Search Results (17)

Visually impaired people face significant obstacles when navigating complex environments. However, recent technological advances have greatly improved the functionality of navigation systems tailored to their needs. The objective of this research is to evaluate the effectiveness and functionality these navigation systems through a comparative analysis of recent technologies. For this purpose, the PRISMA 2020 methodology was used to perform a systematic literature review. After identification and screening, 58 articles published between 2019 and 2024 were selected from three academic databases: Dimensions (26 articles), Web of Science (18 articles), and Scopus (14 articles). Bibliometric analysis demonstrated a growing interest of the research community in the topic, with an average of 4.552 citations per published article. Even with the technological advances that have occurred in recent times, there is still a significant gap in the support systems for people with blindness due to the lack of digital accessibility and the scarcity of adapted support systems. This situation limits the autonomy and inclusion of people with blindness, so the need to continue developing technological and social solutions to ensure equal opportunities and full participation in society is evident. This study emphasizes the great advances with the integration of sensors such as high-precision GPS, ultrasonic sensors, Bluetooth, and various assistance apps for object recognition, obstacle detection, and trajectory generation, as well as haptic systems, which provide tactile information through wearables or actuators and improve spatial awareness. Current navigation algorithms were also identified in the review with methods including obstacle detection, path planning, and trajectory prediction, applied to technologies such as ultrasonic sensors, RGB-D cameras, and LiDAR for indoor navigation, as well as stereo cameras and GPS for outdoor navigation. It was also found that AI systems employ deep learning and neural networks to optimize both navigation accuracy and energy efficiency. Finally, analysis revealed that 79% of the 58 reviewed articles included experimental validation, 87% of which were on haptic systems and 40% on smartphones. These results underscore the importance of experimentation in the development of technologies for the mobility of people with visual impairment. Full article

This study proposes multicultural preaching across generations as a means of effective preaching in the time of the Great Dechurching. Young generations, represented by Millennials and Generation Z, are the least religious of all age groups, showing the strongest intention to leave the church. The author argues that the failure to form a Christian identity, rather than the church’s failure to adapt culturally, is the main cause of the Great Dechurching among young generations and that preaching to a generation-segregated congregation, tailored to a target generation, contributes to the failure of forming a Christian identity, as it obstructs the sharing of faith experiences intergenerationally. Based on empirical evidence from multiple surveys, I demonstrate that preaching is influential in the dechurching of young generations, and that the faith gap across generations, rather than the cultural gap, contributes to the dechurching of young generations. Then, by analyzing preaching models in relation to generation, the author points out the problems in generation-blind and -separated preaching and suggests multicultural preaching across generations as a desirable homiletical model for overcoming the dechurching of young generations by formulating a Christian identity through intergenerational conversations around faith. I describe this as conversational preaching that seeks mutual listening and learning based on equal and reciprocal relationships across generations, as well as the recognition of cultural differences across generations. Full article

This paper introduces a novel approach to blind adaptive equalization for digital communication systems using genetic algorithms (GAs). Unlike traditional methods that rely on linear programming and suffer from local minima issues, this technique utilizes a stochastic linear programming cost function with GAs for robust optimization. The proposed method termed Blind Linear Equalizer based on genetic algorithm (BLE-GA) enhances performance by leveraging a GA’s ability to handle stochastic variables, offering rapid convergence and resilience against signal noise and inter-symbol interference. Extensive simulations demonstrate the effectiveness of BLE-GA across different QAM systems, outperforming conventional techniques like the Constant Modulus Algorithm in scenarios with high modulation levels. This study validates the potential of using GAs in adaptive blind equalization to achieve reliable and efficient communication, even in complex and noisy channel conditions. Full article

