We investigated three different methods for simulating neuromusculoskeletal (NMS) control to generate estimates of knee joint loading which were compared to in-vivo measured loads. The major contributions of this work to the literature are in generalizing EMG-informed and probabilistic methods for modelling NMS control. A single calibration function for EMG-informed NMS modelling was identified which accurately estimated knee loads for multiple people across multiple trials. Using a stochastic approach to NMS modelling, we investigated the range of possible solutions for knee joint loading during walking, showing the method’s generalizability and capability to generate solutions which encompassed the measured knee loads. Through this stochastic approach, we were able to show that a single degree of freedom planar knee is suited to estimating total knee loading, but is insufficient for estimating the directional components of load.

Whereas domestication of farm animals has primarily focused on desired productivity traits, the intensification of livestock farming has highlighted the need for improving animal resilience, too. Animal resilience is a complex concept that encompasses the ability for an animal to recover from a particular disturbance. The concept includes resilience to disease, environmental resilience such as extreme and fluctuating climates, but also psychological resilience including stress resilience. Sensor-based data models enable prediction of livestock farming outcomes in response to varying behavioral, physiological, stress and affective states. The quantification of resilience post-disturbance, as well as estimating and predicting resilience pre-disturbance, is challenging. We present a review-based approach in exploring the sensor-data enabled indicators in the investigation of livestock resilience. We assess the intricacies of resilience of farm animals using conceptual, comprehensive, and integrated systems framework. We analyze progress in sensor methods and its possible use to assess various dynamic indicators of livestock resilience. With the rise of sensor-based technologies for livestock farming systems, accurate and sophisticated monitoring systems of animal resilience become more readily available. Wearable sensors, tracking systems, as well as automatic milking systems, provide a way to continuously collect large amounts of quantitative and qualitative data that aid the monitoring of not only health, productivity, and welfare aspects, but also resilience. Sensor-based technologies help breeding goals by contributing to the understanding of the complex, multidimensional framework of livestock resistance. Animal resilience is an essential trait that should be promoted to improve the sustainability of intensive livestock farming. Through digitalization of data collection, farmers can make better livestock management decisions by enhanced understanding of the indicators of environmental, health and psychological resilience, and will be able to predict degrading resilience.

Long-range (LoRa) communication has attracted much attention recently due to its application for many Internet of Things applications. However, one of the key problems of the LoRa technology is it is vulnerable to noise/interference due to the use of only up-chirp signals during modulation. In this paper, to solve this problem, unlike the conventional LoRa modulation scheme, we propose a modulation scheme for LoRa communication based on joint up- and down-chirps. A fast Fourier transform (FFT) based demodulation scheme is devised to detect modulated symbols. To further improve demodulation performance, a hybrid demodulation scheme, comprised of FFT and correlation-based demodulation is also proposed. The performance of the proposed scheme is evaluated through extensive simulation results. Compared to the conventional LoRa modulation scheme, we show that the proposed scheme exhibits over 3 dB performance gain at bit error rate of 10^-4.

This research is done for optimizing telemedicine framework by using fogging or fog computing for smart healthcare systems. Fog computing is used to solve the issues that arise on telemedicine framework of smart healthcare system like Infrastructural, Implementation, Acceptance, Data Management, Security, Bottleneck system organization, and Network latency Issues. we mainly used Distributed Data Flow (DDF) method using fog computing in order to fully solve the listed issues.

Objective: Data compression is a useful process in tele-monitoring applications, in which lesser number of bits are needed to represent the same data. In this work, a run-time lossless compression of single channel Electrocardiogram (ECG) and Photoplethysmogram (PPG) signal is proposed, maintaining all dominant features. Methods: The single channel data are first quantized using optimal quantization level, so that lesser number of bits are needed to represent it, maintaining low quantization error. Then second order delta encoding and run-length encoding (RLE) based data compression are proposed in this work. A new approach of using ‘buffer array’ along with RLE is also introduced, so that minimum bits are needed to store. Results: This algorithm was tested on various single lead ECG and PPG signals available in Physionet. An average compression ratio (CR) was achieved of 6.52, 3.82, and 2.49 for 547 PTBDB ECG records, 48 MITDB ECG records, and 53 MIMIC-II PPG records, respectively. This algorithm was also performed on single channel ECG, collected from 10 healthy volunteers using AD8232 ECG module, with 125 Hz sampling frequency and 10-bit data resolution, which resulted in average CR of 2.34. Discussion: This algorithm was also performed on a smartphone device that provided user-friendly operation. The low computational complications and standalone operation of data collection, compression, and transmission encouraged its implementation for run-time operation. Significance: A comparative study of proposed work with previously published works proved this fact that this algorithm provided better performance in the area of run-time patient health monitoring applications.

