Signals, Volume 3, Issue 1 (March 2022) – 10 articles

Several parameter tables for PID controllers are known from the literature, for the control of time-delayed systems. The best known is that of Ziegler and Nichols, but there are others also. In this publication, a parameter table is presented that minimizes the quality criteria IAE, ITAE and ISE for the control of time-delayed systems that can be identified with PTn systems. The controller output limitation is also taken into account. Since it is very computationally intensive to calculate these PID parameter sets, a well-known approach from the field of artificial intelligence was chosen. This was hill-climbing, which can find the parameter sets with much less computational effort than is needed for nested loops. With this method, the PID parameter tables for the minimized quality criteria were found and compared, each on the basis of many randomized starting parameter values. The application of the hill-climbing method shows rapid convergence. The parameters are discussed with two examples of time-delayed systems: a PT3 and a PT5. The results are regulated systems which show very good transient behavior. Furthermore, it is also possible to use the procedure presented for optimal PID parameters to control general, stable systems. Full article

Wearable technologies are making a significant impact on people’s way of living thanks to the advancements in mobile communication, internet of things (IoT), big data and artificial intelligence. Conventional wearable technologies present many challenges for the continuous monitoring of human health conditions due to their lack of flexibility and bulkiness in size. Recent development in e-textiles and the smart integration of miniature electronic devices into textiles have led to the emergence of smart clothing systems for remote health monitoring. A novel comprehensive framework of smart clothing systems for health monitoring is proposed in this paper. This framework provides design specifications, suitable sensors and textile materials for smart clothing (e.g., leggings) development. In addition, the proposed framework identifies techniques for empowering the seamless integration of sensors into textiles and suggests a development strategy for health diagnosis and prognosis through data collection, data processing and decision making. The conceptual technical specification of smart clothing is also formulated and presented. The detailed development of this framework is presented in this paper with selected examples. The key challenges in popularizing smart clothing and opportunities of future development in diverse application areas such as healthcare, sports and athletics and fashion are discussed. Full article

Being the main pillar in the context of Industry 4.0, the Internet of Things (IoT) leads evolution towards a smarter and safer planet. Being human-centered, rather than machine-centered, as was the case of wireless sensor networks used in the industry for decades, the IoT may enhance human intelligence with situational awareness, early warning, and decision support tools. Focusing on air cargo transportation, the “INTELLICONT” project presented a novel solution capable of improving critical air cargo challenges such as the reduction of total aircraft weight, detection and suppression of smoke and/or fire in a container, elimination of permanent moving and locking hardware, loading and unloading logistics enhancement and maintenance. In the present work, the IoT-based monitoring and control system for intelligent aircraft cargo containers is presented from a hardware perspective. The system is based on low-cost, low-energy sensors that are integrated into the container, can track its status, and detect critical events, such as fire/smoke, impact, and accidental misuse. The focus has been given to the design and development of a system capable of providing better and safer control of the aircraft cargo during the loading/unloading operations and the flight. It is shown that the system could provide a breakthrough in the state of the art of current cargo container technology and aircraft cargo operations. Full article

The use of lightweight cryptographic algorithms is essential for addressing security in highly constrained environments such as the Internet of Things. In this paper, the performance of lightweight block ciphers in such highly constrained environments is studied. More precisely, focusing—as a case study—on an important family of lightweight ciphers called “Saturnin”, which has been evaluated as a candidate for standardization in the relative ongoing NIST’s competition, we analyze its efficiency in case that it is implemented in a specific resource-constrained environment. To evaluate the results, a comparative study with the Advanced Encryption Standard (AES) is performed, through an appropriate experimental environment. Our results illustrate that significant gain in performance can be achieved, since Saturnin—whose design is inspired by the design of AES—can be almost two times faster than AES in such restricted environments. Full article

Most environments change over time. Being able to adapt to such non-stationary environments is vital for real-world applications of many machine learning algorithms. In this work, we propose CORAL, a computationally efficient regression algorithm capable of adapting to a non-stationary target. CORAL is based on Bayesian linear regression with a sliding window and offline/online meta-learning. The sliding window makes our model focus on the recently received data and ignores older observations. The meta-learning approach allows us to learn the prior distribution of the model parameters. It speeds up the model adaptation, complements the sliding window’s drawback, and enhances the performance. We evaluate CORAL on two tasks: a toy problem and a more complex blood glucose level prediction task. Our approach improves the prediction accuracy for the non-stationary target significantly while also performing well for the stationary target. We show that the two components of our method work in a complementary fashion to achieve this. Full article

