Search Results (29)

Models for must fermentation kinetics are investigated and used in simulations for enabling control strategies based on temperature adjustment during the alcoholic fermentation process using the estimated CO₂ rate. An acoustic emission digital processing approach for estimating the CO₂ rate in the must fermentation process is proposed and investigated. The hardware architecture was designed considering easy integration with other sensor networks, remote monitoring of the fermentation-related parameters, and wireless communication between the fermentation vessel and the processing unit. It includes a virtual CO₂ rate sensor, having as input the audio signal collected by a microphone and as output the estimated CO₂ rate. Digital signal processing performed on an embedded board involves acquisition, filtering, analysis, and feature extraction. Time domain-based methods for analyzing the audio signal were implemented. An experimental setup with a small fermentation vessel (100 L) was used for CO₂ rate monitoring during the fermentation of grape must. The estimated CO₂ rate data were fitted with the CO₂ rate profiles generated by simulating the models under similar conditions. Simulations for controlling fermentation kinetics through a CO₂ rate-based temperature control were performed and analyzed. The simulations indicate the proposed approach as valid for a closed-loop system implementation capable of controlling kinetics behavior using estimated CO₂ rate and temperature control. Full article

Computer Algebra Systems (CAS) currently lack an effective auditory representation, with most existing solutions relying on screen readers that provide limited functionality. This limitation prevents blind users from fully understanding and interpreting mathematical expressions, leading to confusion and self-doubt. This paper addresses the challenges blind individuals face when comprehending mathematical expressions within a CAS environment. We propose “Math for Everybody” (Math4e, version 1.0), a software module to reduce barriers for blind users in education. Math4e is a Sonification Module for CAS that generates a series of auditory tones, prosodic cues, and variations in audio parameters such as volume and speed. These resources are designed to eliminate ambiguity and facilitate the interpretation and understanding of mathematical expressions for blind users. To assess the effectiveness of Math4e, we conducted standardized tests employing the methodologies outlined in the Software Engineering Body of Knowledge (SWEBOK), International Software Testing Qualifications Board (ISTBQ), and ISO/IEC/IEEE 29119. The evaluation encompassed two scenarios: one involving simulated blind users and another with real blind users associated with the “Asociación de Invidentes Milton Vedado” foundation in Ecuador. Through the SAM methodology and verbal surveys (given the condition of the evaluated user), results are obtained, such as 90.56% for pleasure, 90.78% for arousal, and 91.56% for dominance, which demonstrates significant acceptance of the systems by the users. The outcomes underscored the users’ commendable ability to identify mathematical expressions accurately. Full article

A new prototype device to monitor breathing in children diagnosed with central sleep apnoea (CSA) was developed. CSA is caused by the failure of central nervous system signals to the respiratory muscles and results in intermittent breathing pauses during sleep. Children diagnosed with CSA require home respiration monitoring during sleep. Apnoea monitors initiate an audio alarm when the breath-to-breath respiration interval exceeds a preset time. This allows the child’s parents to attend to the child to ensure safety. The article describes the development of the monitor’s hardware, software, and evaluation. Features of the device include the detection of abnormal respiratory pauses and the generation of an associated alarm, the ability to record the respiratory signal and its storage using an on-board disk, miniaturised hardware, child-friendliness, cost-effectiveness, and ease of use. The device was evaluated on 10 healthy adult volunteers with a mean age of 46.6 years (and a standard deviation of 14.4 years). The participants randomly intentionally paused their breathing during the recording. The device detected and provided an alarm when the respiratory pauses exceeded the preset time. The respiration rates determined from the device closely matched the values from a commercial respiration monitor. The study indicated the peak-detection method of the respiration rate measurement is more robust than the zero-crossing method. Full article

Identification of different species of animals has become an important issue in biology and ecology. Ornithology has made alliances with other disciplines in order to establish a set of methods that play an important role in the birds’ protection and the evaluation of the environmental quality of different ecosystems. In this case, the use of machine learning and deep learning techniques has produced big progress in birdsong identification. To make an approach from AI-IoT, we have used different approaches based on image feature comparison (through CNNs trained with Imagenet weights, such as EfficientNet or MobileNet) using the feature spectrogram for the birdsong, but also the use of the deep CNN (DCNN) has shown good performance for birdsong classification for reduction of the model size. A 5G IoT-based system for raw audio gathering has been developed, and different CNNs have been tested for bird identification from audio recordings. This comparison shows that Imagenet-weighted CNN shows a relatively high performance for most species, achieving 75% accuracy. However, this network contains a large number of parameters, leading to a less energy efficient inference. We have designed two DCNNs to reduce the amount of parameters, to keep the accuracy at a certain level, and to allow their integration into a small board computer (SBC) or a microcontroller unit (MCU). Full article

