The proposed work introduces a metasurface-based dual band stop filter having very high attenuation; thus making it suitable for wearable biomedical applications. The use of jeans substrate makes the structure perfectly wearable and suitable for biomedical applications. Each unit cell in the proposed structure comprises of an outer metallic strip and inner novel double dumbbell shaped resonators. Each unit cell has a dimension of 0.24λ0 × 0.24λ0 with a thickness of 0.002λ0 making it quite small and size efficient. The first stop band from this design is realized at exactly 2.45 GHz while the second stop band is achieved at 5.74 GHz with an attenuation greater than 30 dB at both the bands. To comprehend the reflection and transmission phenomena better, the surface current distribution on the patches is also examined. The study of the electric field distributions at the two bands reveals the roles of the patches used in the unit cell design. The structure has also been found to be angularly stable up to 75⁰ incidence.
This work presents a single layered metasurface (MS)-based slot type dual bandpass wearable filter with low insertion loss and sharp roll off performances. The substrate material selected is jeans, having a relative permittivity of 1.68; thereby making the design wearable in nature. The proposed structure’s unit cell comprises mainly three shapes of slots viz., a rectangular ring, a square and a dumbbell-shaped. The filter exhibits two transmission peaks at 2.45 GHz and 4.7 GHz, respectively and hence is well suited for biomedical applications at the ISM and IoT bands. Furthermore, the proposed filter also has a good stop band in between the transmission bands and hence can be useful for wearable applications. The 3-dB transmission bandwidths of the wearable filter have been computed as 1.21 GHz and 0.86 GHz respectively. The electric field distributions at the transmission peaks are studied to understand the roles of the various slots in the final design. The angular stability of the proposed filter is also analyzed under incident angle variation to find its potential use in various wearable applications.
This paper presents the development of advanced electromagnetic (EM) sensor utilizing novel metamaterial-inspired modified dual-SRR based resonators for non-invasive detection of glucose levels in biological samples. The sensor resonator employs a dual-SRR along with two SRRs to detect glucose concentration in physiological solution mimicking human blood. The best-suited optimized dimensions for the proposed sensor resonator structure is carried out using the Genetic-Algorithm within the framework of the CST Microwave-Studio. Leveraging frequency-shifting properties this sensor resonator prototyped on FR-4 substrate, detects a significant shift of 1 GHz in resonant frequency for 100 mg/dL and 200 mg/dL glucose-DI water physiological solution. This proposed sensor is compact, robust, inexpensive and sensitive makes it a good alternative in non-invasive blood glucose detection in biomedical industries.
The need to efficiently detect and measure radiation is critical in various fields. This paper addresses the challenge of developing a fast, portable, and user-friendly radiation monitoring system. The proposed system utilizes plastic scintillating fibers (PSF) for radiation detection. A silicon photo-multiplier (SiPM) detects the radioactive particles absorbed by the fibers and converts them into electrical signals. Due to their low amplitude, these pulses require amplification; thus, an amplifier is employed. The quantization of these pulses is achieved through a comparator circuit, which converts the continuous signal into a discrete digital signal. The system leverages a field-programmable gate array (FPGA) to count the quantized outputs, determine pulse width, and implement coincidence logic for accurate radiation detection. Finally, wireless universal asynchronous receiver/transmitter (UART) communication allows data transmission from the monitoring device to a mobile device accessible by authorized users. This system offers real-time radiation level monitoring with potential applications in nuclear safety, security, and emergency response scenarios.
Peripheral neuropathies represent a significant challenge in medicine, often inadequately addressed by conventional treatments. This work proposes a method for transferring information between implanted devices using galvanic currents. Diseases like nerve lesions causing facial palsy could be addressed by transmitting signals from a healthy to a damaged nerve via intra-body communication. A spike detection algorithm enables highly efficient data compression, with a compression factor up to 13000, extracting only the essential information required for nerve stimulation and minimizing transmission size. Experimental validation demonstrated effective communication up to 10 cm, with peak currents of 3 mA, compliant with ICNIRP safety guidelines. This innovative approach offers new prospects for restoring normal movements and improving the quality of life for affected patients.
