This a topical review on the applications of silicene and the Xenes, the elemental 2D materials family beyond graphene. Two paradigmatic applications of silicene, i.e. field-effect transistors and flexible membranes, are here reviewed and an overview on the applications involving Xenes out of silicene is discussed. Finally, an outlook on the lab-to-fab transition for silicene and the Xenes is provided.

Perovskite technology has shown impressive improvement in the last decade. Here, the first GPS-perovskite-powered application is showcased by integrating flexible perovskite modules (fPSMs) onto an animal tracking collar for powering the IoT device that monitors wildlife bison to preserve the species and its habitat. A prototype of a solar-powered collar was designed to be more optimal for monitoring ample wildlife than commercially available asset-tracking devices, so it can provide necessary data to successfully introduce bison to new habitats. The fPSM were fabricated on a plastic substrate and connected in series configuration to generate power of 400 mW. A robust packaging with polycarbonate sheets and mechanical rivets was realized to withstand harsh environmental conditions, validated by conducting scratch and bending tests. The electronics and software system were optimized for ultra-low power mode operation, consuming only 1.9 mWh, which is 200 times less than the energy generated by our modules. The packaged modules in the outdoor testing setup retained over 30 % of their initial PCE and power after 350 days (8400 hours), giving still 60 times more power than necessary over ~1 year of operational condition.

This work studies the 3D stacking of an approach to silicon carbide (SiC) integrated circuit (IC) chips using flip-chip technology with gold stud bumps for high-temperature (up to 600°C) applications. Standard photolithography, sputtering deposition, and lift-off process were used for chip metallization and patterning with titanium (Ti), tantalum silicide (TaSi2), platinum (Pt), and gold (Au) thin films. Gold stud bumps were used to bond the SiC chips with a flip-chip die bonder forming a daisy chain connection. Die shear tests were conducted, and the electric resistance of the daisy chain interconnect between the chips was measured before and after thermal aging in the air at 600°C for up to twelve days. It is found that the electric resistance of the daisy chain interconnects decreases with heating and stabilizes at about 1 Ohm for 36 bumps, showing that the thermal aging process improves the electric performance of the interconnect with gold stud bumps. The destructive die shear test shows that, with thermal aging, the shear force decreases for the chip stacks and stabilizes at about 15-gram force (gf) per bump with TaSi2 diffusion barrier in the metallization. In contrast, the shear strength increases for the chip stacks without TaSi2 barrier in the metallization.

Virtualized Network Function (VNF) sharing among multiple Fifth Generation (5G) slices allows network operators to increase the efficiency and utilization of the network. However, this sharing of VNFs can result in a degradation of the performance of the slices in the presence of an attack. In this paper, we evaluate the impact of flooding attacks on the performance of 5G slices with different VNF sharing configurations. We consider two VNF sharing configurations, in the first configuration, the Session Management Function (SMF) and User Plane Function (UPF) are shared among the deployed slices, while the SMF and UPF of the slices are isolated in the second configuration. The performance of these configurations is evaluated using different traffic types under two flooding attack scenarios; a ping flood attack targeting the data plane of 5G network, and a registration request flood attack directed at the control plane of 5G network. Our results showed different responses in the control and data planes. In the data plane, isolating VNFs of the slices provides better performance and mitigates the adverse effects of the attacks studied.

This narrative review delves into the diagnostic test accuracy of AI-assisted mammography for breast imaging, examining its potential, challenges, and future implications. AI's integration into medical imaging has promised to revolutionize breast cancer diagnosis, prompting an evaluation of its performance and impact. A comprehensive literature search was conducted, resulting in the identification of key articles. These studies encompassed diverse AI techniques, training datasets, and geographical contexts. The review synthesizes findings on sensitivity, specificity, interpretability, ethical considerations, and global implications. The studies collectively demonstrate AI's potential in enhancing diagnostic accuracy, with deep learning algorithms exhibiting commendable sensitivity and specificity levels. However, challenges arise in data diversity, algorithmic bias, and interpretability. Ethical and regulatory considerations underscore the need for responsible AI deployment. The concept of augmented intelligence, wherein AI collaborates with radiologists, holds promise for future clinical practice. AI-assisted mammography holds transformative potential in breast cancer diagnosis, accuracy and efficiency. While challenges exist, collaborative efforts between stakeholders are essential to navigate complexities and realize AI's benefits. The review concludes that while AI holds promise in breast cancer diagnosis, ethical, global, and research considerations are imperative for its responsible and effective deployment.

