Volume 74 (2023): Edizione 1 (February 2023)

A new cost-effective mobile fronthaul architecture has been proposed by designing promising analog radio over fiber (ARoF) transmission system compliant with the next generation ultra-dense wavelength division multiplexing passive optical network (UDWDM-PON). It is conceived for standalone 5G/5G+ mobile communication systems driven by broadband multi-input multi-output (MIMO) millimeter wave data signals using two high spectral efficient modulations. The feasibility of the proposed system was firstly proven in terms of spectral tracing supported by theoretical analysis that disclosed good agreement. Furthermore, the obtained simulation results regarding the system performance evaluation after 22 km bidirectional optical fiber links were revealed high reliable characteristics including error-free transmissions at low receiver sensitivities, and measured power penalties less than 1 dB. In addition, the recovered eye diagrams maintained wide openings with high-quality patterns.

This paper presents a comparative EMI susceptibility study of different integrated operational transconductance amplifier (OTA) topologies. We analyzed conventional well-known amplifier topologies based on the Miller OTA and folded cascode concepts with lower power consumption. The output dc voltage shifts induced by power supply and input common mode high frequency disturbances are presented. On top of the EMI susceptibility comparison, we discuss PSRR and CMRR within large and small excitation signal with a new simulation setup. Even more, the back-gate connections of differential MOS pair in OTA input stage are investigated for EMI susceptibility impact as well.

Biometric systems aim to provide reliable authentication and verification of users. The behaviour of the users may alter the authentication performance when accessing these systems. Therefore, clustering users based on their actions is crucial. A biometric menagerie defines and labels user groups statistically according to their variability. However, determining groups is a fuzzy process and it may lead to inconsistencies. In this work, a novel and flexible approach is introduced based on the classification performance of the users data collected in a database without imposing any other restrictions. According to the performance measures obtained from the confusion matrix of the classification algorithms, users are ranked and then clustered. Additionally, the norm of a confusion matrix is offered augmenting the state-of-the-art performance metrics. The proposed scheme is evaluated using the behavioural biometrics modality on two benchmark keystroke databases. The performance results successfully illustrate the alternative way of grouping and identification of users sharing the same behaviour irrespective of the chosen classifiers or performance metrics.

Magnetic storms are severe disorders in the Earth’s magnetosphere caused by enormous solar activity. The magnetic storm induces a quasi-stationary geomagnetic current (GIC) in the wires of the very high voltage (VHV) lines. These currents cause the current and thus thermal overload of the VHV part of the transmission lines, especially the transformers of the system. In the submitted article described by a device that indicates these effects of magnetic storms on the transmission system and allows it to carry out its safe operation. The indicator is located on the VHV wire of the bundle conductor. Using the Hall probe, it senses the magnetic field of the current in the line. With a digital frequency low-pas filter removes the ac component of the indicated current and information about its dc component, ie about the GIC, wireless transmission to the workplace of the system operator. This will then instruct the transmission system protective regime.

Tooth diseases including dental caries, periodontitis and cracks have been public health problems globally. How to detect them at the early stage and perform thorough diagnosis are critical for the treatment. The diseases can be viewed as defects from the perspective of non-destructive testing. Such a defect can affect the material properties (e.g., optical, chemical, mechanical, acoustic, density and dielectric properties). A non-destructive testing method is commonly developed to sense the change of one particular property. Microwave testing is one that is focused on the dielectric properties. In recent years, this technique has received increased attention in dentistry. Here, the dielectric properties of human teeth are presented first, and the measurement methods are addressed. Then, the research progress on the detection of teeth over the last decade is reviewed, identifying achievements and challenges. Finally, the research trends are outlined, including electromagnetic simulation, radio frequency identification and heating-based techniques.

With the expansion of the communicative and perceptual capabilities of mobile devices in recent years, the number of complex and high computational applications has also increased rendering traditional methods of traffic management and resource allocation quite insufficient. Recently, mobile edge computing (MEC) has emerged as a new viable solution to these problems. It can provide additional computing features at the edge of the network and allow alleviation of the resource limit of mobile devices while increasing the performance for critical applications especially in terms of latency. In this work, we addressed the issue of reducing the service delay by choosing the optimal path in the MEC network, which consists of multiple MEC servers that has different capabilities, applying network load balancing where multiple requests need to be handled simultaneously and routing selection based on a deep- Q network (DQN) algorithm. A novel traffic control and resource allocation method is proposed based on deep Q-learning (DQL) which allows reducing the end-to-end delay in cellular networks and in the mobile edge network. Real life traffic scenarios with various types of user requests are considered and a novel DQL resource allocation scheme which adaptively assigns computing and network resources is proposed. The algorithm optimizes traffic distribution between servers reducing the total service time and balancing the use of available resources under varying environmental conditions.

This paper presents modeling of high current capability of mixed carbon nanotube (CNT) bundle interconnects depending upon the type of constituent CNT materials and their orientations. With different arrangements, one category of novel mixed CNT bundles formed by the combination of multi-walled/multi-shell CNT and double-shell CNT bundles (MDCB) are proposed and compared with the mixed CNT bundles (MSCB) formed with multi-shell CNT and single-walled CNT bundles. A time-domain analysis is performed for these structures to analyse the effect of delay and power dissipation. It has also been observed that MDCB structures give better performance (≈ 30%) than MSCB structures in terms of power-delay product at the global length of interconnect for nano-regime technology nodes. Also, MDCB structure formed by placing multi-walled CNTs along the periphery and double-walled CNTs in the centre of structure yields the best result against all proposed mixed CNT bundled structures and can be employed for future interconnect applications.

Phlebotomy may cause unnecessary injuries to a patient whose veins are not easily visible to a healthcare professional. To mitigate this problem we designed a new system to image subcutaneous veins. Multispectral images were obtained using a microprocessor, an IR (infrared) camera, different wavelengths of NIR (near-infrared) sources, and an IR band-pass filter. Raw vein images were enhanced, colored, and displayed on a monitor using an easy-to-use interface. The mean dice similarity index (DSI) between the vein border specified by a doctor on the raw images manually and the automated segmented by the proposed system is determined as 0.92 ± 2.1 for 20 subjects. Also, the average peak signal-to-noise ratio (PSNR) obtained a high value of 68.37 ± 1.56 from the enhanced image. Phlebotomists can easily observe the subcutaneous veins in real-time with the three different options using the proposed device. As a result, this study advances the vein imaging field which has the potential to reduce injury to the patient during venipuncture.

One of the most important tasks to be considered in wireless communication systems, especially in multi-carrier systems such as Multi-Input Multi Output Non-Orthogonal (MIMO-NOMA), is to correctly estimate the channel state information for coherent detection at the receiver. A hybrid deep learning model, called convolutional fuzzy deep neural networks, is proposed in this study for accurately estimating channel state information and detecting symbols in MIMO-NOMA systems. The performance of this proposal has been compared to traditional algorithms like Least Square Error- Successive Interference Cancelation (LS-SIC) and linear minimum mean square (LMMSE-SIC), as well as to other deep learning methods such as convolutional neural networks. With this proposed scheme, significantly less bit error rate is obtained in both Rician and Rayleigh channel environment compared to other algorithms. In addition to the high performance of this scheme, the fact that it does not need channel statistics is another important advantage.