This paper conducted a study on the classification of basic hand movement from the two-channel surface Electromyogram (sEMG) data acquired from the upper limb. For classification, the scattering coefficients are obtained from sEMG signals by applying wavelet scattering transform with different quality (Q) factors. The statistical features namely mean value, summation, root mean square value, variance, maximum value, kurtosis, and skewness were computed from the scattering coefficients. The features were ranked using the ReliefF algorithm and classified using different machine learning algorithms to determine the most accurate classifier model. A comparative study of the performance of different classifiers in terms of accuracy has been done in this study. The proposed method has been applied to the sEMG data obtained from five subjects. The simulation results show that the average classification accuracy achieved by the proposed method is 93.4
Next-generation wireless communication systems will face various demands, including exponentially increasing data rates, ensuring ultra low power consumption for battery-operated communication sensors, and providing extremely short response times critical for control applications. To meet these flexible requirements, a unified physical layer waveform has been proposed, known as Generalized Frequency Division Multiplexing (GFDM), designed for beyond 5G (B5G) communication systems. One of the key features of GFDM is the use of flexible prototype pulse-shaping filters, which leads to improved performance. In this paper, the performance of the GFDM system is analysed for the Discrete Gabor Transform (DGT) based filter over fluctuating two-ray (FTR) fading channel with zero forcing (ZF) receiver. The GFDM performance is analysed in terms of average symbol error rate (ASER) and Noise Enhancement Factor (NEF) for 16 QAM modulation scheme.
Smart textile materials have huge applications on wearable physiological monitoring devices. Monitoring of muscular stretch and bending is important for sports and geriatric people due to their continuous physical activities and due to age factors their muscles become weak which leads to muscular disorders. In this research, a carbon nanotube (CNT) based smart fabric is developed for monitoring the bending activities of the hand with respect to wrist moment in different angles of bending. The conductive fabric is developed from cotton fabric, coated with multi-walled carbon nanotube (MWCNT) synthesis using a hydrothermal process. The developed material acts as a sensor which is placed along with the glove near the wrist. The sensor resistance is tested in four different zones. The sensor converts the bending angle stimuli into voltage, such that the various zones within the range of motion of the wrist are comprehended by the algorithm. The range of resistance was monitored with the aid of a serial monitor. The results show resistance values exceeding 190 Ω for the Green Zone; a range of 182–190 Ω for the Yellow Zone; 174–182 Ω for the Red Zone; and resistance values dipping below 174 Ω for extension beyond 51o, where 1000 Ω was used as the known resistance of the circuit.
The neurological condition known as epilepsy, which is marked by recurring seizures, presents considerable obstacles to prompt diagnosis and treatment. We investigate the effectiveness of hierarchical clustering (HC) as a feature extraction technique for pre-processing electroencephalogram (EEG) signals using data from publicly accessible datasets. Preictal and ictal state-balanced samples are included in the dataset, enabling reliable model training and assessment. The performance of hierarchical clustering as a pre-processing technique for a biological signal like EEG is evaluated by training couple of models and obtaining the evaluation metrics from them and finally all the metrics are compared together.
The non-stationary signals are the signals with statistical properties that change with time. The spectral properties of the non-stationary signals can be analyzed by estimating the instantaneous frequency (IF). The separation of the individual mono-components from the multi-component signal is essential for the IF estimation. In this paper, we propose a methodology for the separation of the mono-components from a multi-component non-stationary signal based on a dynamic Q-value-based wavelet transform (DQVWT) method. The windowing of the time domain signal with a moving Gaussian function and the separation of components using an array of tunable Q wavelet transform (TQWT) blocks results in the mono-component separation followed by the IF computation. The proposed methodology is applied to signals that consist of linearly frequency modulated (LFM) mono-components and non-LFM (NLFM) mono-components. The IF estimation by the proposed method is compared with the existing TQWT-based filter bank (TQWT-FB) method and the Fourier Bessel/time order method. The proposed method has been applied to estimate the IF of the fundamental frequency component of the speech signal. The performance is analyzed in terms of mean square error (MSE) and the proposed method has shown better performance than the other compared methods.