Molecular communication via diffusion (MCvD) expects Brownian motions of the information molecules to transmit information. However, the signal propagation largely depends on the geometric characteristics of the assumed flow model, i.e., the characteristics of the environment, design, and position of the transmitter and receiver, respectively. These characteristics are assumed to be lucid in many ways by either consideration of one-dimensional diffusion, unbounded environment, or constant drift. In reality, diffusion often occurs in blood-vessel-like channels. To this aim, we try to study the effect of the biological environment on channel performance. The Red-Blood Cells (RBCs) found in blood vessels enforces a higher concentration of molecules towards the vessel walls, leading to better reception. Therefore, in this paper we derive an analytical expression of Channel Impulse Response (CIR) for a dispersion-advection-based regime, contemplating the influence of RBCs in the model and considering a point source transmitter and a realistic design of the receiver.

This study investigates an innovative method to combat ransomware, leveraging the New Technology File System (NTFS) spare space in Windows operating systems. The focus is on RansomCillin, a tool designed for effective data recovery after ransomware attacks. The methodology entailed a simulated environment using prevalent ransomware strains to assess RansomCillin’s file recovery success, time efficiency, data integrity, and system performance impact. Results showed RansomCillin’s high effectiveness in restoring encrypted files with minimal system disruption. Despite its promising performance, the study is limited to NTFS file systems and specific ransomware families, indicating the need for broader application in future research. This work suggests a paradigm shift in ransomware mitigation strategies, emphasizing proactive recovery and continuous adaptation to evolving cyber threats. RansomCillin’s development and testing highlight its potential as a practical solution in the ongoing battle against ransomware.

Efficiently transferring image-based object detectors to the domain of video remains challenging under resource constraints. Previous efforts used feature propagation to avoid recomputing unchanged features. However, the overhead is significant when working with very slowly changing scenes, such as in surveillance applications. In this paper, we propose temporal early exits to reduce the computational complexity of video object detection. Multiple temporal early exit modules with low computational overhead are inserted at early layers of the backbone network to identify the semantic differences between consecutive frames. Full computation is only required if the frame is identified as having a semantic change to previous frames; otherwise, detection results from previous frames are reused. Experiments on ImangeNet VID and TVnet show that the approach can accelerate video object detection by 1.7x compared to SOTA, with a reduction of only <1% in mAP.

In the realm of 3D monolithic integrated circuits, inter-layer vias are prone to defects during fabrication, assembly, and operation that necessitate robust Built-In Self-Test solutions. This paper introduces an innovative BIST design aimed at the detection and localization of different types of faults in irregularly placed ILVs using a walking pattern methodology. In the proposed BIST methodology, the ILVs are clustered based on the fault occurrence probability distribution. Then, an effective detection approach is developed for all stuck-at faults, bridging faults, and almost all multiple faults in a single cluster. This approach allows designers to adjust different parameters such as fault coverage percentage, fault localization, and test time in alignment with specific requisites. The proposed BIST method reduces test configurations to two and test time to just 20 cycles by dividing ILVs into more clusters, overcoming a key limitation of recent approach. This methodology also demonstrates improved efficiency in terms of power consumption, area, and hardware utilization, particularly for large-scale benchmarks. For instance, when considering LU32PEENG and the scenario where ILVs are divided into 64 clusters, the power, area, and hardware overhead are 0.82% and 1.03%, and 1.14%, respectively.

