High Sensitivity of Dielectrically Modulated Tunnel Field Effect Transistor for Biosensor Applications.

The Dielectrically Modulated Full Gate Tunnel Field Effect Transistor (FET) with dual nanocavities, as described in the paper, is a novel device designed as a label-free biosensor for detecting cancer cell biomolecules. This biosensor utilizes the principles of field-effect transistors and incorporates nanocavities to enhance the detection sensitivity. The simulations are conducted using the Silvaco Atlas model, which allowed for the analysis of the device's electrical characteristics in the presence of various cancer cell biomolecules. The performance of the proposed device is evaluated using several sensing metrics, including current, threshold voltage, and subthreshold slope. These metrics are examined to assess their sensitivity to the presence of different cancer cell biomolecules. By analyzing these electrical characteristics, we can able to determine the device's ability to detect and differentiate between specific biomolecules associated with cancer cells. One important aspect discussed in the paper is the incorporation of nanocavities in the device design. These nanocavities have a significant impact on enhancing the sensing capabilities of the biosensor. The paper also introduces the concept of the filling factor parameter, which describes the fraction of the nanocavity volume occupied by the cancer cell biomolecules. This parameter plays a crucial role in achieving optimal sensing performance. Overall, the paper presents a comprehensive analysis of the proposed Dielectrically Modulated Full gate Tunnel FET embedded with dual nanocavities as a label-free biosensor for cancer cell biomolecules. The simulations conducted using the Silvaco Atlas model provide valuable insights into the device's electrical characteristics and its sensitivity to different biomolecules. The study emphasizes the significance of nanocavities and their filling factor parameter in achieving enhanced sensing performance for cancer cell detection.

Editorial Topical Collection: "Explainable and Augmented Machine Learning for Biosignals and Biomedical Images".

Machine learning (ML) is a well-known subfield of artificial intelligence (AI) that aims at developing algorithms and statistical models able to empower computer systems to automatically adapt to a specific task through experience or learning from data [...].

A Novel Prognostic Model Using Chaotic CNN with Hybridized Spoofing for Enhancing Diagnostic Accuracy in Epileptic Seizure Prediction.

Epileptic seizure detection has undergone progressive advancements since its conception in the 1970s. From proof-of-concept experiments in the latter part of that decade, it has now become a vibrant area of clinical and laboratory research. In an effort to bring this technology closer to practical application in human patients, this study introduces a customized approach to selecting electroencephalogram (EEG) features and electrode positions for seizure prediction. The focus is on identifying precursors that occur within 10 min of the onset of abnormal electrical activity during a seizure. However, there are security concerns related to safeguarding patient EEG recordings against unauthorized access and network-based attacks. Therefore, there is an urgent need for an efficient prediction and classification method for encrypted EEG data. This paper presents an effective system for analyzing and recognizing encrypted EEG information using Arnold transform algorithms, chaotic mapping, and convolutional neural networks (CNNs). In this system, the EEG time series from each channel is converted into a 2D spectrogram image, which is then encrypted using chaotic algorithms. The encrypted data is subsequently processed by CNNs coupled with transfer learning (TL) frameworks. To optimize the fusion parameters of the ensemble learning classifiers, a hybridized spoofing optimization method is developed by combining the characteristics of corvid and gregarious-seeking agents. The evaluation of the model's effectiveness yielded the following results: 98.9 ± 0.3% accuracy, 98.2 ± 0.7% sensitivity, 98.6 ± 0.6% specificity, 98.6 ± 0.6% precision, and an F1 measure of 98.9 ± 0.6%. When compared with other state-of-the-art techniques applied to the same dataset, this novel strategy demonstrated one of the most effective seizure detection systems, as evidenced by these results.

A Deep Learning-Based Framework for Retinal Disease Classification.

This study addresses the problem of the automatic detection of disease states of the retina. In order to solve the abovementioned problem, this study develops an artificially intelligent model. The model is based on a customized 19-layer deep convolutional neural network called VGG-19 architecture. The model (VGG-19 architecture) is empowered by transfer learning. The model is designed so that it can learn from a large set of images taken with optical coherence tomography (OCT) and classify them into four conditions of the retina: (1) choroidal neovascularization, (2) drusen, (3) diabetic macular edema, and (4) normal form. The training datasets (taken from publicly available sources) consist of 84,568 instances of OCT retinal images. The datasets exhibit all four classes of retinal disease mentioned above. The proposed model achieved a 99.17% classification accuracy with 0.995 specificities and 0.99 sensitivity, making it better than the existing models. In addition, the proper statistical evaluation is done on the predictions using such performance measures as (1) area under the receiver operating characteristic curve, (2) Cohen's kappa parameter, and (3) confusion matrix. Experimental results show that the proposed VGG-19 architecture coupled with transfer learning is an effective technique for automatically detecting the disease state of a retina.