Wearable technologies have revolutionized muscle activity monitoring, offering significant advances in fields such as prosthetics, rehabilitation, and human-computer interaction. While Electromyography (EMG) has been a staple in capturing muscle signals, it comes with limitations like precise electrode placement, noise susceptibility, and user discomfort. Force Myography (FMG) emerges as a promising alternative, using pressure variations on the skin to detect muscle movements. This study introduces a novel flexible tactile sensor array, integrated into an armband for FMG applications, aimed at improving muscle signal monitoring. The sensor array was fabricated using piezoresistive materials, specifically Velostat, which offers flexibility and durability. Eight customized sensors were arranged in an armband structure, capturing forearm muscle movements and translating them into electrical signals. The developed FMG armband was tested for hand gesture recognition in five healthy participants, achieving an average classification accuracy of 96.34
The implementation of an Intelligent Reflecting Surface (IRS) for Mobile User Equipment (UE) localization is investigated in this paper. We propose a method for localizing a user employing multiple IRS primarily based on Rician fading channels, which is more aligned with real-world conditions, while using the widely examined Rayleigh fading channels as a benchmark for comparison, suitable for both indoor and outdoor scenarios. This method is contrasted with the traditional Rayleigh fading models, which have been extensively studied and documented in the literature. By leveraging multiple IRS units and considering these channel models, our approach facilitates accurate user positioning in both indoor and outdoor settings. The reflection of signals from the IRS arrays is utilized to pinpoint the user’s location. Numerical results highlight the enhanced accuracy of our Rician-based localization method when juxtaposed with conventional techniques, maintaining robustness even amidst fading phenomena.
Electroencephalogram (EEG) contains important physiological information that can reflect the activity of human brain so that it is useful for epileptic seizure detection and epilepsy diagnosis. In this paper, we develop a novel unified framework for real-time monitoring of EEG for epileptic seizure prediction with minimum number of electrodes for wearable application without any prior knowledge. This research work uses Principal Component Analysis (PCA) for ranking the highest contribution of channels during seizure period to reduce the number of EEG scalp electrodes. CHB-MIT data has average 23 channels for each patient, but obtained results of our study show that average five to six channels are enough to get good sensitivity with less False Prediction Rate (FPR) per hour. In this research work, different channels combinations in the term of accuracy, sensitivity and FPR/hr have been analyzed and the result obtained 92.73
Global Navigation Satellite system (GNSS) facilitate Earth based mapping via orbiting Satellite network. GNSS receivers calculate distance to these Satellites allowing users to Precisely determine their location. GNSS encompass multiple constellations providing global coverage. In order for GNSS systems to operate nowadays, high-precision atomic clock data must be transmitted from satellites to receivers, which requires massive bandwidth. The notion that each satellite transmits its own time information for this data redundancy. Current GNSSs rely on ground networks to monitor and correct satellite clocks. This project work discusses about how optical clocks are becoming more accurate timepieces than atomic clocks for wireless transmitter applications. An introduction to optical clock technology, including its history and features, is given at the beginning of the text. The Allan Deviation (ADEV) method is then used to evaluate clock stability, and a stability analysis is performed by comparing optical clocks with the current Global Navigation Satellite System (GNSS) satellite clocks. The results indicate that on board GNSS satellites, optical clocks are more stable than atomic clocks. To achieve spacecraft payload requirements, additional technological developments might be required. Optical clocks, as opposed to the current atomic clocks on board GNSS satellites, may provide sub-millimeter range inaccuracy and far better timing performance in the GNSS location. The Study Specifically Pinpoints the atomic of the Galileo satellites.
Molecular communication (MC) leverages the transmission of information through patterns of molecules, mimicking biological systems to address various technological challenges. This study focuses on particle-based simulations (PBS) of molecular communication, particularly comparing the performance of fully absorbing (FA) and partially absorbing (PA) receivers. In real-world scenarios, receivers are PA because environmental factors often prevent complete absorption, leading to partial interactions with molecules or signals. Motivated by this here the author compare the performance of FA and PA receivers. Our simulations were conducted with a fixed transmitter-receiver topology, involving two receivers placed at different locations. The cumulative number of molecules absorbed over 30 s was calculated, accounting for the random movement of molecules via Brownian motion. The results show that the PA receiver outperforms the FA one, especially in mitigating intersymbol interference (ISI). Molecules absorbed at the back side of the receiver, which likely belonged to previously transmitted signals, contribute significantly to ISI. This finding is critical in understanding the dynamics of molecular diffusion and optimizing receiver design for practical MC systems. This paper validates the findings of an asymptotic model through PBS and offers insights into improving molecular communication systems for real-world applications.
