internal details. Therefore, in this paper, the basics of ICA are provided to show how it works to serve as a comprehensive source for researchers who are interested in this field. This paper starts by introducing the definition and underlying principles of ICA. Additionally, different numerical examples in a step-by-step approach are demonstrated to explain the preprocessing steps of ICA and the mixing and unmixing processes in ICA. Moreover, different ICA algorithms, challenges, and applications are presented.

The technique for hiding confidential data in specific digital media by enhancing the graphical contents is known as watermarking. The dissemination of information over a secure channel is essential for multimedia applications. The purpose of this study is to develop a secure communication approach for OFDM system.

This paper exploits a secure communication in the orthogonal frequency division multiplexing (OFDM) system using wavelet-based video watermarking technique. In this work, the Chronological-MS algorithm is used for securing the data communication in the OFDM system. Here, the secret message is embedded in video frames using wavelet transform for hiding sensitive information and the hidden information is transmitted over the OFDM system. The Chronological-MS algorithm is used for selecting the optimal regions in the video for embedding secret message. In embedding phase, wavelet coefficients are obtained by applying wavelet transform on the frame for embedding the secret message. Meanwhile, in extraction phase, the inverse wavelet transform is applied to extract the secret message.

Considering number of frames, the maximum Peak signal-to-noise ratio (PSNR) value is attained by proposed Wavelet + Chronological MS method for Video 2 with value 73.643 dB, respectively. Meanwhile, the minimum mean squared error (MSE) attained by the proposed Wavelet + Chronological MS method is when considering number of frames with MSE values as 0.001 for both Videos 1 and 2. The minimum bit error rate (BER) value is attained by the proposed method with value 0.00009 considering random noise with Video 1. Thus, the proposed Wavelet + Chronological MS have shown better results than the existing techniques.

This work proposes a wavelet-based watermarking method using Chronological-MS, for initiating secured communication over an OFDM. One of the main advantages of wavelets is that they offer a simultaneous localization in time and frequency domain. Hence, the proposed method offers the highly secured data transmission over the OFDM.

With SMD‐Technology, the first purpose of plated‐through holes—to fix the components to the board—is no longer relevant. The utilisation of blind vias, preferably in combination with buried vias, results in an extremely high interconnection and packaging density and in most cases in fewer signal layers. Technology problems like drilling with precisely controlled Z‐axis as well as through‐plating of blind vias have been overcome. Reliability tests have shown a substantially lower failure rate in thermal cycling tests for DENSTRATE® multilayers.

This paper investigates the semantic association mechanisms of weak demand signals that facilitate innovative product development in terms of conceptual and temporal precedence, despite their inherent ambiguity and uncertainty.

To address this challenge, a domain ontology approach is proposed to construct a customer demand scenario-based framework that eliminates the blind spots in weak demand signal identification. The framework provides a basis for identifying such signals and introduces evaluation indices, such as depth, novelty and association, which are integrated to propose a three-dimensional weak signal recognition model based on domain ontology that outperforms existing research.

Empirical analysis is carried out based on customer comments of new energy vehicles on car platform such as “Auto Home” and “Bitauto”. Results demonstrate that in terms of recognition quantity, the three-dimensional weak demand signal recognition model, based on domain ontology, can accurately identify six demand weak signals. Conversely, the keyword analysis method exhibits a recognition quantity of four weak signals; in terms of recognition quality, the three-dimensional weak demand signal recognition model based on domain ontology can exclude non-demand signals such as “charging technology”, while keyword analysis methods cannot. Overall, the model proposed in this paper has higher sensitivity.

This paper proposes a novel method for identifying weak demand signals that considers the frequency of the signal's novelty, depth and relevance to the target demand. To verify its effectiveness, customer review data for new energy vehicles is used. The results provide a theoretical reference for formulating government policies and identifying weak demand signals for businesses.

Looks at the problems that visually disabled people are experiencing with the increasing use of computer software with graphical user interfaces [GUIs] and the move away from text based interfaces. The GUIB project, funded by the European Commission, has been looking at ways of solving this problem and has developed a terminal which integrates vertical and horizontal braille displays, a multi lingual speech synthesizer, other acoustic signals and a pressure sensitive touch pad. The guide display makes it possible for a blind person to look at the screen in a spatial way. The project has also identified ten principles to assist in the training of blind people in the use of GUIs. Concludes that in the future it will be necessary to anticipate new technological advances and ensure that they are developed with the needs of disabled people in mind.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.