A chipless RFID tag for smart temporal applications

A compact, robust, chipless radio frequency identification (RFID) tag is proposed. Resonant elements patterned in a concentric fashion encode data in the spectral domain employing frequency shift encoding. The proposed tag encodes 28.25 data bits over a miniscule physical footprint of 25 × 25 mm². The formulated scheme is demonstrated to be viable for encoding of temporal variables. The electromagnetic performance of the presented design is investigated for different laminates: Rogers RT/duroid® 5880 and Taconic TLX‐0. Multiple tag prototypes employing a variety of substrates are realized and evaluated for in‐laboratory performance. The proposed design is compared with existing work reported in literature. Code density of 4.52 bits/cm² has been successfully achieved. The tag design operates from 3.07 to 10.6 GHz and is readily realizable on flexible laminates. Smart retail, intelligent packaging, adaptive ticketing, and similar time‐related applications can be materialized using the proposed tag.     

Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms

Clinical image processing plays a significant role in healthcare systems and is currently a widely used methodology. In carcinogenic diseases, time is crucial; thus, an image’s accurate analysis can help treat disease at an early stage. Ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) are common types of malignancies that affect both women and men. The number of cases of DCIS and LCIS has increased every year since 2002, while it still takes a considerable amount of time to recommend a controlling technique. Image processing is a powerful technique to analyze preprocessed images to retrieve useful information by using some remarkable processing operations. In this paper, we used a dataset from the Mammographic Image Analysis Society and MATLAB 2019b software from MathWorks to simulate and extract our results. In this proposed study, mammograms are primarily used to diagnose, more precisely, the breast’s tumor component. The detection of DCIS and LCIS on breast mammograms is done by preprocessing the images using contrast-limited adaptive histogram equalization. The resulting images’ tumor portions are then isolated by a segmentation process, such as threshold detection. Furthermore, morphological operations, such as erosion and dilation, are applied to the images, then a gray-level co-occurrence matrix texture features, Harlick texture features, and shape features are extracted from the regions of interest. For classification purposes, a support vector machine (SVM) classifier is used to categorize normal and abnormal patterns. Finally, the adaptive neuro-fuzzy inference system is deployed for the amputation of fuzziness due to overlapping features of patterns within the images, and the exact categorization of prior patterns is gained through the SVM. Early detection of DCIS and LCIS can save lives and help physicians and surgeons todiagnose and treat these diseases. Substantial results are obtained through cubic support vector machine (CSVM), respectively, showing 98.95% and 98.01% accuracies for normal and abnormal mammograms. Through ANFIS, promising results of mean square error (MSE) 0.01866, 0.18397, and 0.19640 for DCIS and LCIS differentiation during the training, testing, and checking phases.     

Smart-City-based Data Fusion Algorithm for Internet of Things

Increasingly, Wireless Sensor Networks (WSNs) are contributing enormous amounts of data. Since the recent deployments of wireless sensor networks in Smart City infrastructures, significant volumes of data have been produced every day in several domains ranging from the environment to the healthcare system to transportation. Using wireless sensor nodes, a Smart City environment may now be shown for the benefit of residents. The Smart City delivers intelligent infrastructure and a stimulating environment to citizens of the Smart Society, including the elderly and others. Weak, Quality of Service (QoS) and poor data performance are common problems in WSNs, caused by the data fusion method, where a small amount of bad data can significantly impact the total fusion outcome. In our proposed research, a WSN multi-sensor data fusion technique employing fuzzy logic for event detection. Using the new proposed Algorithm, sensor nodes will collect less repeated data, and redundant data will be used to increase the data's overall reliability. The network's fusion delay problem is investigated, and a minimum fusion delay approach is provided based on the nodes’ fusion waiting time. The proposed algorithm performs well in fusion, according to the results of the experiment. As a result of these discoveries, It is concluded that the algorithm describe here is effective and dependable instrument with a wide range of applications.     

Cyber Security and Privacy Issues in Industrial Internet of Things

The emergence of industry 4.0 stems from research that has received a great deal of attention in the last few decades. Consequently, there has been a huge paradigm shift in the manufacturing and production sectors. However, this poses a challenge for cybersecurity and highlights the need to address the possible threats targeting (various pillars of) industry 4.0. However, before providing a concrete solution certain aspect need to be researched, for instance, cybersecurity threats and privacy issues in the industry. To fill this gap, this paper discusses potential solutions to cybersecurity targeting this industry and highlights the consequences of possible attacks and countermeasures (in detail). In particular, the focus of the paper is on investigating the possible cyber-attacks targeting 4 layers of IIoT that is one of the key pillars of Industry 4.0. Based on a detailed review of existing literature, in this study, we have identified possible cyber threats, their consequences, and countermeasures. Further, we have provided a comprehensive framework based on an analysis of cybersecurity and privacy challenges. The suggested framework provides for a deeper understanding of the current state of cybersecurity and sets out directions for future research and applications.     