This paper presents a theoretical framework for designing and optimizing graphene-based RIS for THz wave manipulation. The RIS configuration consists of an array of circular graphene meta-atoms on a silicon substrate grounded with a metal. Numerical analysis shows that graphene-based RIS achieves nearly perfect reflection, with close to 100
Wireless power transfer (WPT) technique presents a viable approach to extending the battery life of implantable medical electronics (IMEs), such as cardiac defibrillators, cochlear implants, pacemakers, etc. Battery depletion necessitates frequent IME battery replacements, placing a burden on the patient. To this end, WPT techniques can assist in improving the durability of IMEs. Utilizing a photovoltaic (PV) energy harvester in conjunction with an external light source offers advantages for WPT systems, providing sufficient power for IMEs. This paper presents a wireless electrical power system for IMEs. In this study, energy is delivered via a tissue-mimicking optical phantom. Commercially available monocrystalline silicon (Si) PV cells and a high-power 850 nm near-infrared (NIR) LED light were used as energy harvesters and to deliver optical power across the phantoms, respectively. This study considers the following parameters: the received optical power in the different thicknesses of phantoms (i.e., 10 mm, 40 mm, and 50 mm) under varying transmitted power conditions, the voltage output at open-circuit ( V_OC ) and the short-circuit current ( I_SC ) of the PV cells. The results show that PV cells can generate voltage in a reasonable harvesting time even when exposed to NIR light that has penetrated phantoms up to 50 mm thick. Moreover, the findings offer useful guidelines for developing future optical WPT (OWPT) systems and open a world of possibilities for future research. The potential for commercial monocrystalline Si PV cells to serve as energy harvesters to power various IMEs from external light sources, in this case, high-power 850 nm NIR light, is a fascinating area for further exploration.
The Respeck is a wireless sensor device is worn as a plaster on the chest to measure continuously features of pulmonary function such as the respiratory rate and respiratory flow/effort. Given the constraints of the location of the Respeck on the body, this paper describes deep learning methods - firstly, to classify five different types of walking: shuffle walking, normal walking, running, ascending/descending stairs, and, secondly, to count the number of steps (step-count) based on methods using representation of the sensor data in the time and frequency domains to count the number of steps. Results are presented for the accuracy of greater than 90
Wireless Body Area Network (WBAN) is an innovative technological approach for universal health care and an essential part of the Internet of Things. WBAN leverages advanced sensing technology for comprehensive data processing and aggregation, enabling intelligent health monitoring. These sensors gather information from an individual’s body and wirelessly send it to a remote coordinator device or server, where this data is examined and analyzed. In WBAN, nodes are energy constrained, and the network topology frequently changes due to human movement or posture changes. Therefore, the connection between the WBAN and the distant server constantly changes. This instability leads to frequent packet loss, resulting in inaccurate data collection. Therefore, efficient use of energy to ensure reliable data transmission becomes critical. As a result, one of the challenges of the current study is obtaining a suitable sink position and providing harmonious connectivity that improves sensor power consumption, security, and privacy. This paper introduces a novel strategic sink positioning method to improve the transfer of essential body information. By evaluating different sink positions (head, left hand, right leg, and waist), the study optimises network lifespan, stability period, throughput, and residual energy, significantly advancing WBAN performance.
EEG channel selection is a technique that tries to fetch maximum information from the brain with less number of channels for a specific cognitive task. Though higher number of channels provide more information, the complexity in placing the electrodes and duration of recording is high. In this context proposed here is selection of suitable channel for different cognitive tasks. Brain signals are recorded using 16 channel EEG from group of volunteers by using Control Oral Word Association test, Motor imagery, Stroop test, Trial making test and Rest. SNR based algorithm is used to find 8 optimum channels. Phase locking value (PLV) computed and grouped based on the results of channel selection algorithms. Mahalanobis distance (MD) is used to find the closeness between the groups. MD values between the groups of optimal channels is lesser when compared to other channels. This means the selected channels are functionally related that indicates the active brain region for specific cognitive task is only selected as optimal channels. The extension of this study could be helpful in development of handheld or portable device.