Design and Analysis of Bio-Inspired Micro-Needle for Drug Delivery Applications.

Years of research show that the Trans-dermal drug delivery (TDD) route showed promising results due to good immunogenic responses. In this paper, we have proposed a bio-inspired micro-needle suggested by a snake belonging to the family of Elapids, since they inject venom with high pressures during the bite. The proposed micro-needle is strong enough to puncture the skin and withstand different kinds of loads during the insertion. The proposed micro-needle is of [Formula: see text] length, and the maximum compressive, buckling, bending, load it can handle are 0.27N, 0.16N, 0.024N respectively. The proposed micro-needle (MN) has an inner channel diameter of 44 [Formula: see text] and it gives a flow rate of [Formula: see text]/s. In our work, we have modeled a substrate of epidermis and dermis as a porous medium with porosity and permeability as 0.74, [Formula: see text] respectively. The porosity and permeability are calculated using an SEM image of the human dermis consisting of only collagen fibers and empty pores. We have applied Darcy's law to the modeled substrate and obtained the velocity field of the drug administrated. The diffusion study of Doxorubicin ( 87 µ mol/l) is carried out using Darcy velocity field and concentration gradient.

An Affordable Streamflow Measurement Technique Based on Delay and Sum Beamforming.

At the local scale, environmental parameters often require monitoring by means of affordable measuring techniques and technologies given they need to be frequently surveyed. Streamflow in riverbeds or in channels is a hydrological variable that needs to be monitored in order to keep the runoff regimes under control and somehow forecast floods, allowing prevention of damage for people and infrastructure. Moreover, measuring such a variable is always extremely important for the knowledge of the environmental status of connected aquatic ecosystems. This paper presents a new approach to assessing hydrodynamic features related to a given channel by means of a beamforming technique that was applied to video sensing. Different features have been estimated, namely the flow velocity, the temperature, and the riverbed movements. The applied beamforming technique works on a modified sum and delay method, also using the Multiple Signal Classification algorithm (MUSIC), by acting as Synthetic Aperture Radar (SAR) post-processing. The results are very interesting, especially compared to the on-site measured data and encourage the use of affordable video sensors located along the channel or river course for monitoring purposes. The paper also illustrates the use of beamforming measurements to be calibrated by means of conventional techniques with more accurate data. Certainly, the results can be improved; however, they indicate some margins of improvements and updates. As metrics of assessment, a histogram of greyscale/pixels was adopted, taking into account the example of layers and curve plots. They show changes according to the locations where the supporting videos were obtained.

About 3D Incompressible Flow Reconstruction from 2D Flow Field Measurements.

In this paper, an assessment of the uncertainty affecting a hybrid procedure (experimental/numerical) is carried out to validate it for industrial applications, at the least. The procedure in question serves to depict 3D incompressible flow fields by using 2D measurements of it and computing the third velocity component by means of the continuity equation. A quasi-3D test case of an incompressible flow has been inspected in the wake of a NACA 0012 airfoil immersed in a forced flow of water running in a rectangular open channel. Specifically, starting from a 2D measurement data in planes orthogonal to the stream-wise direction, the computational approach can predict the third flow velocity component. A 3D ADV instrument has been utilized to measure the flow field, but only two velocity components have been considered as measured quantities, while the third one has been considered as reference with which to compare the computed component from the continuity equation to check the accuracy and validity of the hybrid procedure. At this aim, the uncertainties of the quantities have been evaluated, according to the GUM, to assess the agreement between experiments and predictions, in addition to other metrics. This aspect of uncertainty is not a technical sophistication but a substantial way to bring to the use of a 1D and 2D measurement system in lieu of a 3D one, which is costly in terms of maintenance, calibration, and economic issues. Moreover, the magnitude of the most relevant flow indicators by means of experimental data and predictions have been estimated and compared, for further confirmation by means of a supervised learning classification. Further, the sensed data have been processed, by means of a machine learning algorithm, to express them in a 3D way along with accuracy and epoch metrics. Two additional metrics have been included in the effort to show paramount interest, which are a geostatistical estimator and Sobol sensitivity. The statements of this paper can be used to design and test several devices for industrial purposes more easily.

COFE-Net: An ensemble strategy for Computer-Aided Detection for COVID-19.

