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5G (fifth-generation technology) technologies are becoming more mainstream thanks to great efforts from telecommunication companies, research facilities, and governments. This technology is often associated with the Internet of Things to improve the quality of life for citizens by automating and gathering data recollection processes. This paper presents the 5G and IoT technologies, explaining common architectures, typical IoT implementations, and recurring problems. This work also presents a detailed and explained overview of interference in general wireless applications, interference unique to 5G and IoT, and possible optimization techniques to overcome these challenges. This manuscript highlights the importance of addressing interference and optimizing network performance in 5G networks to ensure reliable and efficient connectivity for IoT devices, which is essential for adequately functioning business processes. This insight can be helpful for businesses that rely on these technologies to improve their productivity, reduce downtime, and enhance customer satisfaction. We also highlight the potential of the convergence of networks and services in increasing the availability and speed of access to the internet, enabling a range of new and innovative applications and services.

[phrase omitted] [phrase omitted] Metodos Se recopilaron los datos que suministraron los usuarios de la aplicacion movil <> durante el periodo comprendido entre el 1.[grados] de octubre de 2018 y el 30 de junio de 2020. Se analizaron a traves de RStudio y se genero un grafico de uso de la aplicacion con un promedio variable de 7 dias. Se calcularon las reducciones a nivel de pais en el uso de la aplicacion como un porcentaje de valor basal. Se obtuvieron datos sobre el recuento de los casos de la COVID-19 del Centro Europeo para la Prevencion y el Control de las Enfermedades. Se trazaron graficos sobre el cambio en el uso de las aplicaciones y el recuento de casos de la COVID-19 en varios paises y regiones. Resultados Un total de 100 099 usuarios de la aplicacion en 214 paises y territorios suministraron datos. Se observo que el uso de la aplicacion se redujo durante los dias festivos, los fines de semana y por las noches, en correlacion con las fluctuaciones previstas de la cantidad de intervenciones quirurgicas. Se observo que el inicio de la pandemia genero reducciones sustanciales en el uso de la aplicacion. Se registro una fuerte correlacion cruzada entre el recuento de los casos de la COVID-19 y las reducciones en el uso de la aplicacion en los paises de ingresos bajos y medios, pero no en los paises de ingresos altos. De los 112 paises y territorios que no usaron la aplicacion durante el momento basal y durante la pandemia, se calculo una reduccion mediana en el uso de la aplicacion hasta el 73,6 % del valor basal.

Abstract Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using this data, we compared the contributions of multiple features (motion, local standard deviation in heart rate, and “clock proxy”) to performance across several classifiers. Best performance was achieved using neural nets, though the differences across classifiers were generally small. For sleep-wake classification, our method scored 90% of epochs correctly, with 59.6% of true wake epochs (specificity) and 93% of true sleep epochs (sensitivity) scored correctly. Accuracy for differentiating wake, NREM sleep, and REM sleep was approximately 72% when all features were used. We generalized our results by testing the models trained on Apple Watch data using data from the Multi-ethnic Study of Atherosclerosis (MESA), and found that we were able to predict sleep with performance comparable to testing on our own dataset. This study demonstrates, for the first time, the ability to analyze raw acceleration and heart rate data from a ubiquitous wearable device with accepted, disclosed mathematical methods to improve accuracy of sleep and sleep stage prediction.

Quantitative diagnosis of glucose-6-phosphate dehydrogenase (G6PD) deficiency is essential for the safe administration of 8-aminoquinoline based radical cure for the treatment of Plasmodium vivax infections. Here, we present the PreQuine Platform (IVDS, USA), a quantitative biosensor that uses a dual-analyte assay for the simultaneous measurement of Hemoglobin (Hgb) levels and G6PD enzyme activity within the same sample. The platform relies on a downloadable mobile application. The device requires 10[mu]l of whole blood and works with a reflectance-based meter. Comparing the G6PD measurement normalized by Hgb of 12 samples from the PreQuine Platform with reference measurements methods (spectrophotometry, Pointe Scientific, USA and hemoglobin meter, HemoCue, Sweden) showed a positive and significant agreement with a slope of 1.0091 and an intercept of -0.0379 under laboratory conditions. Next steps will be to conduct field trials in Bangladesh, Cambodia, and the USA to assess diagnostic performance, user friendliness and acceptance.

Antennas in wireless sensor networks (WSNs) are characterized by the enhanced capacity of the network, longer range of transmission, better spatial reuse, and lower interference. In this paper, we propose a planar patch antenna for mobile communication applications operating at 1.8, 3.5, and 5.4 GHz. A planar microstrip patch antenna (MPA) consists of two F-shaped resonators that enable operations at 1.8 and 3.5 GHz while operation at 5.4 GHz is achieved when the patch is truncated from the middle. The proposed planar patch is printed on a low-cost FR-4 substrate that is 1.6 mm in thickness. The equivalent circuit model is also designed to validate the reflection coefficient of the proposed antenna with the S11 obtained from the circuit model. It contains three RLC (resistor–inductor–capacitor) circuits for generating three frequency bands for the proposed antenna. Thereby, we obtained a good agreement between simulation and measurement results. The proposed antenna has an elliptically shaped radiation pattern at 1.8 and 3.5 GHz, while the broadside directional pattern is obtained at the 5.4 GHz frequency band. At 1.8, 3.5, and 5.4 GHz, the simulated peak realized gains of 2.34, 5.2, and 1.42 dB are obtained and compared to the experimental peak realized gains of 2.22, 5.18, and 1.38 dB at same frequencies. The results indicate that the proposed planar patch antenna can be utilized for mobile applications such as digital communication systems (DCS), worldwide interoperability for microwave access (WiMAX), and wireless local area networks (WLAN).