Deep learning-based segmentation models have made a profound impact on medical procedures, with U-Net based computed tomography (CT) segmentation models exhibiting remarkable performance. Yet, even with these advances, these models are found to be vulnerable to adversarial attacks, a problem that equally affects automatic CT segmentation models. Conventional adversarial attacks typically rely on adding noise or perturbations, leading to a compromise between the success rate of the attack and its perceptibility. In this study, we challenge this paradigm and introduce a novel generation of adversarial attacks aimed at deceiving both the target segmentation model and medical practitioners. Our approach aims to deceive a target model by altering the texture statistics of an organ while retaining its shape. We employ a real-time style transfer method, known as the texture reformer, which uses adaptive instance normalization (AdaIN) to change the statistics of an image’s feature.To induce transformation, we modify the AdaIN, which typically aligns the source and target image statistics. Through rigorous experiments, we demonstrate the effectiveness of our approach. Our adversarial samples successfully pass as realistic in blind tests conducted with physicians, surpassing the effectiveness of contemporary techniques. This innovative methodology not only offers a robust tool for benchmarking and validating automated CT segmentation systems but also serves as a potent mechanism for data augmentation, thereby enhancing model generalization. This dual capability significantly bolsters advancements in the field of deep learning-based medical and healthcare segmentation models. Full article

Smartphones are increasingly being used to enable patients to play an active role in managing their own health through applications, also called apps. The latest generation of sound processors for cochlear implants offer Bluetooth connectivity that makes it possible to connect smartphones or tablets and thus enable patients to modify their hearing sensation or measure system parameters. However, to achieve a high adoption rate and secure operation of these applications, it is necessary to design intuitive user interfaces (UI) for end users. The main goal of the current study was to evaluate the usability of two different UIs. A second goal was to compare the hearing outcomes based on the patient’s adjustments. The two different UIs were explored in a group of adult and older adult bimodal cochlear-implant users, with adjustments possible for both the cochlear implant and the contralateral hearing aid. One of the UIs comprised a classical equalizer and volume-dial approach, while the second UI followed a 2D-Surface concept, to manipulate the corresponding sound parameters. The participants changed their fitting parameters using both UIs in seven different sound scenes. The self-adjusted settings for the different scenarios were stored and recalled at a later stage for direct comparison. To enable an assessment of reliability and reproducibility, the self-adaptation was also repeated for two of the seven sound scenes. Within minutes, the participants became accustomed to the concept of both UIs and generated their own parameter settings. Both UIs resulted in settings that could be considered similar in terms of spontaneous acceptance and sound quality. Furthermore, both UIs showed high reliability in the test–retest procedure. The time required for adjustment was significantly shorter with the 2D-Surface UI. A closer look at the bimodal aspect shows that participants were able to compensate for differences in loudness and frequencies between the cochlear implant and the hearing aid. The blind comparison test showed that self-adjustment led to a higher acceptance of the sound perception in more than 80% of the cases. Full article

Background and Objectives. To differentiate the intensity of postoperative pain after primary molar pulpectomy employing manual instrumentation versus two single-file systems with different kinetics (the XP-Endo shaper file with adaptive instrumentation vs. the Kedo-SG blue file with continuous rotation instrumentation). Materials and Methods. This three-arm, single-blind, randomized clinical trial included assessing 75 healthy children between 4 to 9 years who required pulpectomy for primary molars (mandibular first and second). The three groups each had an equal number of children. Children in Group 1 had their teeth instrumented with the XP-endo Shaper, children in Group 2 had their teeth instrumented with the Kedo-SG Blue file, and children in Group 3 had their teeth instrumented manually using K-files. The degree of postoperative pain was measured using a four-point pain scale at 6-, 12-, 24-, 48-, and 72-h following therapy. Each participant’s parent received five flashcards with four faces and a word characterizing each face. The data were analyzed using Kruskal–Wallis and chi-square tests. The level of significance was set to 5%. Results. During the follow-up period, there was a significant difference in postoperative pain intensity between the three groups. The XP-endo shaper was associated with considerably decreased post operative at the 6- and 12-h interval followed by Kedo-SG. The highest post-operative discomfort across the groups was related to the patients who underwent manual instrumentation. Conclusion. In comparison to rotary and manual instrumentation, postoperative pain severity was reduced with adaptive instrumentation. Full article

