There are privacy[1] concerns with using smart wearables for health monitoring. One of the primary ethical concerns with wearable digital health technology is data collection[2] and storage. As these devices track and monitor personal health data, such as heart rate, activity levels, and sleep patterns, they collect a significant amount of personal data. This data is often stored in the cloud, and third-party access is granted to companies and researchers who may use this data for various purposes. This can lead to concerns regarding data privacy and security.[3] There are ongoing efforts[4] to address these concerns, including the development of industry self-regulation and voluntary codes of conduct.[5] However, there is still a need for stronger regulations and harmonization between states to ensure the proper use and protection of personal health data.[6] Regulations for smart wearables vary depending on the region. In Europe, there is no separate regulation for smart wearables, but it is mandatory to adhere to the applicable EU directive or EU Regulation such as the General Data Protection Regulation (GDPR). In the US, the FDA views wearable devices as ‘general wellness’ products that promote wellness and present very low risk to the user’s safety, thus they refrain from regulating wearable devices. Wearable OEMs are either self-regulating their offerings or fitting into medical device compliance guidelines. Most wearable devices fall outside federal regulatory frameworks, but they could still be subject to state consumer protection laws and other state-level regulatory frameworks, which vary per jurisdiction.

[1] Kapoor, Vidhi, et al. "Privacy issues in wearable technology: An intrinsic review." Proceedings of the International Conference on Innovative Computing & Communications (ICICC). 2020.

[2] Huarng, Kun-Huang, Tiffany Hui-Kuang Yu, and Cheng fang Lee. "Adoption model of healthcare wearable devices." Technological Forecasting and Social Change 174 (2022): 121286.

[3] Barua, Arup, et al. "Security and privacy threats for Bluetooth low energy in IoT and wearable devices: A comprehensive survey." IEEE Open Journal of the Communications Society 3 (2022): 251-281.

[4] Chikwetu, Lucy, et al. "Does deidentification of data from wearable devices give us a false sense of security? A systematic review." The Lancet Digital Health (2023).

[5] Paul, Metty, et al. "Digitization of healthcare sector: A study on privacy and security concerns." ICT Express (2023).

[6] Boumpa, E., Tsoukas, V., Gkogkidis, A., Spathoulas, G., Kakarountas, A. (2022). Security and Privacy Concerns for Healthcare Wearable Devices and Emerging Alternative Approaches. In: Gao, X., Jamalipour, A., Guo, L. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-031-06368-8_2.

In recent years, the demand for efficient and sustainable agricultural practices has been leveraged leading to smart farming practices. These practices aim to enhance agricultural processes, productivity and minimize resource wastage. One of the crucial challenges faced by farmers is the uneven distribution of soil humidity and pH across their agricultural land. Further, the irregularity in soil moisture content and pH can lead to poor crop performance, water wastage, and increased resource utilization. In this work, an Internet of things based smart sensor nodes is developed which consists of humidity and pH sensors to ensure the efficient management of water and soil conditions across an entire farm. Also, an array of humidity and pH sensors are placed across the farm and these units work independently as they have their own controller and battery unit. The developed device is integrated with a solar cell which charges the battery. Further, the data acquired from these sensors are wirelessly transmitted to base station and it gathers the information of each unit including humidity levels, pH values, signal strength and energy supply. This information is processed in the base station and graphical overview of the farm with acquired information is represented which provides farmer with a real view insight to identify the areas with poor humidity and pH conditions. The data is transmitted to IoT cloud offering the farmer to monitor their farm from remote location and in cases where humidity levels drops drastically and remains unchecked for more than two hours, the system triggers an alert. This mechanism makes sure that farmers are notified of potential issues, allowing them to prevent crop damage and optimize resource usage.

The human population continues to grow, and specific efforts must be made in order to meet foreseeable food demands. In this paper, it is suggested that an IoT-based fuzzy control system has been used for smart soil monitoring systems. The proposed study is based on the semi-arid regions of India. The real-time data is collected from NPK sensors which are suitable for detecting the content of nitrogen, phosphorus, and potassium in the soil that helps in determining the fertility of the soil thereby facilitating the systematic assessment of the soil condition, and a fuzzy classifier is used to classify the data into three parameters such as sodium, potassium, and calcium which is based on the proposed model which gets trained from a dataset and then select the optimal crop based on sensor results. With the aid of this system, a farmer would be able to monitor soil health in real time environment and also track the growth of their plants. Farmers will be able to enhance productivity while decreasing resource waste with the aid of an IoT-enabled fuzzy system. The experimental data has been collected from Mahoba district, Uttar Pradesh provinces in India, the results show that the proposed system is a more accurate, reliable and precise concept which is used for precision farming that will certainly enhance the overall production of the crop with better quality. These results obtained with the help of the proposed model system have been compared with the existing one with accuracy of the data that has been improved and well accepted.