Failure or degradation effects lead to power losses in solar panels during their field operation and are identified commonly by electroluminescence imaging. Failures like potential induced degradation and light and enhanced temperature induced degradation require an identification of the electroluminescence pattern over the entire solar panel. As the manual process of analysing patterns is prone to error, we seek for an automatic detection of these failure types. We predict automatically the failure types potential induced degradation and light and enhanced temperature induced degradation by adopting the principle component analysis method in combination with a k-nearest neighbour classifier.

Any predictive software deployed with this hypothesis that test data distribution will not differ from training data distribution. Real time scenario does not follow this rule, which results inconsistent and non-transferable observation in various cases. This makes the dataset shift, a growing concern. In this paper, we’ve explored the recent concept of Label shift detection and classifier correction with the help of Black Box shift detection(BBSD), Black Box shift estimation(BBSE) and Black Box shift correction(BBSC). Digits dataset from ”sklearn” and ”LogisticRegression” classifier have been used for this investigation. Knock out shift was clearly detected by applying Kolmogorov–Smirnov test for BBSD. Performance of the classifier got improved after applying BBSE and BBSC from 91% to 97% in terms of overall accuracy.

This work presents a performance analysis on cooperative non-orthogonal multiple accesses (C-NOMA) when assisted with energy harvesting enabled unmanned aerial vehicle (UAV) decode-and-forward (DF) relaying. In particular, two scenarios are considered, an outdoor-indoor one, where the NOMA signal propagates through outdoor-to-indoor, and a conventional outdoor scenario where the channel gains follow a k-u generalized fading model. The objectives of this work is to analyze the downlink performance of this C-NOMA system and derive closed-form expressions for the outage probability (OP), ergodic capacity (EC), throughput and energy efficiency (EE) for the users assuming imperfect successive interference cancellation (SIC). In particular, the OP approach considers the individual users’ rate where it is required to satisfy certain quality of service (QoS) requirements. The results provide insights into the considered performance metrics relative to key parameters such as power allocation, power splitting factor, fading parameters, and residual interference. Extensive simulations results are presented to validate the accuracy of the derived expressions.

This paper proposes an architecture taking advantage of artificial intelligence and text mining techniques in order to: (i) detect paranoid people by classifying their set of tweets into two classes (Paranoid/not-Paranoid), (ii) ensure the surveillance of these people by classifying their tweets about Covid-19 into two classes (person with normal behavior, person with inappropriate behavior). These objectives are achieved using an approach that takes advantage of different information related to the textual part, user and tweets for features selection task and deep neural network for the classification task. We obtained as an F-score rate 70% for the detection of paranoid people and 73% for the detection of the behavior of these people towards Covid-19. The obtained results are motivating and encouraging researchers to improve them given the interest and the importance of this research axis.

Automatic retinal blood vessel segmentation is very crucial to ophthalmology. It plays a vital role in the early detection of several retinal diseases such as Diabetic Retinopathy, hypertension, etc. In recent times, deep learning based methods have attained great success in automatic segmentation of retinal blood vessels from images. In this paper, a U-NET based architecture is proposed to segment the retinal blood vessels from fundus images of the eye. Furthermore, 3 pre-processing algorithms are also proposed to enhance the performance of the system. The proposed architecture has provided significant results. On the basis of experimental evaluation on the publicly available DRIVE data set, it has been observed that the average accuracy (Acc) is .9577, sensitivity (Se) is .7436, specificity (Sp) is .9838 and F1-score is .7931. The proposed system outperforms all recent state of art approaches mentioned in the literature.

This paper deals with a method of demodulation for oversampled LoRa signal. The usual maximum likelihood (ML) based demodulation method for LoRa chirp spread spectrum (CSS) waveform is dedicated to signals sampled at Nyquist rate, whereas considering oversampled signals may improve the performance of the LoRa demodulation process. In this respect, when an oversampling rate (OSR) 2 is assumed, the method suggested in this paper consists in applying two demodulation processes to the even and odd samples of the oversampled LoRa signal, and then combining the results. This principle is then generalized to any OSR, and we show that the complexity of the method is low since it only involves discrete Fourier transforms (DFT). Moreover, a performance analysis in terms of symbol and bit error rate (SER and BER) is presented considering both additive white Gaussian noise (AWGN) and Rayleigh channel models. Simulations show the relevance of the method and the performance analysis as a gain of 3 dB is achieved by the demodulation at OSR 2 compared with OSR 1.

One of the basic assumptions of physics is that information cannot be transferred more rapidly than light. If one equates information with data, then information transfer is a process in which data are transmitted between two remote locations. According to this basic principle, the transmission process should require an appropriate amount of time so that the distance between these locations divided by the needed time does not exceed the speed of light in a vacuum. However, it is technologically feasible to transmit a voice message, music, or any bit sequence at a low bit rate between locations separated by hundreds of meters, with almost no loss of time. Herein, we demonstrate that this behavior most likely does not contradict special relativity, and we explain this phenomenon based on a model of a superconducting cable.