In a blind adaptive deconvolution problem, the convolutional noise observed at the output of the deconvolution process, in addition to the required source signal, is—according to the literature—assumed to be a Gaussian process when the deconvolution process (the blind adaptive equalizer) is deep in its convergence state. Namely, when the convolutional noise sequence or, equivalently, the residual inter-symbol interference (ISI) is considered small. Up to now, no closed-form approximated expression is given for the residual ISI, where the Gaussian model can be used to describe the convolutional noise probability density function (pdf). In this paper, we use the Maximum Entropy density technique, Lagrange’s Integral method, and quasi-moment truncation technique to obtain an approximated closed-form equation for the residual ISI where the Gaussian model can be used to approximately describe the convolutional noise pdf. We will show, based on this approximated closed-form equation for the residual ISI, that the Gaussian model can be used to approximately describe the convolutional noise pdf just before the equalizer has converged, even at a residual ISI level where the “eye diagram” is still very closed, namely, where the residual ISI can not be considered as small. Full article

The college entrance rate of the disabled is gradually increasing, and each university is trying to provide equal rights and opportunities for college students with disabilities. However, students with disabilities still have difficulty adapting to college life due to limitations in the range of experience and diversity, restrictions in walking ability, and restrictions on interaction with the environment. Visually impaired students cannot perform tasks given by universities independently without the help of others, but universities do not have a system that is helpful except for supporting helpers. Therefore, in this paper, we aimed to develop independent report generation software, VTR4VI (Voice to Report program for the Visually Impaired) for students with visual impairment by using mobile devices that are always in possession. Since the existing speech recognition document editing software is designed for non-visually impaired people, it is difficult for the visually impaired to use. Accordingly, the requirements of a report generator for blind students were identified so blind students could freely perform assignments or write reports without helpers, just like non-visually impaired students. This software can be easily used by clicking on the Bluetooth remote control instead of touching the phone screen, and the operation is simple. As a result of our usability evaluation, our VTR4VI will surely help the visually impaired to study and make a written report. Full article

Deep neural networks in the area of information security are facing a severe threat from adversarial examples (AEs). Existing methods of AE generation use two optimization models: (1) taking the successful attack as the objective function and limiting perturbations as the constraint; (2) taking the minimum of adversarial perturbations as the target and the successful attack as the constraint. These all involve two fundamental problems of AEs: the minimum boundary of constructing the AEs and whether that boundary is reachable. The reachability means whether the AEs of successful attack models exist equal to that boundary. Previous optimization models have no complete answer to the problems. Therefore, in this paper, for the first problem, we propose the definition of the minimum AEs and give the theoretical lower bound of the amplitude of the minimum AEs. For the second problem, we prove that solving the generation of the minimum AEs is an NPC problem, and then based on its computational inaccessibility, we establish a new third optimization model. This model is general and can adapt to any constraint. To verify the model, we devise two specific methods for generating controllable AEs under the widely used distance evaluation standard of adversarial perturbations, namely $L_p$ constraint and $SSIM$ constraint (structural similarity). This model limits the amplitude of the AEs, reduces the solution space’s search cost, and is further improved in efficiency. In theory, those AEs generated by the new model which are closer to the actual minimum adversarial boundary overcome the blindness of the adversarial amplitude setting of the existing methods and further improve the attack success rate. In addition, this model can generate accurate AEs with controllable amplitude under different constraints, which is suitable for different application scenarios. In addition, through extensive experiments, they demonstrate a better attack ability under the same constraints as other baseline attacks. For all the datasets we test in the experiment, compared with other baseline methods, the attack success rate of our method is improved by approximately 10%. Full article

