TSV-Based Shoelace Inductor: A High-Density Solution for Advanced Packaging Platforms

Machine learning provides automated means to capture complex dynamics of wireless spectrum and support better understanding of spectrum resources and their efficient utilization. As communication systems become smarter with cognitive radio capabilities empowered by machine learning to perform critical tasks such as spectrum awareness and spectrum sharing, they also become susceptible to new vulnerabilities due to the attacks that target the machine learning applications. This chapter identifies the emerging attack surface of adversarial machine learning and corresponding attacks launched against wireless communications in the context of 5G systems. The focus is on attacks against (i) spectrum sharing of 5G communications with incumbent users such as in the Citizens Broadband Radio Service (CBRS) band and (ii) physical layer authentication of 5G User Equipment (UE) to support network slicing. For the first attack, the adversary transmits during data transmission or spectrum sensing periods to manipulate the signal-level inputs to the deep learning classifier that is deployed at the Environmental Sensing Capability (ESC) to support the 5G system. For the second attack, the adversary spoofs wireless signals with the generative adversarial network (GAN) to infiltrate the physical layer authentication mechanism based on a deep learning classifier that is deployed at the 5G base station. Results indicate major vulnerabilities of 5G systems to adversarial machine learning. To sustain the 5G system operations in the presence of adversaries, a defense mechanism is presented to increase the uncertainty of the adversary in training the surrogate model used for launching its subsequent attacks.

The rapid-developing Artificial Intelligence (AI) technology, fast-growing network traffic, and emerging intelligent applications (e.g., autonomous driving, virtual reality, etc.) urgently require a new, faster, more reliable and flexible network form. At this time, researchers in both industry and academia have turned their attention to the sixth generation (6G) communication networks. In the 6G vision, various intelligent application scenarios that utilize Machine Learning (ML) technology (the most important branch of AI) will bring rich heterogeneous connections, as well as massive information storage and operations. When ML meets 6G, new opportunities will emerge along with numerous privacy challenges. On one hand, a secure ML structure, or the correct application of ML, can protect privacy in 6G. On the other hand, ML may be attacked or abused, resulting in privacy violation. It is worth noting that the alliance between 6G and ML may also be a double-edged sword in many cases, rather than absolutely infringe or protect privacy. Therefore, based on lots of existing meaningful works, this paper aims to provide a comprehensive survey of ML and privacy in 6G, with a view to further promoting the development of 6G and privacy protection technologies.

<p>This survey paper provides an in-depth analysis of localization systems for autonomous vehicles (AVs), a cornerstone technology crucial for the safe and efficient operation of AVs. The paper encompasses a comprehensive examination of various localization technologies, their comparative analysis, the challenges they face, and the emerging trends shaping their future. We begin by exploring the principal technologies employed in AV localization: GPS, LiDAR, radar, cameras, and ultrasonic sensors. Each technology is scrutinized to understand its strengths, weaknesses, and applicability in different environmental contexts. GPS offers broad geographical positioning but struggles with precision in dense urban areas. LiDAR provides high-resolution mapping but is hindered by adverse weather conditions. Radar ensures reliability in poor visibility but lacks the finer details provided by optical systems. Cameras offer rich visual data but are dependent on lighting conditions, and ultrasonic sensors, while effective for close-range detection, are limited to low-speed applications. The paper then presents a comparative analysis of these technologies against key performance metrics such as accuracy, reliability, latency, scalability, and cost-effectiveness. This analysis is critical in understanding the suitability of each technology for various driving scenarios, from congested urban streets to open highways and in diverse weather conditions. Challenges in AV localization are multifaceted, encompassing technological, environmental, and system integration issues. Technological challenges include sensor limitations and computational constraints, while environmental factors like weather and urban infrastructure significantly impact localization accuracy. System integration poses another significant challenge, necessitating seamless interaction between localization systems and other vehicle control systems for real-time decision-making. Emerging trends and potential solutions to these challenges are discussed, highlighting advancements in sensor technology, AI, machine learning, and 5G communications. These developments promise to overcome existing limitations and enhance the accuracy and reliability of AV localization. The integration of localization systems with smart city infrastructures and the implementation of collaborative technologies like V2X communication are speculated to further augment AV capabilities. In conclusion, the paper emphasizes that the future of AV localization is intrinsically linked to the continuous evolution of technologies and their integration. Addressing current challenges and harnessing emerging trends are pivotal for the advancement of AVs, steering us toward a future of autonomous transportation characterized by increased safety, efficiency, and reliability.<strong></strong></p>