Heart-related ailments have become a significant cause of death around the globe nowadays. Due to lifestyle changes, people of almost all age brackets face these issues. Preventing and treating heart-related issues require electrocardiogram (ECG) monitoring of the patients. The study of patients' ECG signals helps doctors identify abnormal heart rhythm patterns by which screening problems like arrhythmia (irregular heart rhythm), myocardial infarction (heart attacks), and myocarditis (heart inflammation) are possible. The need for 24-hour heart rate monitoring leads to the development of wearable devices, and constant monitoring of ECG data leads to generating a large amount of data since wearable systems are resource-constrained regarding energy, memory, size, and computing capabilities. The optimization of biomedical monitoring systems is required to increase their efficiency. This project presents an ECG compression system to reduce the amount of data generated which reduces the energy consumption due to the transceiver, which is a significant part of the overall energy consumed. The proposed system uses hybrid Golomb-Rice coding for data compression which is a lossless data compression technique, the data compression is performed on the MIT BIH arrhythmia database, and the achieved compression ratio of the compression system is 2.89 and 3.6 for average and maximum values which when compared to the raw ECG samples requires less transmission cost in terms of power consumed.

In this paper, designs and optimization of wideband THz metamaterial absorbers (MMA) is proposed. By simulation, we reached four structures with absorptions higher than 50%, 70%, 80%, and 90% with relative absorption bandwidths (RABW) of 1.43, 1.29, 0.93, and 0.72, respectively. Terahertz absorbers can be used in many potential applications such as in imaging, energy harvesting, scattering reduction, and thermal sensing. Our intended application is to use an optimal absorber on a thermal detector for the detectivity in a wide THz range. Since a broadband absorption in the range of 0.3 to 2 terahertz is considered for use in medical imaging, the MMA with more than 50% absorption in the range of 0.35-2.1 THz has been selected to be achieved. The designs are also intended to have the capability to be implemented on different devices such as bolometers. The cost of the fabrication of the proposed absorbers is also low, because of the implementation of single-layer MMA design, and utilization of affordable and more accessible materials and techniques. Our proposed structure has a minimum feature size of 3 μm making the fabrication process convenient using the standard photolithography method as well. We used thin layers of Nickel as a metal for both single-layer pattern and ground layer which are placed on the front and back sides of the structure respectively. The Nickel thin film layers are deposited using the sputtering technique and are separated by a dielectric layer. The material chosen for the dielectric layer is SU8 which has proper properties and also has good adhesion to Nickel. Characterization of the fabricated absorber has been performed using a terahertz spectroscopy system, and the experimental results verified the high absorption of the sample.

Lipids are organic compounds widely distributed in nature and represent one of the four main classes of organic compounds of biological interest, along with carbohydrates, proteins and nucleic acids. In eukaryotes, lipids contribute to numerous cellular functions, ranging from energy storage to cell signalling [1]. Fourier Transform Infrared Spectroscopy (FT-IR) thanks to its ability to analyze cellular components at a molecular level can be particularly useful in investigating the biochemical features of the lipid content of cells and their changes induced by interaction with physicochemical external agents.

In the present work, we aim to investigate lipids extract from cells in order to compare the results obtained by using two different geometries that are usually available for the acquisition of FT-IR spectra for liquid samples [2]. In particular, for measurements in transmission geometry few microliters of lipids extracted from hepatocarcinoma cells [3, 4] and dissolved in methanol were placed on CaF₂ windows and spectra were thus acquired using the microscope stage of a Perkin Elmer Spectrum One spectrometer equipped with a mercury cadmium telluride (MCT) detector. This approach allows the collection of spectra in the range from 4000 to 1000 cm⁻¹. For measurements in Attenuated Total Reflectance (ATR) geometry, drops of lipid extracts were placed on the top of the diamond crystal of the Universal ATR accessory of the above-mentioned FT-IR spectrometer provided by a MIR TGS detector. In this case, spectra were collected in the 4000 to 650 cm⁻¹ wavenumber region. Multiple acquisitions of spectra were carried out, and statistical criteria were applied for monitoring and comparing them. The positive and negative aspects of the two examined acquisition modes are presented and discussed.

References

[1] Muro, E.; Atilla-Gokcumen, G.E.; Eggert, U.S. Lipids in cell biology: How can we understand them better? Mol. Biol. Cell 2014, 25, 1819–1823

[2] Errico, S.; Moggio, M.; Diano, N.; Portaccio, M.; Lepore, M.; Different experimental approaches for Fourier-Trasform infrared spectroscopy applications in biology and biotechnology: A selected choice of representative results. Biotechnol Appl Biochem. 2023, 70, 937–961.