The development of mobile health for epilepsy has grown in the last years, bringing new applications (apps) to the market and improving already existing ones. In this systematic review, we analyse the scope of mobile apps for seizure detection and epilepsy self-management, with two research questions in mind: what are the characteristics of current solutions and do they meet users’ requirements? What should be considered when designing mobile health for epilepsy? We used PRISMA methodology to search within App Store and Google Play Store from February to April of 2021, reaching 55 potential apps. A more thorough analysis regarding particular features was performed on 26 of those apps. The content of these apps was evaluated in five categories, regarding if there was personalisable content; features related to medication management; what aspects of seizure log were present; what type of communication prevailed; and if there was any content related to seizure alarm or seizure action plans. Moreover, the 26 apps were evaluated through using MARS by six raters, including two neurologists. The analysis of MARS categories was performed for the top and bottom apps, to understand the core differences. Overall, the lowest MARS scores were related to engagement and information, which play a big part in long-term use, and previous studies raised the concern of assuring continuous use, especially in younger audiences. With that in mind, we identified conceptual improvement points, which were divided in three main topics: customisation, simplicity and healthcare connection. Moreover, we summarised some ideas to improve m-health apps catered around long-term adherence. We hope this work contributes to a better understanding of the current scope in mobile epilepsy management, endorsing healthcare professionals and developers to provide off-the-shelf solutions that engage patients and allows them to better manage their condition. Full article

The current and future wireless communication systems, WiFi, fourth generation (4G), fifth generation (5G), Beyond5G, and sixth generation (6G), are mixtures of many frequency spectrums. Thus, multi-functional common or shared aperture antenna modules, which operate at multiband frequency spectrums, are very desirable. This paper presents a multiple-input and multiple-output (MIMO) antenna design for the 5G/B5G Internet of Things (IoT). The proposed MIMO antenna is designed to operate at multiple bands, i.e., at 3.5 GHz, 3.6 GHz, and 3.7 GHz microwave Sub-6 GHz and 28 GHz mm-wave bands, by employing a single radiating aperture, which is based on a tapered slot antenna. As a proof of concept, multiple tapered slots are placed on the corner of the proposed prototype. With this configuration, multiple directive beams pointing in different directions have been achieved at both bands, which in turn provide uncorrelated channels in MIMO communication. A 3.5 dBi realized gain at 3.6 GHz and an 8 dBi realized gain at 28 GHz are achieved, showing that the proposed design is a suitable candidate for multiple wireless communication standards at Sub-6 GHz and mm-wave bands. The final MIMO structure is printed using PCB technology with an overall size of 120 × 60 × 10 mm³, which matches the dimensions of a modern mobile phone. Full article

In late 2019, a new genre of coronavirus (COVID-19) was first identified in humans in Wuhan, China. In addition to this, COVID-19 spreads through droplets, so quarantine is necessary to halt the spread and to recover physically. This modern urgency creates a critical challenge for the latest technologies to detect and monitor potential patients of this new disease. In this vein, the Internet of Things (IoT) contributes to solving such problems. This paper proposed a wearable device that utilizes real-time monitoring to detect body temperature and ambient conditions. Moreover, the system automatically alerts the concerned person using this device. The alert is transmitted when the body exceeds the allowed temperature threshold. To achieve this, we developed an algorithm that detects physical exercise named “Continuous Displacement Algorithm” based on an accelerometer to see whether a potential temperature rise can be attributed to physical activity. The people responsible for the person in quarantine can then connect via nRF Connect or a similar central application to acquire an accurate picture of the person’s condition. This experiment included an Arduino Nano BLE 33 Sense which contains several other sensors like a 9-axis IMU, several types of temperature, and ambient and other sensors equipped. This device successfully managed to measure wrist temperature at all states, ranging from 32 °C initially to 39 °C, providing better battery autonomy than other similar devices, lasting over 12 h, with fast charging capabilities (500 mA), and utilizing the BLE 5.0 protocol for data wireless data transmission and low power consumption. Furthermore, a 1D Convolutional Neural Network (CNN) was employed to classify whether the user is feverish while considering the physical activity status. The results obtained from the 1D CNN illustrated the manner in which it can be leveraged to acquire insight regarding the health of the users in the setting of the COVID-19 pandemic. Full article

Long-range (LoRa) communication has attracted much attention recently due to its utility for many Internet of Things applications. However, one of the key problems of LoRa technology is that it is vulnerable to noise/interference due to the use of only up-chirp signals during modulation. In this paper, to solve this problem, unlike the conventional LoRa modulation scheme, we propose a modulation scheme for LoRa communication based on joint up- and down-chirps. A fast Fourier transform (FFT)-based demodulation scheme is devised to detect modulated symbols. To further improve the demodulation performance, a hybrid demodulation scheme, comprised of FFT- and correlation-based demodulation, is also proposed. The performance of the proposed scheme is evaluated through extensive simulation results. Compared to the conventional LoRa modulation scheme, we show that the proposed scheme exhibits over 3 dB performance gain at a bit error rate of ${10}^{-4}$. Full article