This study explores the conventional ‘funneling’ method by introducing two extra locations beyond the virtual reality (VR) controller boundaries, terming it the extended funneling technique. Thirty-two participants engaged in a localization task, with their responses recorded using eye-tracking technology. They were tasked with localizing a virtual ping-pong ball as it bounced both within and outside their virtual hands on a virtual board. Both the experimental and control groups received simultaneous spatial audio and vibrotactile feedback. The experimental group received vibrotactile feedback with extended funneling, while the control group received vibrotactile feedback without funneling for comparison. The results indicate that the experimental group, benefiting from the extended funneling technique, demonstrated a significantly higher accuracy rate (41.79%) in localizing audio–vibrotactile stimuli compared to the control group (28.21%). No significant differences emerged in embodiment or workload scores. These findings highlight the effectiveness of extended funneling for enhancing the localization of sensory stimuli in VR. Full article

Modern processors have improved performance but still face challenges such as power consumption, storage limitations, and the need for faster processing. The 16-bit Digital Signal Processors (DSPs) accelerate DSP applications by significantly enhancing speed and performance for tasks including audio processing, telecommunications, image and video processing, wireless communication, and consumer electronics. This paper presents a novel technique for accelerating DSP applications on a 16-bit processor by combining two methods: Block Random Access Memory (BRAM) and Distributed Arithmetic (DA). Integrating BRAM as a replacement for conventional RAM minimizes timing and critical route delays, improving processor efficiency and performance. Furthermore, the Distributed Arithmetic approach enhances performance and efficiency by utilizing precomputed lookup tables to expedite multiplication operations within the Arithmetic and Logic Unit (ALU). We use the Xilinx Vivado tool, a robust development environment for FPGA-based systems, for the design process and execute the hardware implementation using the Genesys2 Kintex board. The proposed work produces improved efficiency with a cycle per instruction of 2, where the delay is 2.009 ns, the critical path delay is 8.182 ns, and the power consumption is 4 mW. Full article

Healthcare providers use revenue cycle management (RCM) to track patient billing and revenue. The revenue cycle collects data from various systems and compiles it into a single RCM system connected to payers. Effective system integration improves revenue and financial stability. The aim is to assess RCM feasibility in Saudi Arabia’s governmental hospitals, examine financial management, and recommend practical implementation. In this study, healthcare leaders were interviewed face-to-face and via audio recording to collect qualitative data in response to semi-structured questions. Key informants from seven main hospitals were interviewed. Respondents understood RCM and identified internal and external challenges in hospital financial management. Government hospitals face accountability obstacles. Two of the seven surveyed hospitals operate business clinics using a fee-for-service model. The billing system is not integrated with the information system. The RCM system faces challenges such as unclear vision, lack of accountability, staff resistance, process redesign, and importance of project management. Despite these challenges, respondents still value RCM and recognize its importance in improving hospital revenue management. Effective implementation of RCM requires significant transformational processes, including vision, governance, accountability, proper training, and effective monitoring and evaluation processes. Communication should also be emphasized, and the patient’s perspective must be brought into focus. Involving all stakeholders can create direct and holistic patient benefits; therefore, bringing them on board is crucial. New approaches are required to enhance healthcare in Saudi Arabia, addressing gaps in revenue optimization and RCM. Future research should evaluate the move from government-funded to self-operated hospitals, providing a better understanding of the challenges and opportunities. Full article

Wearable assistant devices play an important role in daily life for people with disabilities. Those who have hearing impairments may face dangers while walking or driving on the road. The major danger is their inability to hear warning sounds from cars or ambulances. Thus, the aim of this study is to develop a wearable assistant device with edge computing, allowing the hearing impaired to recognize the warning sounds from vehicles on the road. An EfficientNet-based, fuzzy rank-based ensemble model was proposed to classify seven audio sounds, and it was embedded in an Arduino Nano 33 BLE Sense development board. The audio files were obtained from the CREMA-D dataset and the Large-Scale Audio dataset of emergency vehicle sirens on the road, with a total number of 8756 files. The seven audio sounds included four vocalizations and three sirens. The audio signal was converted into a spectrogram by using the short-time Fourier transform for feature extraction. When one of the three sirens was detected, the wearable assistant device presented alarms by vibrating and displaying messages on the OLED panel. The performances of the EfficientNet-based, fuzzy rank-based ensemble model in offline computing achieved an accuracy of 97.1%, precision of 97.79%, sensitivity of 96.8%, and specificity of 97.04%. In edge computing, the results comprised an accuracy of 95.2%, precision of 93.2%, sensitivity of 95.3%, and specificity of 95.1%. Thus, the proposed wearable assistant device has the potential benefit of helping the hearing impaired to avoid traffic accidents. Full article