[3] Sia,D.; Villanueva,A.; Friedman, S.L.; Llovet J.M. Liver Cancer Cell of Origin, Molecular Class, and Effects

on Patient Prognosis Gastroenterology 2017, 152, 745–76

[4] Bligh, E.G.; Dyer, W.J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 1959, 37, 911–917.

In this paper, we present a RISC-V RV32I based System on Chip (SoC) design approach using High Level Synthesis (HLS) tools. The proposed approach consists of three separate levels: The first one is an HLS design and simulation purely in C++. The second one is a Verilog simulation of the HLS generated Verilog implementation of the CPU core, a RAM unit initialized with a short assembly code, and a simple output port which simply forwards the output data to the simulation console. Finally, the third level is an implementation and testing of this SoC on an FPGA board running at a clock speed of 100 MHz. A sample C code is compiled using the GNU RISC-V compiler tool chain and tested on the HLS generated RISC-V RV32I core as well. The HLS design consists of a single C++ file with less than 300 lines, a single header file, and a testbench in C++. Our design objectives are (1) The C++ code should be easy to read for an average engineer, and (2) The coding style should dictate minimal area and minimal resource utilization without significantly degrading the code readability. The proposed system is implemented for two different I/O bus alternatives: (1) A traditional single clock cycle delay memory interface, and (2) The industry standard AXI bus. We present timing closure, resource utilization, and power consumption estimates. Furthermore, by using the open-source synthesis tool Yosys, we generate a CMOS gate-level design and provide gate count details. All design, simulation, and constraint files are publicly available in a GitHub repo. We also present a simple dual-core SoC design, but detailed multi-core designs and other advanced futures are planned for future research.

Optical Coherence Tomography (OCT) serves as a valuable imaging tool for visualizing the cross-section of a sample. In this study, we employed a portable version of OCT to determine the epidermal thickness and dermal extinction coefficient to understand the differences in the values obtained at different anatomical sites of the porcine skin. The epidermal thickness and extinction coefficients were found to be between 60 to 95 μm and 1 to 7 mm^-1, respectively. Among the anatomical regions studied, the ear region displayed the thinnest epidermis, whereas the leg region exhibited the thickest epidermis. Concurrently, the computed dermal extinction coefficient values for porcine skin indicated an ascending order as follows: Belly < Buttocks < Cheek < Leg < Ear. These findings hold immense potential as valuable diagnostic aids for both human and animal skin conditions. The versatility and portability of the OCT device used in this experiment further signify its potential for broader applications in various clinical and veterinary settings, enabling efficient and non-invasive assessments of skin health.

Eddy current testing instrument is the core equipment for non-destructive testing of key components in nuclear power plants, its performance is of great significance to ensure the safety of nuclear power units throughout their life cycle. At present, mainstream eddy current instruments use analog circuits for signal processing on the hardware architecture. The circuit structure is complex, and there are disadvantages such as high power consumption, high noise, and weak anti-interference ability. In order to solve the above problems and further improve the reliability of eddy current instruments, this paper creatively proposes a digital eddy current instrument architecture.In this architecture, ARM+FPGA is used as the core of signal processing, and DFT(Discrete Fourier transform) digital signal processing algorithms are used instead of traditional hardware detection circuits to complete the analysis of eddy current signals. The parallel DFT operation is realized in the algorithm, and up to 10 superimposed signals of different frequencies can be analyzed at the same time, which further improves the detection efficiency of the eddy current instrument.The test results show that the digital eddy current instrument designed in this paper greatly simplifies the hardware circuit, reduces the overall electronic noise level, reduces the power consumption, and improves the signal-to-noise ratio, stability and detection efficiency of the instrument. At the same time, the instrument supports BOBBIN, MRPC and ARRAY inspection technology, which can meet the NDT application needs of critical components in nuclear power plants.

Hand Pose Recognition presents significant challenges that need to be addressed, such as varying lighting conditions or complex backgrounds, which can hinder accurate and robust hand pose estimation. This can be mitigated by employing MediaPipe to facilitate the efficient extraction of representative landmarks from static images combined with the use of Convolutional Neural Networks. Extracting these landmarks from the hands mitigates the impact of lighting variability or the presence of complex backgrounds. However, the variability of the location and size of the hands is still not addressed by this process. Therefore, the use of processing modules to normalize these points regarding the location of the wrist and the zoom of the hands can significantly mitigate the effects of these variabilities. In all the experiments performed in this work based on American Sign Language alphabet datasets of 870, 27,000, and 87,000 images, the application of the proposed normalizations has resulted in significant improvements in the model performance in a resource-limited scenario. Particularly, under conditions of high variability applying both normalizations resulted in a performance increment of 45.08 %, increasing the accuracy from 43.94 ± 0.64 % to 89.02 ± 0.40 %.