This paper proposes a simple and robust variable modulation-decision-directed least mean square (VM-DDLMS) algorithm for reducing the complexity of conventional equalization algorithms and improving the stability of variable modulation (VM) systems. Compared to conventional adaptive equalization algorithms, known information was used as training sequences to reduce the bandwidth consumption caused by inserting training sequences; compared with conventional blind equalization algorithms, the parameters and decisions of the equalizer were determinate, which was conducive to a stable equalization performance. The simulation and implementation results show that the proposed algorithm has a better bit error rate (BER) performance than that of the constant modulus algorithm (CMA) and modified constant modulus algorithm (MCMA) while maintaining the same level of consumption of hardware resources. Compared to the conventional decision-directed least mean square (DDLMS) algorithm, the proposed algorithm only needs to make quadrature phase shift keying (QPSK) symbol decisions, which reduces the computational complexity. In parallel 11th-order equalization algorithms, the operating frequency of VM-DDLMS can reach up to 333.33 MHz. Full article

The probability density function (pdf) valid for the Gaussian case is often applied for describing the convolutional noise pdf in the blind adaptive deconvolution problem, although it is known that it can be applied only at the latter stages of the deconvolution process, where the convolutional noise pdf tends to be approximately Gaussian. Recently, the deconvolutional noise pdf was approximated with the Edgeworth Expansion and with the Maximum Entropy density function for the 16 Quadrature Amplitude Modulation (QAM) input but no equalization performance improvement was seen for the hard channel case with the equalization algorithm based on the Maximum Entropy density function approach for the convolutional noise pdf compared with the original Maximum Entropy algorithm, while for the Edgeworth Expansion approximation technique, additional predefined parameters were needed in the algorithm. In this paper, the Generalized Gaussian density (GGD) function and the Edgeworth Expansion are applied for approximating the convolutional noise pdf for the 16 QAM input case, with no need for additional predefined parameters in the obtained equalization method. Simulation results indicate that improved equalization performance is obtained from the convergence time point of view of approximately 15,000 symbols for the hard channel case with our new proposed equalization method based on the new model for the convolutional noise pdf compared to the original Maximum Entropy algorithm. By convergence time, we mean the number of symbols required to reach a residual inter-symbol-interference (ISI) for which reliable decisions can be made on the equalized output sequence. Full article

Kefir is an acidic, carbonated, and fermented dairy product produced by fermenting milk with kefir grains. The Lactobacillus species constitutes an important part of kefir grains. In a previous animal study, kefir effectively improved exercise performance and had anti-fatigue effects. The purpose of this research was to explore the benefits of applying kefir to improve exercise performance, reduce fatigue, and improve physiological adaptability in humans. The test used a double-blind crossover design and supplementation for 28 days. Sixteen 20–30 year-old subjects were divided into two groups in a balanced order according to each individual’s initial maximal oxygen uptake and were assigned to receive a placebo (equal flavor, equal calories, 20 g/day) or SYNKEFIR™ (20 g/day) every morning. After the intervention, there were 28 days of wash-out, during which time the subjects did not receive further interventions. After supplementation with SYNKEFIR™, the exercise time to exhaustion was significantly greater than that before ingestion (p = 0.0001) and higher than that in the Placebo group by 1.29-fold (p = 0.0004). In addition, compared with the Placebo group, the SYNKEFIR™ administration group had significantly lower lactate levels in the exercise and recovery (p < 0.05). However, no significant difference was observed in the changes in the gut microbiota. Although no significant changes in body composition were found, SYNKEFIR™ did not cause adverse reactions or harm to the participants’ bodies. In summary, 28 days of supplementation with SYNKEFIR™ significantly improved exercise performance, reduced the production of lactic acid after exercise, and accelerated recovery while also not causing any adverse reactions. Full article