Biomedical images contain a large volume of sensor measurements, which can reveal the descriptors of the disease under investigation. Computer-based analysis of such measurements helps detect the disease, and thereby swiftly aid medical professionals to choose adequate therapy. In this paper, we propose a robust deep learning ensemble framework known as COVID Fuzzy Ensemble Network, or COFE-Net. This strategy is proposed for the task of COVID-19 screening from chest X-rays (CXR) and CT Scans, as a part of Computer-Aided Detection (CADe) for medical practitioners. We leverage the strategy of Transfer Learning for Convolutional Neural Networks (CNNs) widely adopted in recent literature, and further propose an efficient ensemble network for their combination. The principles of fuzzy logic have been leveraged to combine the measured decision scores generated by three state-of-the-art CNNs - Inception V3, Inception ResNet V2 and DenseNet 201 - through the Choquet fuzzy integral. Experimental results support the efficacy of our approach over empirical ensembling, as the fuzzy ensembling strategy for biomedical measurement consists of dynamic refactoring of the classifier ensemble weights on the fly, based upon the confidence scores for coalitions of inputs. This is the chief advantage of our biomedical measurement strategy over others as other methods do not adjust to the multiple generated measurements dynamically unlike ours.Impressive results on multiple datasets demonstrate the effectiveness of the proposed method. The source code of our proposed method is made available at: https://github.com/theavicaster/covid-cade-ensemble.

Video-Sensing Characterization for Hydrodynamic Features: Particle Tracking-Based Algorithm Supported by a Machine Learning Approach.

The efficient and reliable monitoring of the flow of water in open channels provides useful information for preventing water slow-downs due to the deposition of materials within the bed of the channel, which might lead to critical floods. A reliable monitoring system can thus help to protect properties and, in the most critical cases, save lives. A sensing system capable of monitoring the flow conditions and the possible geo-environmental constraints within a channel can operate using still images or video imaging. The latter approach better supports the above two features, but the acquisition of still images can display a better accuracy. To increase the accuracy of the video imaging approach, we propose an improved particle tracking algorithm for flow hydrodynamics supported by a machine learning approach based on a convolutional neural network-evolutionary fuzzy integral (CNN-EFI), with a sub-comparison performed by multi-layer perceptron (MLP). Both algorithms have been applied to process the video signals captured from a CMOS camera, which monitors the water flow of a channel that collects rain water from an upstream area to discharge it into the sea. The channel plays a key role in avoiding upstream floods that might pose a serious threat to the neighboring infrastructures and population. This combined approach displays reliable results in the field of environmental and hydrodynamic safety.

Image Sensors for Wave Monitoring in Shore Protection: Characterization through a Machine Learning Algorithm.

Waves propagating on the water surface can be considered as propagating in a dispersive medium, where gravity and surface tension at the air-water interface act as restoring forces. The velocity at which energy is transported in water waves is defined by the group velocity. The paper reports the use of video-camera observations to study the impact of water waves on an urban shore. The video-monitoring system consists of two separate cameras equipped with progressive RGB CMOS sensors that allow 1080p HDTV video recording. The sensing system delivers video signals that are processed by a machine learning technique. The scope of the research is to identify features of water waves that cannot be normally observed. First, a conventional modelling was performed using data delivered by image sensors together with additional data such as temperature, and wind speed, measured with dedicated sensors. Stealth waves are detected, as are the inverting phenomena encompassed in waves. This latter phenomenon can be detected only through machine learning. This double approach allows us to prevent extreme events that can take place in offshore and onshore areas.

Accurate Spectral Estimation Technique Based on Decimated Linear Predictor for Leak Detection in Waterworks.

Rural pipelines dedicated to water distribution, that is, waterworks, are essential for agriculture, notably plantations and greenhouse cultivation. Water is a primary resource for agriculture, and its optimized management is a key aspect. Saving water dispersion is not only an economic problem but also an environmental one. Spectral estimation of leakage is based on processing signals captured from sensors and/or transducers generally mounted on pipelines. There are different techniques capable of processing signals and displaying the actual position of leaks. Not all algorithms are suitable for all signals. That means, for pipelines located underground, for example, external vibrations affect the spectral response quality; then, depending on external vibrations/noises and flow velocity within pipeline, one should choose a suitable algorithm that fits better with the expected results in terms of leak position on the pipeline and expected time for localizing the leak. This paper presents findings related to the application of a decimated linear prediction (DLP) algorithm for agriculture and rural environments. In a certain manner, the application also detects the hydrodynamics of the water transportation. A general statement on the issue, DLP illustration, a real application and results are also included.

Infrared Monitoring of Oxygenation Process Generated by Robotic Verticalization in Bedridden People.