Beam management (BM) is a key component in fifth generation (5G), 5G-Advanced, and sixth generation (6G) millimeter-wave (mmWave) operations. Due to mobility or rotation of user equipment (UE), frequent beam measurements and search are needed at the UE based on also frequently transmitted reference signals (RSs). To enable mmWave BM with lower power consumed at the UE and less RS overhead, this paper proposes a machine learning (ML) assisted beam change prediction scheme based on long short-term memory (LSTM) facilitated models. Particularly, the ML-models are equipped at base station (BS) and use time series of UE reported beam qualities as inputs, which are based on the latest 3rd Generation Partnership Project (3GPP)5G new radio (NR) standards. The model outputs prediction on whether the strongest beam will be different from the currently reported one until the next applicable measurement occasion. This allows the system to operate with an extended beam measurement cycle greater than commercially popular ones with very limited beam mismatch errors. Our proposed ML-models are trained and tested via data collected using a cellphone equipped with Snapdragon 5G-mmWave Modem chipset and a prototype BS, both compliant with 3GPP Release-15 5G standards. Evaluation results show that the proposed scheme can accurately predict beam change instances and reduce over half of the power consumed at the UE for BM, which also demonstrate feasibility of ML based BM for 5GAdvanced and 6G communications.

The next generation 6G era is considered to be highly coupled with intelligent network management and orchestration, while 5G is completely renowned for micro-service architecture-based network cloudification. 6G has been revolutionized for satisfying the mandatory services and carry forwarding the potentialities of 5G to superior and intelligent level. 6G network structure is determined to be dynamic, densely deployed and extremely heterogeneous, and when integrated with a high degree of Quality of Service (QoS) completely transforms the complex architecture into a seamless operating process of classical networks. The immense role of Artificial Intelligence (AI) and Machine Learning (ML) is required for improving the paradigm of 6G for learning information from uncertain and dynamic environments. This integration of AI and 6G resembles a double-edged sword since the application of AI may positively influences the privacy or security of 6G on one side, and negatively introduces the possibility of security infringement into 6G on the other side. In specific, the self-sustaining networks in 6G are obtained by guaranteed application of intelligent security attack mitigation schemes and proactive threat discovery approaches that facilitate end-to-end future network automation. In this Chapter, a comprehensive review of AI and ML-based security enforcement techniques are contributed for improving reliability during robust data dissemination in 6G communications. It presents consolidated and solidified role of AI and ML towards the enforcement of security in 6G networks. In addition, it also demonstrates the challenges and solutions that are handled by the inclusion of AI and ML-based attack mitigation approaches concerning energy and security-based ultra-massive access.

The energy domain worldwide is experiencing high transformative pressure due to the imperative of climate change and new opportunities brought by various rapidly evolving digital technologies. Particularly, this change is affecting smart grids (SGs), which are increasingly shifting toward utilizing renewable and distributed energy sources. The natural intermittency of these energy sources increases the complexity of SG operations, such as ensuring continuity of energy supply and demand response balancing. In mitigation of these challenges, the tools and complex approaches that digitalization can provide have shown themselves particularly advantageous. There is a solid body of work showing how technologies like the Internet of Things (IoT), distributed ledgers, edge and cloud computing, machine learning (ML), etc., can be applied to address a variety of technical and economic problems in the energy sector, emphasizing SGs. This paper presents the most comprehensive literature review to date on digitalization in renewable energy source-based SGs, synthesizing over 200 studies across data analytics, artificial intelligence and ML, digital twins, edge–fog–cloud computing, the IoT, advanced metering infrastructure, and distributed ledger technologies. Unlike previous reviews, which are often limited to a single technology or narrow application, this work provides a cross-technology synthesis linking technical and financial aspects, identifies consolidated research gaps, and proposes a unified research agenda. The review further highlights future trends, including large language models, 6G communications, and distributed autonomous organizations, and discusses their implications for both industry practice and academic research.