The step-size parameter and the equalizer’s tap length are the system parameters in the blind adaptive equalization design. Choosing a large step-size parameter causes the equalizer to converge faster compared with applying a smaller value for the step size parameter. However, a higher step-size parameter leaves the system with a higher residual inter-symbol-interference (ISI) than does a lower step-size parameter. The equalizer’s tap length should be set large enough to compensate for the channel distortions. However, since the channel parameters are unknown, the required equalizer’s tap length is also unknown. The system parameters are usually designed via simulation trials, in such a way that the equalizer’s performance from the residual ISI point of view reaches a system desired residual ISI level. Recently, a closed-form approximated expression was derived for the residual ISI as a function of the system parameters, input sequence statistics and channel power. This expression was obtained under the assumption having a value for the equalizer’s tap length that is sufficient to compensate for the channel distortions. Based on this approximated expression, the outcome from the step-size parameter multiplied by the equalizer’s tap length can be derived when the residual ISI is given. By choosing a step-size parameter, we automatically have also the value for the equalizer’s tap length which might now not be large enough to compensate for the channel distortions and thus leaving the system with a higher residual ISI than the required one. In this work, we derive an expression that sets a condition on the equalizer’s tap length based on the input sequence statistics, on the chosen equalizer’s characteristics and required residual ISI. In addition, highlights are supplied on how to set the equalizer’s tap length for different channel cases based on this new derived expression. The findings are accompanied by simulation results. Full article

Glaucoma, an eye disease, occurs due to Retinal damages and it is an ordinary cause of blindness. Most of the available examining procedures are too long and require manual instructions to use them. In this work, we proposed a multi-level deep convolutional neural network (ML-DCNN) architecture on retinal fundus images to diagnose glaucoma. We collected a retinal fundus images database from the local hospital. The fundus images are pre-processed by an adaptive histogram equalizer to reduce the noise of images. The ML-DCNN architecture is used for features extraction and classification into two phases, one for glaucoma detection known as detection-net and the second one is classification-net used for classification of affected retinal glaucoma images into three different categories: Advanced, Moderate and Early. The proposed model is tested on 1338 retinal glaucoma images and performance is measured in the form of different statistical terms known as sensitivity (SE), specificity (SP), accuracy (ACC), and precision (PRE). On average, SE of 97.04%, SP of 98.99%, ACC of 99.39%, and PRC of 98.2% are achieved. The obtained outcomes are comparable to the state-of-the-art systems and achieved competitive results to solve the glaucoma eye disease problems for complex glaucoma eye disease cases. Full article

Probiotics have been rapidly developed for health promotion, but clinical validation of the effects on exercise physiology has been limited. In a previous study, Lactobacillus plantarum TWK10 (TWK10), isolated from Taiwanese pickled cabbage as a probiotic, was demonstrated to improve exercise performance in an animal model. Thus, in the current study, we attempted to further validate the physiological function and benefits through clinical trials for the purpose of translational research. The study was designed as a double-blind placebo-controlled experiment. A total of 54 healthy participants (27 men and 27 women) aged 20–30 years without professional athletic training were enrolled and randomly allocated to the placebo, low (3 × 10¹⁰ colony forming units (CFU)), and high dose (9 × 10¹⁰ CFU) TWK10 administration groups (n = 18 per group, with equal sexes). The functional and physiological assessments were conducted by exhaustive treadmill exercise measurements (85% VO_2max), and related biochemical indices were measured before and after six weeks of administration. Fatigue-associated indices, including lactic acid, blood ammonia, blood glucose, and creatinine kinase, were continuously monitored during 30 min of exercise and a 90 min rest period using fixed intensity exercise challenges (60% VO_2max) to understand the physiological adaptation. The systemic inflammation and body compositions were also acquired and analyzed during the experimental process. The results showed that TWK10 significantly elevated the exercise performance in a dose-dependent manner and improved the fatigue-associated features correlated with better physiological adaptation. The change in body composition shifted in the healthy direction for TWK10 administration groups, especially for the high TWK10 dose group, which showed that body fat significantly decreased and muscle mass significantly increased. Taken together, our results suggest that TWK10 has the potential to be an ergogenic aid to improve aerobic endurance performance via physiological adaptation effects. Full article