Bedridden people, especially at home, suffer from diverse pathologies beyond the main one that brings them to a specific position. Long-term cares are suitable at home to avoid congestions within hospital facilities. There are different technologies available to improve such people's conditions in their daily life. The standing posture is the key solution to enhance people's wellness amid the psychological burden due to the almost impossibility to be completely healed. The paper proposes the use of a polyfunctional and robotic bed capable of displaying many positions namely vertical, tilting, anti-trendelenburg with necessary graduation. A three-year monitoring of a patient, using a polyfunctional and robotic bed, suffering from amyotrophic lateral sclerosis (ALS), has been investigated. Different physiological parameters have been measured and, particularly, the variation of temperature has been measured in presence of body position connected to the robotic bed rotation that provokes biomechanical effort. It is demonstrated that certain body positions correspond to major and minor physical effort, hence major and minor oxygenation. An infrared camera has been used. As a positive result, the variation of posture has been delaying the increase of the pathological signs, because of better conditions.

Spatial and temporal classification of coastal regions using bioclimatic indices in a Mediterranean environment.

Bioclimatic indices combine atmospheric parameters to provide analytical indication of climatic features and their evolution in space and time that can directly relate with natural resource availability, distribution, and related bio-physical processes. The availability of bioclimatic information can provide natural resource managers with analytical means to assess the magnitude and temporal evolution of drought and climate change parameters that could affect the availability, demand and use of natural resources for various sectors. This paper presents a methodology to process bioclimatic data in the space and time domains for assessing the moisture/dryness level and water requirements of a region, and inform water resource planning and management decisions related to drought, climate variability and change. The methodology relies on a modular assembly of statistical tests and methods, and utilizes point scale measurements of meteorological data to perform the analysis of the spatial behavior of derived bioclimatic indicators at the continuous regional scale, and evaluate the significance of the temporal trends. Also, the article presents an application of the proposed methodology to a coastal area of southern Italy (the Apulia Region) that is characterized by recurring water supply limitations, involving the use of the popular De Martonne bioclimatic aridity index. The methodology allowed to obtain qualitative and quantitative information about the aridity level of the Apulia region, the identification of main bioclimatic zones, and the evaluation of spatial pattern and time evolution of aridity. The determination of bioclimatic zones showed that nearly 40% of the regional territory is characterized by dry sub-humid (Mediterranean) climate, about 30% by sub-humid climate, while nearly 10% and 20% are characterized by semi-arid and humid climates, respectively. The temporal analysis revealed that the Salento and the Ionian coastal zone are areas at risk of increasing aridity, with resulting impacts on the water supply and demand for irrigated agriculture.

A Sensing and Monitoring System for Hydrodynamic Flow Based on Imaging and Ultrasound.

A built environment, that also includes infrastructures, needs to be taken under control to prevent unexpected modifications, otherwise it could react as a loose cannon. Sensing techniques and technologies can come to the rescue of built environments thanks to their capabilities to monitor appropriately. This article illustrates findings related to monitoring a channel hydrodynamic behavior by means of sensors based on imaging and ultrasound. The ultrasound approach is used here to monitor the height of the water with respect to a maximum limit. Imaging treatment is here proposed to understand the flow velocity under the area to be considered. Since these areas can be covered by trash, an enhanced version of the particle image velocimetry technique has been implemented, allowing the discrimination of trash from water flow. Even in the presence of the total area occupied by trash, it is able to detect the velocity of particles underneath. Rainfall and hydraulic levels have been included and processed to strengthen the study.

Multispectral medical image fusion scheme based on hybrid contourlet and shearlet transform domains.

Multimodal medical image sensor fusion has revolutionized the medical analysis by improving the precision of computer assisted diagnosis. This is incorporated by highlighting the complementary information while minimizing the redundant content in the fused images from various biomedical sensors like MRI, Computed Tomography, and Positron Emission Tomography/Single-Photon Emission Computerized Tomography. Multispectral image fusion is a special case of multimodal fusion which serves to encompass both spatial and spectral details in the fused image. This paper presents a hybrid sub-band decomposition scheme for multispectral image fusion comprising of non-subsampled contourlet transform and shearlet transform domains. The pre-processing stage involves color transformation of an input multispectral image from red-green-blue to YIQ color space. Thereafter, both the source images (i.e., panchromatic and multispectral images) after sub-band decomposition are processed via the application of contrast enhancement, weighted-principal component analysis, and max-max algorithms. The low frequency coefficients are processed via phase congruency whereas a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The objective assessment of image quality has been carried out using various reference and no-reference based performance metrics. The distinguishing fusion response of the proposed hybrid scheme has been validated by the comparisons done with the other fusion approaches.