Terahertz (THz) is a promising technology for future wireless communication networks, particularly for 6G and beyond. The ultra-wide THz band, ranging from 0.1 to 10 THz, can potentially address the limited capacity and scarcity of spectrum in current wireless systems such as 4G-LTE and 5G. Furthermore, it is expected to support advanced wireless applications requiring high data transmission and quality services, i.e., terabit-per-second backhaul systems, ultra-high-definition streaming, virtual/augmented reality, and high-bandwidth wireless communications. In recent years, artificial intelligence (AI) has been used mainly for resource management, spectrum allocation, modulation and bandwidth classification, interference mitigation, beamforming, and medium access control layer protocols to improve THz performance. This survey paper examines the use of AI in state-of-the-art THz communications, discussing the challenges, potentials, and shortcomings. Additionally, this survey discusses the available platforms, including commercial, testbeds, and publicly available simulators for THz communications. Finally, this survey provides future strategies for improving the existing THz simulators and using AI methods, including deep learning, federated learning, and reinforcement learning, to improve THz communications.

Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction models have been developed for mobile applications, a systematic overview of these studies is still missing. Therefore, we carried out a Systematic Literature Review (SLR) study to evaluate how machine learning has been applied to predict faults in mobile applications. This study defined nine research questions, and 47 relevant studies were selected from scientific databases to respond to these research questions. Results show that most studies focused on Android applications (i.e., 48%), supervised machine learning has been applied in most studies (i.e., 92%), and object-oriented metrics were mainly preferred. The top five most preferred machine learning algorithms are Naïve Bayes, Support Vector Machines, Logistic Regression, Artificial Neural Networks, and Decision Trees. Researchers mostly preferred Object-Oriented metrics. Only a few studies applied deep learning algorithms including Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN). This is the first study that systematically reviews software defect prediction research focused on mobile applications. It will pave the way for further research in mobile software fault prediction and help both researchers and practitioners in this field.

In this paper, a novel microstrip line-fed meander-line-based four-elements quad band Multiple Input and Multiple Output (MIMO) antenna is proposed with a gain enhancement technique. The proposed structure resonates at four bands simultaneously, that is, 1.23, 2.45, 3.5 and 4.9 GHz, which resemble GPS L2, Wi-Fi, Wi-MAX and WLAN wireless application bands, respectively. The unit element is extended to four elements MIMO antenna structure exhibiting isolation of more than 22 dB between the adjacent elements without disturbing the resonant frequencies. In order to enhance the gain, two orthogonal microstrip lines are incorporated between the antenna elements which result in significant gain improvement over all the four resonances. Furthermore, the diversity performance of the MIMO structure is analyzed. The Envelope Co-Relation Coefficient (ECC), Diversity Gain (DG), Channel Capacity Loss (CCL), Mean Effective Gain (MEG) and Multiplexing Efficiency are obtained as 0.003, 10 dB, 0.0025 bps/Hz, −3 dB (almost) and 0.64 (min.), respectively, which are competent and compatible with practical wireless applications. The Total Active Reflection Coefficient (TARC) resembles the characteristic of the individual antenna elements. The layout area of the overall MIMO antenna is 0.33 λ × 0.29 λ, where λ is the free-space wavelength corresponding to the lowest resonance. The advantage of the proposed structure has been assessed by comparing it with previously reported MIMO structures based on number of antenna elements, isolation, gain, CCL and compactness. A prototype of the proposed MIMO structure is fabricated, and the measured results are found to be aligned with the simulated results.

In this article, a compact 4-port UWB (Ultra-Wide Band) MIMO (Multiple Input Multiple Output) antenna is proposed. A low profile FR-4 substrate is used as a dielectric material with the dimensions of 58 × 58 mm2 (0.52λ × 0.52λ) at 2.8 GHz and a standard thickness of 1.6 mm. The proposed design characterizes an impedance bandwidth starting from 2.8 to 12.1 GHz (124.1%). Each of the four elements of the proposed MIMO antenna configuration consists of a monopole antenna with PG (partial ground) that has a slot at its center. The corner of each patch (radiator) and ground slot are rounded for impedance matching. Each unit cell is in an orthogonal orientation, forming a quad-port MIMO antenna system. For reference, the partial ground of each unit cell is connected meticulously with the others. The simulated results of the proposed quad-port MIMO antenna design were configured and validated by fabrication and testing. The proposed Quad-port MIMO design has a 6.57 dBi peak gain and 97% radiation efficiency. The proposed design has good isolation below 15 dB in the lower frequency range and below 20 dB in the higher frequency range. The design has a measured ECC (Envelop Correlation Co-efficient) of 0.03 and DG (Diversity Gain) of 10 dB. The value of TARC (Total Active Reflection Coefficient) over the entire operating band is less than 10 dB. Moreover, the design maintained CCL (Channel Capacity Loss) < 0.4 bits/sec/Hz and MEG (Mean Effective Gain) < 3 dB. Based on the obtained results, the proposed design is suitable for the intended high data rate UWB wireless communication portable devices.