A multi-band, small, high gain, low specific absorption rate (SAR), and circularly polarized (CP) textile wearable antenna fed using aperture coupled technique is designed on two layers of denim ( ε r = 1.2 , h = 0.787 mm as feeding layer) and felt ( ε r = 1.3 , h = 1.5 mm as a resonating layer) textile substrates. The antenna is designed on two layers with two relative permittivities to enhance the BW and reduce the negative mutual coupling. Afterward, another layer of denim with the complete ground of ShieldIt conductor is added to decrease the SAR value along with the directive gain. The antenna works for Industrial, Scientific, and Medical (ISM), 5G, and sub-6 GHz communication systems as it operates at 2.45–2.55 GHz and 3.8 GHz (3.6–4.15 GHz), and 5.6 GHz (5.55–5.65 GHz), respectively. The proposed antenna has the maximum directive gain of 8.35 dBi, acceptable SAR values at a 10 mm distance from the human body for both standards. In addition, the antenna's CP is examined, showing AR values of <2.5 dB at the working BW. Finally, the proposed antenna is measured and compared with the simulation results. A good agreement exists between simulation and measurement results.

With the advent of 5G communication networks, the number of devices on the core 5G network significantly increases.A5Gnetwork is a cloud native, massively connected internet of things (IoT) platform with a huge number of devices hosted on the network now known as massive machine type communication (mMTC). As ultra-low latency is pivotal in developing 5G communication, a proper cell association scheme is now required to meet the load and traffic needs of the new network, opposed to older cell association schemes which were based only on the reference signal received power (RSRP). This paper proposes an unsupervised machine learning algorithm, namely hidden Markov model (HMM) learning on the network's telemetry data, which is used to learn network parameters and select the best eNodeB for cell association. The proposed model uses an HMM learning followed by decoding for selecting the optimal cell for association.

Mobile traffic, which has increased significantly with the emergence of Fourth generation longterm evolution (4G-LTE) communications and advances in video streaming services, is still currently increasing at an incredible pace. Fifth-generation (5G) mobile communication systems, which were developed to deal with such a drastic increase in mobile traffic, aim to achieve ultra-high-speed data transmission, low latency, and the accommodation of many more connected devices compared to 4G-LTE systems. 5G communication uses high-frequency bandwidth to implement these features, which leads to an inevitable drawback of a high path loss. In order to overcome this disadvantage, small cell technology was developed, and is defined as small, low-power base stations that can extend the network coverage and solve the shadow area problem. Although small cell technology has these advantages, different problems, such as the effects of interference due to the deployment of a large number of small cells and the differences in devices accessing the network, need to be solved. To do so, it is necessary to develop an algorithm for a service method. However, general algorithms have difficulties in responding to the diverse environment of mobile communication systems, such as sudden increase in traffic in certain areas or sudden changes in the mobile population, and machine learning technology has been applied to solve this problem. This study employs a machine learning algorithm to determine small cell connections. In addition, a 5G macro system, the application of small cells, and the application of machine learning algorithms are compared to determine the performance improvement in the machine learning algorithm. Moreover, Support Vector Machine (SVM), Logistic Regression and Decision Tree algorithm are employed to show a training method that uses basic training data and a small cell on-off method, and the performance enhancement is verified based on this method.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;This study presents a novel approach for predicting fuel consumption in heavy-duty vehicles using a Machine Learning-based model, which is based on feedforward neural network (FFNN). The model is designed to enhance real-time vehicle monitoring, optimize route planning, and reduce both operational costs and environmental impact, making it particularly suitable for fleet management applications. Unlike traditional physics-based approaches, the FFNN relies solely on a refined selection of input variables, including vehicle speed, acceleration, altitude, road slope, ambient temperature, and engine power. Additionally, vehicle mass is estimated using a methodology presented elsewhere and is included as an input for a better generalization of the consumption model. This parameter significantly impacts fuel consumption and is particularly challenging to obtain for heavy-duty vehicles. Engine power is derived from both engine torque and speed (RPM), ensuring a direct relationship with fuel consumption while keeping computational complexity low.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Experimental data were collected from a fleet of heavy-duty trucks under real-world operating conditions. In particular, the study focuses on a turbocharged diesel truck with a maximum power output of 353 kW. The acquisition system is based on an On-Board Unit (OBU) featuring CANbus connectivity, GPS tracking and 4G/LTE-5G communication. The OBU enables continuous logging of vehicle and engine parameters over each mission. Additional road data, such as altitude and slope, were obtained from external mapping services.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;For the purpose of FFNN development, the training dataset was compiled from approximately 10 different routes, capturing diverse driving conditions, while validation was conducted on 10 independent routes to assess model generalization. Before training, input data were pre-processed using normalization and standardization techniques to ensure stable convergence and mitigate the impact of scale differences among input variables. A correlation analysis was performed to evaluate the relationships among available parameters and fuel consumption, guiding the selection of the most informative inputs. This step reduced redundancy in the dataset and improved network efficiency. Furthermore, hyperparameter optimization was conducted using a Randomized Grid Search algorithm, enabling the identification of an optimal network architecture - specifically in terms of the number of layers and hidden neurons - and training parameters while minimizing overfitting.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;The final model demonstrated high predictive accuracy across various validation routes, confirming the effectiveness of the FFNN in estimating fuel consumption with a reduced input set. Accuracy was tested on up to 50 routes, whose data where not considered for both training and validation; it was assessed that fuel consumption percentage error per route never exceeded 2%. This approach provides a practical and computationally efficient solution for fleet operators, facilitating advanced route planning and enabling more sustainable transportation strategies through cost-effective fuel management and emissions reduction.&lt;/div&gt;&lt;/div&gt;