A Comparison between the Decimated Padé Approximant and Decimated Signal Diagonalization Methods for Leak Detection in Pipelines Equipped with Pressure Sensors.

Pipelines conveying fluids are considered strategic infrastructures to be protected and maintained. They generally serve for transportation of important fluids such as drinkable water, waste water, oil, gas, chemicals, etc. Monitoring and continuous testing, especially on-line, are necessary to assess the condition of pipelines. The paper presents findings related to a comparison between two spectral response algorithms based on the decimated signal diagonalization (DSD) and decimated Padé approximant (DPA) techniques that allow to one to process signals delivered by pressure sensors mounted on an experimental pipeline.

Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer's disease.

Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

Tactile-Foot Stimulation Can Assist the Navigation of People with Visual Impairment.

Background. Tactile interfaces that stimulate the plantar surface with vibrations could represent a step forward toward the development of wearable, inconspicuous, unobtrusive, and inexpensive assistive devices for people with visual impairments. Objective. To study how people understand information through their feet and to maximize the capabilities of tactile-foot perception for assisting human navigation. Methods. Based on the physiology of the plantar surface, three prototypes of electronic tactile interfaces for the foot have been developed. With important technological improvements between them, all three prototypes essentially consist of a set of vibrating actuators embedded in a foam shoe-insole. Perceptual experiments involving direction recognition and real-time navigation in space were conducted with a total of 60 voluntary subjects. Results. The developed prototypes demonstrated that they are capable of transmitting tactile information that is easy and fast to understand. Average direction recognition rates were 76%, 88.3%, and 94.2% for subjects wearing the first, second, and third prototype, respectively. Exhibiting significant advances in tactile-foot stimulation, the third prototype was evaluated in navigation tasks. Results show that subjects were capable of following directional instructions useful for navigating spaces. Conclusion. Footwear providing tactile stimulation can be considered for assisting the navigation of people with visual impairments.

Analysis of absence seizure generation using EEG spatial-temporal regularity measures.

Epileptic seizures are thought to be generated and to evolve through an underlying anomaly of synchronization in the activity of groups of neuronal populations. The related dynamic scenario of state transitions is revealed by detecting changes in the dynamical properties of Electroencephalography (EEG) signals. The recruitment procedure ending with the crisis can be explored through a spatial-temporal plot from which to extract suitable descriptors that are able to monitor and quantify the evolving synchronization level from the EEG tracings. In this paper, a spatial-temporal analysis of EEG recordings based on the concept of permutation entropy (PE) is proposed. The performance of PE are tested on a database of 24 patients affected by absence (generalized) seizures. The results achieved are compared to the dynamical behavior of the EEG of 40 healthy subjects. Being PE a feature which is dependent on two parameters, an extensive study of the sensitivity of the performance of PE with respect to the parameters' setting was carried out on scalp EEG. Once the optimal PE configuration was determined, its ability to detect the different brain states was evaluated. According to the results here presented, it seems that the widely accepted model of "jump" transition to absence seizure should be in some cases coupled (or substituted) by a gradual transition model characteristic of self-organizing networks. Indeed, it appears that the transition to the epileptic status is heralded before the preictal state, ever since the interictal stages. As a matter of fact, within the limits of the analyzed database, the frontal-temporal scalp areas appear constantly associated to PE levels higher compared to the remaining electrodes, whereas the parieto-occipital areas appear associated to lower PE values. The EEG of healthy subjects neither shows any similar dynamic behavior nor exhibits any recurrent portrait in PE topography.

Prediction of quantitative intrathoracic fluid volume to diagnose pulmonary oedema using LabVIEW.

Pulmonary oedema is a life-threatening disease that requires special attention in the area of research and clinical diagnosis. Computer-based techniques are rarely used to quantify the intrathoracic fluid volume (IFV) for diagnostic purposes. This paper discusses a software program developed to detect and diagnose pulmonary oedema using LabVIEW. The software runs on anthropometric dimensions and physiological parameters, mainly transthoracic electrical impedance (TEI). This technique is accurate and faster than existing manual techniques. The LabVIEW software was used to compute the parameters required to quantify IFV. An equation relating per cent control and IFV was obtained. The results of predicted TEI and measured TEI were compared with previously reported data to validate the developed program. It was found that the predicted values of TEI obtained from the computer-based technique were much closer to the measured values of TEI. Six new subjects were enrolled to measure and predict transthoracic impedance and hence to quantify IFV. A similar difference was also observed in the measured and predicted values of TEI for the new subjects.