This study explores the architectural design firms in Shi Jiazhuang City, China, focusing on enhancing their competitiveness in the services industry. Offering a holistic view of these firms’ challenges and opportunities by employing a dual survey research design incorporating internal and external perspectives. The study highlights 10 Chinese architectural design companies in terms of their strengths, weaknesses, opportunities, and threats (SWOT) while integrated with Porter’s Model in the external forces. Furthermore, the research introduces Betz’s Strategic Business Models, providing architectural firms with a roadmap for future success. The study identifies four generic business models applicable to respondents, including Strategic Response, Strategic Enterprise, Strategic Innovation, and Strategic Firm, offering tailored strategies to leverage strengths and address weaknesses effectively. In Porter’s Five Forces analysis, three generic strategies are identified, emphasizing the need for strategic responses to new entrants, leveraging partnerships for supplier negotiations, and positioning as a premium service provider to mitigate customer bargaining power. This research equips architectural firms in Shi Jiazhuang City with actionable recommendations to thrive in a competitive industry. Integrating SWOT and Porter’s Five Forces analyses with Betz’s Strategic Business Models, this study leads to strategic adaptation to market dynamics and securing a solid position in the industry.

This technical note presents proof of the convergence of the Alternating Direction Method of Multipliers (ADMM) for addressing the sharing problem when applied in conjunction with two algorithms: 1) the stochastic STEP-GP algorithm and 2) its variant named LGP, which includes adaptive uniform quantization. For the case using LGP, the coordinator can assign different quantization resolutions at each iteration and we assume that the number of bits that can be assigned is unrestricted and can go to infinity. This document describes and analyzes the two methods for integrating learning and uniform quantization into the ADMM to reduce its communication overhead and a general formulation of their communication decision method. The problems are formulated for a multi-agent setting.

Three-dimensional (3-D) through-the-wall imaging is a challenging topic. It has attracted some research attention in recent years. The 3-D structure is hard to reconstruct because of the limited measurement in the CT-mode imaging method. In order to obtain an accurate 3-D result, the 3-D total variation (3-D TV) algorithm has been adopted. However, the result suffers from image blurring and artifacts. In this paper, a tensor-based optimization framework is proposed to exploit more features of the 3-D wall structure and make up for the shortcomings of the 3-D TV algorithm. The 3-D building structure is modeled as a three-order tensor. Just like the 3-D TV algorithm, the local similarity is considered by the TV regularization constraint to guarantee the reconstruction of the edge. Besides, the group sparsity of the structure is considered to suppress the effect of artifacts and blur. Moreover, in order to keep the global correlation of the image in the case of the errors, the tensor Tucker decomposition is adopted. The performance of this method is discussed in the simulation and real radar data results. It shows that the artifacts and blur are suppressed effectively and the 3-D structure is kept as well.

A document by Aniruddha Das . Click on the document to view its contents.

This paper presents a novel, power- and hardware-efficient, multiuser, multibeam RIS (Reflective Intelligent Surface) architecture for multiuser MIMO, especially suited to operate in very high frequency bands (e.g., high mmWave and sub-THz), where channels are typically sparse in the beamspace and line-of-sight (LOS) is the dominant component. The key module is formed by an active multiantenna feeder (AMAF) with a small number of active antennas, placed in the near field of a RIS with a much larger number of passive controllable reflecting elements. We propose a pragmatic approach to obtain a steerable beam with high gain and very low sidelobes. Then K independently controlled beams can be achieved by closely stacking K such AMAF-RIS modules. Our analysis includes the mutual interference between the modules and the fact that, due to the delay difference of propagation through the AMAF-RIS structure, the resulting channel matrix is frequency selective even in the presence of pure LOS propagation. We consider a 3D geometry and show that “beam focusing” is in fact possible (and much more effective in terms of coverage) also in the far-field, by creating spotbeams with limited footprint both in angle and in range. Our results show that: 1)  simple RF beamforming without computationally expensive baseband digital multiuser precoding is sufficient to practically eliminate multiuser interference when the users are chosen with sufficient angular/range separation, thanks to the extremely low sidelobes of the proposed module; 2) the impact of beam pointing errors with standard deviation as large as 2.5 deg and RIS quantized phase-shifters with quantization bits > 2 is essentially negligible; 3) The proposed architecture is more power efficient and much simpler from a hardware implementation viewpoint than standard RF beamforming active arrays with the same beamforming performance. As a side result, we show also that the array gain of the proposed AMAF-RIS structure grows linearly with the RIS aperture, in line with classical results for standard reflector antennas.