6G wireless communications will be immersed in the future with different applications. It is expected to support all IoT services and satellite communications, and it is expected to support artificial intelligence (AI) and machine learning (ML). The THz frequency band has a vital role in 6G communication. In this study, a new graphene plasmonic two-port Terahertz (THz) MIMO antenna is analyzed by the characteristic mode theory (CMA), which gives a better insight into the physical behavior of the MIMO configurations. The proposed MIMO antenna is compact and designed on a Teflon substrate of 130 × 85 µm2. The antenna provides a wide impedance bandwidth of 0.6 THz (3.2–3.8 THz). The CMA is applied to clarify the position at which the mutual coupling gives a maximum concentrated current distribution. It is mainly used to reveal the preferable MIMO antenna configuration by the usage of the model significant and model current distribution property. To reduce the mutual coupling between the radiating elements, a complementary dumbbell-structure Metamaterial (MTM) unit cell is etched in the ground plane to block the coupling mode without any affection on the dominant mode. The preferred MIMO configuration gives high isolation of −55 dB between the radiating patches. The fundamental characteristics have been discussed in detail. The proposed MIMO design offers several attractive features such as large bandwidth of 0.6 THz, low envelope correlation coefficient (ECC) of 0.000168, compact size, stable radiation, high gain of 7.23 dB, and low channel capacity loss (CCL) of 0.006. The proposed MIMO design is suitable for different applications in the THz band according to the high-performance parameters such as biomedical applications, security scanning, sensing, IoT, and 6G high-speed wireless communication systems.

The unmanned aerial vehicles (UAVs) have been widely considered as one of the key applications for future wireless communication systems, where UAVs can be used as aerial base stations (BSs) for coverage extension, transmission improvement, emergency communication, and etc. Against this background, each UAV BS is expected to select the optimal codeword to form directional analog beams, and it is capable of achieving concurrent transmissions from multiple other UAV BSs simultaneously. However, in such a kind of UAV networks, due to the vast number of connected mobile users (MUEs), UAV BSs cannot timely and preciously select the codeword from the pre-defined codebook. Fortunately, machine learning (ML) is suitable for decreasing complexity in codeword selection, because ML could extract features from the data samples acquired in real environments. In this paper, we propose an ML approach to achieve an efficient and low complexity codeword selection for UAV networks. Specifically, we first derive the probabilities that multiple UAV BSs serve one MUE to obtain the average sum rate (ASR) in UAV networks. On that basis, we develop an ML approach to maximize the ASR, where we design a classifier based on support vector machine (SVM), where our ML approach is used for selecting the optimal codeword and maximizing the ASR in UAV networks. Third, we proposed an iterative sequential minimal optimization (SMO) training algorithm to train the data of all links between UAV BSs and MUEs, where the algorithm convergence is also discussed. Finally, we show the comparison between our proposed algorithm and the traditional methods by the simulation results. The simulation at last demonstrate our method is a more efficient solution for obtain a higher performance, where a much lower computational complexity can achieved than the traditional algorithm based on channel estimation.