Bare board AudioMoth recorders offer a low-cost, open-source solution to passive acoustic monitoring (PAM) but need protecting in an enclosure. We were concerned that the choice of enclosure may alter the spectral characteristics of recordings. We focus on polythene bags as the simplest enclosure and assess how their use affects acoustic metrics. Using an anechoic chamber, a series of pure sinusoidal tones from 100 Hz to 20 kHz were recorded on 10 AudioMoth devices and a calibrated Class 1 sound level meter. The recordings were made on bare board AudioMoth devices, as well as after covering them with different bags. Linear phase finite impulse response filters were designed to replicate the frequency response functions between the incident pressure wave and the recorded signals. We applied these filters to ~1000 sound recordings to assess the effects of the AudioMoth and the bags on 19 acoustic metrics. While bare board AudioMoth showed very consistent spectral responses with accentuation in the higher frequencies, bag enclosures led to significant and erratic attenuation inconsistent between frequencies. Few acoustic metrics were insensitive to this uncertainty, rendering index comparisons unreliable. Biases due to enclosures on PAM devices may need to be considered when choosing appropriate acoustic indices for ecological studies. Archived recordings without adequate metadata may potentially produce biased acoustic index values and should be treated cautiously. Full article

In digital audio systems, filters and equalizers are essential modules for audio improvement at the input and output stages. Due to their computational complexity, most audio tasks are processed with digital signal processors. Due to the fact that latency in audio systems is a critical specification and audio trends require higher sample rates, noise canceling, and bigger data sizes, having an independent high-resolution equalizer would reduce the computational power needed for audio systems. This research had the goal of designing and implementing a hardware architecture for a configurable filter bank based on finite impulse response (FIR) filters and a noise-cancellation stage with an inter-integrated circuit (I2C) communication interface, which allows the filter configuration. The system was implemented as a standalone integrated circuit (IC) for which its inputs were the inter-IC sound (I2S) bus control signals. The digital audio system was optimized to perform one-cycle convolutional operations by implementing a vector–vector arithmetic logic unit. Furthermore, this applied research provides the register transfer level description and the functional verification of the digital design, the system-on-chip (SoC) implementation in TSMC 180 nm technology, and the post-silicon validation with a printed circuit board for testing the output digital signals of the system. Full article

The introduction of artificial neural networks to speech recognition applications has sparked the rapid development and popularization of digital assistants. These digital assistants constantly monitor the audio captured by a microphone for a small set of keywords. Upon recognizing a keyword, a larger audio recording is saved and processed by a separate, more complex neural network. Deep neural networks have become an effective tool for keyword spotting. Their implementation in low-cost edge devices, however, is still challenging due to limited resources on board. This research demonstrates the process of implementing, modifying, and training neural network architectures for keyword spotting. The trained models are also subjected to post-training quantization to evaluate its effect on model performance. The models are evaluated using metrics relevant to deployment on resource-constrained systems, such as model size, memory consumption, and inference latency, in addition to the standard comparisons of accuracy and parameter count. The process of deploying the trained and quantized models is also explored through configuring the microcontroller or FPGA onboard the edge devices. By selecting multiple architectures, training a collection of models, and comparing the models using the techniques demonstrated in this research, a developer can find the best-performing neural network for keyword spotting given the constraints of a target embedded system. Full article

Cardiac and respiratory diseases are the primary causes of health problems. If we can automate anomalous heart and lung sound diagnosis, we can improve the early detection of disease and enable the screening of a wider population than possible with manual screening. We propose a lightweight yet powerful model for simultaneous lung and heart sound diagnosis, which is deployable in an embedded low-cost device and is valuable in remote areas or developing countries where Internet access may not be available. We trained and tested the proposed model with the ICBHI and the Yaseen datasets. The experimental results showed that our 11-class prediction model could achieve 99.94% accuracy, 99.84% precision, 99.89% specificity, 99.66% sensitivity, and 99.72% F1 score. We designed a digital stethoscope (around USD 5) and connected it to a low-cost, single-board-computer Raspberry Pi Zero 2W (around USD 20), on which our pretrained model can be smoothly run. This AI-empowered digital stethoscope is beneficial for anyone in the medical field, as it can automatically provide diagnostic results and produce digital audio records for further analysis. Full article