Purpose: Due to the exponential growth of internet users and internet traffic, information seekers are highly dependent upon search engines to extract relevant information. Due to the accessibility of a large amount of textual, audio, video etc. contents, the responsibility of search engines has increased. Design/methodology/approach: The search engine provides relevant information to internet users concerning to their query; based on content, link structure etc. However, it does not provide the guarantee of the correctness of the information. The performance of a search engine is highly dependent upon the ranking module. The performance of the ranking module is dependent upon the link structure of web pages, which analyse through Web structure mining (WSM) and their content, which analyses through Web content mining (WCM). Web mining plays an important role in computing the rank of web pages. Findings: In this article, web mining types, techniques, tools, algorithms and their challenges are presented. Further, it provides a critical comprehensive survey for the researchers by presenting different features of web pages, which are important to check the quality of web pages. Originality: In this work, authors presented different approaches/techniques, algorithms and evaluation approaches in previous researches and identified some critical issues in page ranking & web mining, which provide future directions for the researchers, working in the area.

Recent results in language processing show that language models are capable of performing several natural language tasks without the need of supervised learning. A challenging task for pre-trained language models is dialogue summarization. One way of generating summaries is engineering prompt templates for few-shot training. However, a static approach of creating prompts leads to unreliable outcomes between different classes of dialogues. Focusing on the dialogues structure properties, we propose a scoring system to improve the few-shot training performances by building tuned prompts composed by the highest scored dialogue samples. Our evaluation based on ROUGE scores and human evaluation shows that there is improvement for the experiments where we made use of the score system. The positive results are validated by all the three large-scale datasets we used in testing. All experiments were performed within the framework of the GPT-3 API.

Frequency control is considered to be an important function of multi-terminal HVDC (MTDC) systems. Sharing frequency containment reserves (FCRs) among interconnected AC systems using flexible HVDC technology is particularly attractive in the European context. However, coordination of the contribution between systems with different sizes, quality requirements and dynamic characteristics requires harmonized rules and well-defined control strategies. If the controllers are not properly designed, they can lead to disproportional supports, noncompliance with the existing regulation framework, or even degradation of the frequency quality. This paper analyzes several frequency control solutions for MTDC systems in terms of the adequacy of the expected contribution to the frequency containment process. The performance analyses are then confirmed using load-frequency models coupled with a three-terminal DC system. It is revealed that certain control solutions result in insufficient performance with respect to the desired FCR provision. The proposed distributed solution for each pair of converters can be the most compliant with the existing framework while keeping the highest degrees of freedom.

Using the Brazilian visual lab mastology data set containing dynamic thermograms, applying components analysis resulted in two corresponding images: 1. Slight decrease as function of rewarming (suggests correlation with the cancer generated heat), 2. Temperature increase as function of rewarming (suggests correlation with veins affected by vasomodulation). All components appear clear and distinct. Applying signal processing methods to the dynamic infrared data, we found distinct components with correspondence to understood physiologic processes. The cases shown are self-evident of the capability of the method but are lacking supporting ground truth that is unavailable with such a limited data set.

Several computational models provide varying approaches to emotional modeling, e.g., event appraisal, motivational states, embodied robots, and data-driven. However, no model has wholly represented the complex emotional process, which calls for searching alternative ways. This paper proposes a simple hybrid computational model called RIO: Robotic Intuition Operation applicable in autonomous agents. Every event or object capable of emotional influence on an agent is classified as a stimulus having independent properties (including emotions and personality). An agent’s emotional state is defined as a set of six coexisting basic emotions (Ekman’s). Each perceived stimulus in a cycle independently deflects this emotional state towards its own (basic) emotions while considering the stimuli, other users, and the agent’s personality, trust, and likeness. The agent makes emotional decisions to support its primary goal and perform specific skills. Experiments and limitations follow the model’s novelty and discussion.

In this paper, we propose an energy efficient SW based approximate 4:2 compressor comprising a 3-input and a 5-input Majority gate. We validate our proposal by means of micromagnetic simulations, and assess and compare its performance with one of the state-of-the-art SW, 45nm CMOS, and Spin-CMOS counterparts. The evaluation results indicate that the proposed compressor consumes 31.5\% less energy in comparison with its accurate SW design version. Furthermore, it has the same energy consumption and error rate as the approximate compressor with Directional Coupler (DC), but it exhibits 3x lower delay. In addition, it consumes 14% less energy, while having 17% lower average error rate than the approximate 45nm CMOS counterpart. When compared with the other emerging technologies, the proposed compressor outperforms approximate Spin-CMOS based compressor by 3 orders of magnitude in term of energy consumption while providing the same error rate. Finally, the proposed compressor requires the smallest chip real-estate measured in terms of devices.