Multifunctional Flexible Biointerfaces for Simultaneous Colocalized Optophysiology and Electrophysiology

Recent developments in optophysiology techniques such as optogenetics have revolutionized the ability to actuate cell activity. Further combining optophysiology and electrophysiology will integrate the advantages from both optical and electrical modalities and yield enabling technologies that allow simultaneous monitoring of cellular activity in response to modulation, which are crucial for biomedical applications. However, multifunctional devices that can deliver optical stimuli to regions beneath the electrodes and perform simultaneous sensing remain largely unexplored. Existing transparent microelectrode technologies depend on external bulk optical instruments for optical interventions. Here, innovative monolithic integrated multifunctional microsystems are demonstrated by applying transparent nanogrid electrodes onto microscale light sources to permit simultaneous electrophysiology and optical modulation at the same anatomical site. The nanogrid electrodes have transmittances > 70% with a low normalized impedance of 5.9 Ω cm². Additional features of the devices include superior mechanical flexibility, minimized light‐induced electrical artifacts, and excellent biocompatibility. Ex vivo experiments demonstrate that the multifunctional devices can record abnormal heart rhythm in transgenic mouse hearts and simultaneously restore the sinus rhythm via optogenetic pacing. This work provides a versatile approach for constructing multifunctional colocalized biointerfaces containing crosstalk‐free optical and electrical modalities with expanded opportunities in both fundamental and applied biomedical research.     

Perceptual thresholds and electrode impedance in three retinal prosthesis subjects.

Three test subjects blind from retinitis pigmentosa were implanted with retinal prostheses as part of a FDA-approved clinical trial. The implant consisted of an extraocular unit that contained electronics for wireless data, power, and generation of stimulus current, and an intraocular unit that consisted of 16 platinum stimulating electrodes arranged in a 4 x 4 pattern within a silicone rubber substrate. The array was held to the retina by a small tack. The stimulator was connected to the array by a multiwire cable and was controlled by a computer based external system that allowed precise control over each electrode. Perception thresholds and electrode impedance were obtained on each electrode from the subjects over several months of testing. The electrode distance from the retina was determined from optical coherence tomography imaging of the array and retina. Across all subjects, average thresholds ranged from 24-702 microA (1-ms pulse). The data show that proximity to the retina played a role in determining the threshold and impedance, but only for electrodes that were greater than 0.5 mm from the retina.     

Simulation and validation of resin flow during manufacturing of composite panels containing embedded impermeable inserts with the VARTM process

Modern composite materials are becoming more and more advanced as engineers are better able to take advantage of their properties. In addition to their lighter weight and net‐shape manufacturing, current interest is to make these materials multifunctional. This may require one to insert various objects into the composite to achieve a variety of different goals. It is important to understand how these embedded objects will affect both the manufacturing and the structural integrity of the component. In this work, the effects of impermeable embedded inserts on the infusion stage of vacuum‐assisted resin transfer molding (VARTM) will be explored. In VARTM, one places a distribution media on top of the preform to aid the filling as the resin will first fill the face of the preform in contact with the distribution media and will then infuse the preform in the thickness direction. However, if one has an embedded impermeable insert in the thickness direction, it will obstruct the flow in the region below the embedded object. Several case studies are conducted to understand the effect of the geometry and placement of the embedded insert and the distribution media lay out and properties on the impregnation of the resin into the fiber preform. Finally, an approach is outlined to modify the layout of the distribution media in order to ensure a complete saturation of the preform under all but the most extreme conditions. The approach is validated with experiments. POLYM. COMPOS., 28:442–450, 2007. © 2007 Society of Plastics Engineers     

Speech understanding performance of cochlear implant subjects using time-frequency masking-based noise reduction

Cochlear implant (CI) recipients report severe degradation of speech understanding under noisy conditions. Most CI recipients typically can require about 10-25 dB higher signal-to-noise ratio than normal hearing (NH) listeners in order to achieve similar speech understanding performance. In recent years, significant emphasis has been put on binaural algorithms, which not only make use of the head shadow effect, but also have two or more microphone signals at their disposal to generate binaural inputs. Most of the CI recipients today are unilaterally implanted but they can still benefit from the binaural processing utilizing a contralateral microphone. The phase error filtering (PEF) algorithm tries to minimize the phase error variance utilizing a time-frequency mask for noise reduction. Potential improvement in speech intelligibility offered by the algorithm is evaluated with four different kinds of mask functions. The study reveals that the PEF algorithm which uses a contralateral microphone but unilateral presentation provides considerable improvement in intelligibility for both NH and CI subjects. Further, preference rating test suggests that CI subjects can tolerate higher levels of distortions than NH subjects, and therefore, more aggressive noise reduction for CI recipients is possible.     

Heat transfer on peristaltically induced Walter's B fluid flow

This paper look at the effects of heat transfer on peristaltic flow of Walter's B fluid in an asymmetric channel. The regular perturbation method is used to solve the governing equations by taking the wave number as the small parameter. Expressions for stream function, temperature distribution, and heat transfer coefficient are presented in explicit form. Solutions are analyzed graphically for different values of arising parameters. It has been found that these parameters affect considerably the considered flow characteristics. Results show that with an increase in the Eckert and Prandtl numbers, the temperature and heat transfer coefficient increase. Further, the absolute value of the heat transfer coefficient increases with an increasing viscoelastic parameter. Comparison with published results for viscous fluid is also presented. © 2012 Wiley Periodicals, Inc. Heat Trans Asian Res; Published online in Wiley Online Library ( wileyonlinelibrary.com/journal/htj ). DOI 10.1002/htj.21021