Physically Unclonable Function (PUF) circuits are finding wide-spread use due to increasing adoption of IoT devices. However, the existing strong PUFs such as Arbiter PUFs (APUF) and its compositions are susceptible to machine learning (ML) attacks because the challenge-response pairs have a linear relationship. In this paper, we present a Recurrent-Neural-Network PUF (RNN-PUF) which uses a combination of feedback and XOR function to significantly improve resistance to ML attack, without significant reduction in the reliability. ML attack is also partly reduced by using a shared comparator with offset-cancellation to remove bias and save power. From simulation results, we obtain ML attack accuracy of 62% for different ML algorithms, while reliability stays above 93%. This represents a 33.5% improvement in our Figure-of-Merit. Power consumption is estimated to be 12.3μW with energy/bit of ≈ 0.16pJ.

The key technology concerning the dispatch and control of the cyber-physical-social systems (CPSS) integration and group machine learning (ML) based Web-of-Cells (WoC) is systematically investigated, aiming to develop an intelligent dispatching system that has a high penetration of distributed generations (DG). Based on practical engineering demands and the weakly-centralized WoC, which is characterized by self-organized co-evolution, high independence, high-efficiency synergy, and autonomous learning, a variety of advanced theoretical tools such as complex network theory, group ML, evolutionary game theory, and CPSS-based parallel system theory have been adopted to address the following key issue: How can we achieve overall optimal dispatching and control decision-making in a class of complex systems relying on a large number of group cells with characteristics of limited information, weak controllability, small capacity, and wide distribution? Starting from this, four basic scientific issues are discussed: 1) a modeling method for a self-organized coupled network of the CPSS integration-based WoC; 2) a stability analysis and stability control system of the self-organized evolution of the WoC; 3) a highly autonomous group intelligent decision (GID) method of an independent cell; 4) multi-cell synergetic evolutionary game and GID theory. Hence, an innovative breakthrough on the intersection of complex network game theory and group ML is expected to be obtained, contributing to the emergence of group knowledge in complex circumstances and a significant improvement in the level of GID. Lastly, based on the theoretical investigation in this paper, explorations on the development of the CPSS platform for the WoC as well as its engineering practice in application are conducted. Furthermore, the in-depth development of the WoC, as well as its anticipated technical challenges, are prospected and analyzed with the hope of applying it on practical smart distribution grid demonstration projects in the future.

In recent years, the application of machine learning (ML) techniques in research on the prediction of post-traumatic stress disorder (PTSD) has increased. However, concerns regarding the clinical relevance and generalizability of ML findings hamper their implementation by clinicians and researchers. Here in this systematic review we examined (1) the extent to which pre-, peri- and post-traumatic risk factors identified using ML approaches coincide with the theoretical understanding of the disorder; (2) whether new insights were gained through ML techniques; and (3) whether ML findings, combined with previous research, enable an integrative model of PTSD risk encompassing both predictor categories and their theoretical relevance. We reviewed ML studies on PTSD risk factors in PubMed, Web of Science and Scopus. Studies were included if they specified when predictors and PTSD symptoms were collected in temporal relation to the traumatic event. A total of 30 studies with 12,908 participants (mean age 36.5 years) were included. After extracting the 15 most important predictors from all studies, we categorized them into pre-, peri- and post-trauma exposure predictors and examined their associations with established theoretical models of PTSD. Many studies exhibited a risk of bias, assessed using the prediction model risk of bias assessment tool (PROBAST). However, we found overlaps in identified predictors across studies, a concordance between data-driven results and theory-driven research, and underexplored predictors identified through ML. We propose an integrative model of PTSD risk that incorporates both data-driven and theory-driven findings and discuss future directions. We emphasize the importance of standards on how to apply and report ML approaches for mental health.