People with Alzheimer’s disease are at risk of malnutrition, overeating, and dehydration because short-term memory loss can lead to confusion. They need a caregiver to ensure they adhere to the main meals of the day and are properly hydrated. The purpose of this paper is to present an artificial intelligence system prototype based on deep learning algorithms aiming to help Alzheimer’s disease patients regain part of the normal individual comfort and independence. The proposed system uses artificial intelligence to recognize human activity in video, being able to identify the times when the monitored person is feeding or hydrating, reminding them using audio messages that they forgot to eat or drink or that they ate too much. It also allows for the remote supervision and management of the nutrition program by a caregiver. The paper includes the study, search, training, and use of models and algorithms specific to the field of deep learning applied to computer vision to classify images, detect objects in images, and recognize human activity video streams. This research shows that, even using standard computational hardware, neural networks’ training provided good predictive capabilities for the models (image classification 96%, object detection 74%, and activity analysis 78%), with the training performed in less than 48 h, while the resulting model deployed on the portable development board offered fast response times—that is, two seconds. Thus, the current study emphasizes the importance of artificial intelligence used in helping both people with Alzheimer’s disease and their caregivers, filling an empty slot in the smart assistance software domain. Full article

The role of the community pharmacist has evolved to include the provision of more clinical services for patients. Those people who have stable chronic conditions will be managed in community pharmacies. This qualitative study used semi-structured in-depth interviews to understand the potential of providing additional patient-centred care for patients with stable chronic conditions in community pharmacies and identify potential limitations of this approach. Participants were recruited from Welsh Government, Local Health Boards (LHBS), Community Pharmacy Wales (CPW) and the Royal Pharmaceutical Society Wales (RPSW). The interviews were audio-recorded, transcribed verbatim, and analysed thematically. Eight interviews were conducted. The identified themes were as follows: (1) inconsistency and bureaucracy in commissioning pharmacy services; (2) availability of funding and resources; (3) disagreement and uncertainty about the contribution of the community pharmacy sector; (4) continuity of patient medical information and fragmented care; (5) accessibility, capacity and facilities in community pharmacy; (6) pharmacy education and clinical expertise, and (7) patient acceptability. It was clear that the potential benefit of managing stable chronic diseases in community pharmacies was recognised; however, several limitations expressed by stakeholders of pharmacy services need to be considered prior to moving forward. Full article

This paper reflects on an Ethical Review Board’s (ERB) established structure of practice throughout a student-led project. We use the research project as a means of exploring the three questions set by the Editors, Fox and Busher, regarding the role of ERBs throughout the research process. We gained full university-level ethical approval in October 2020. This project initially focused on collecting data from students, from a UK university. The participatory way we collaborated with both undergraduates and postgraduates illuminated their individual unique perspectives and successfully facilitated their agentive contributions. This required on-going simultaneous negotiation of predetermined ethical procedures through the ERB. We termed this iterative process ‘circumnavigating the revolving door’ as it summarised revisiting ethical approval in the light of requests from our student participants. The participants were also invited to be part of the analysis and dissemination phase of this research. Original data collected related to personalised experiences of learning during the on-going global pandemic. The philosophical approach adopted was through an adaptation of Photovoice. That is, with limited direction by the researchers, the participants were invited to construct images (photos or hand drawn pictures), with captions (written text or voice), to explore their own educative circumstances. With this in mind, this paper explores the students’ participatory agency throughout this visual methods project through three lenses: namely, the appropriateness of ethical practices within a contextualised scenario (i.e., researching learning during lockdown in a higher educational institution); how the ethical process of an educational establishment supported the dynamic and iterative nature of participant-led research; and finally, how the original researchers’ experiences can inform ethical regulations and policy, both nationally and internationally. The circumnavigation of the revolving door of participatory ethics has proved invaluable during this research. This iterative cycle was necessary to incorporate the students (or co-researchers) suggested contributions. One example includes gaining the ERB’s approval, post full approval, for participants to audio record their own captions for a public facing website. From originally welcoming the students as participants, to facilitating them to become agentive co-researchers, it became increasingly important to provide them with opportunities to be actively involved in all parts of the research process. The reciprocal iterative relationship developed between co-researcher, researchers and the ERB served to strengthen the outcomes of the project. Full article