Scaled-down vehicles provide a controlled and repeatable environment for testing autonomous driving algorithms, expediting development while mitigating risks associated with full-scale vehicle testing. This paper introduces a meticulously designed scaled-down electric vehicle, emphasizing vehicle state measurement, precise control, perception sensing, and electric safety. Our approach utilizes actuators with feedback, provides enhanced reliability and precision, and torque across all speed ranges, in contrast to conventional scaled platforms built with actuators of radio-controlled cars. Apart from the conventional sensors used in the existing scaled platforms, our vehicle incorporates novel sensor modules to measure wheel angular velocities, steering angles, and battery cell voltages, which can also be seamlessly integrated into these platforms. We demonstrate the vehicle's motion with manual and autonomous operation and showcase its features for validation.

Recent years have witnessed significant advancements in cross-chain technology. However, the field faces two pressing challenges. On the one hand, hacks on cross-chain bridges have led to monetary losses of around 3.1 billion USD, highlighting flaws in security models governing interoperability mechanisms and the ineffectiveness of incident response frameworks. On the other hand, users and bridge operators experience restricted privacy, which broadens the potential attack surface.In this paper, we present the most comprehensive study to date on the security and privacy of blockchain interoperability. We employ a systematic literature review, yielding a corpus of 212 relevant documents, including 58 academic papers and 154 gray literature documents, out of a pool of 531 results. We systematically categorize 57 interoperability solutions based on a novel security and privacy taxonomy. Our dataset, comprising academic research, disclosures from bug bounty programs, and audit reports, exposes 45 cross-chain vulnerabilities, 25 theoretical attacks, and 93 mitigation strategies. Leveraging this data, we analyze 14 notable bridge hacks accounting for over 2.9 billion USD in losses, mapping them to the identified vulnerabilities.Our findings reveal that a substantial portion (65.8%) of stolen funds originates from projects secured by intermediary permissioned networks with unsecured cryptographic key operations. Privacy-wise, we demonstrate that achieving unlinkability in cross-chain transactions is contingent on the underlying ledgers providing some form of confidentiality. Our study offers critical insights into the security and privacy of cross-chain systems. We pinpoint promising future research directions, underscoring the urgency of enhancing security and privacy efforts in cross-chain technology. The identified improvements can mitigate the financial risks associated with bridge hacks, fostering user trust in the blockchain ecosystem and, consequently, wider adoption.

Radar-based human activity recognition (HAR) is a popular research field. Despite claims of high accuracy on self-collected datasets, the ability of these models to handle unexpected scenarios has been largely overlooked. This work introduces a framework for analyzing corruption robustness of radar micro-Doppler spectrogram classification. A set of corruptions are categorized, applied, and systematically tested on common model architectures. Diverse training methods, including adversarial training, cadence velocity diagram (CVD) transformation and data augmentation, are explored. The performance is evaluated on two tasks: indoor HAR and continuous aquatic HAR. Our study unveils several insights. Firstly, relying solely on accuracy may not adequately assess model performance due to dataset limitations. All well-trained models exhibit sensitivity to corruptions. Secondly, deeper convolutional neural network (CNN) models excel in both accuracy and robustness, but confront the problem of overfitting to background. Thirdly, adversarial training enhances robustness against corruptions, albeit at the cost of a slight decrease in accuracy. Lastly, combining data augmentation and adversarial training achieves a balance between accuracy and robustness. In essence, our study contributes to a more profound understanding of the complex interplay between model architecture, classification accuracy, and corruption robustness in radar HAR tasks.

Walking on uneven terrain is a common task throughout daily living and can cause changes in lower-limb kinematics and mechanics. Joint loading, while difficult to measure experimentally, may also increase on uneven terrain, which could contribute to the development of joint pain and debilitating conditions. This study investigated how lower-limb joint loading is affected during a step on coronally-uneven terrain and during the subsequent recovery step using data from individuals without mobility impairments (n=5) and musculoskeletal modeling and simulation, which can be used to reliably estimate joint contact forces. The simulations indicated changes in joint loading during both the uneven and recovery steps on uneven terrain compared to level-ground walking, and these changes varied throughout stance. During both the uneven and recovery steps, the largest increases in peak joint contact forces and impulses were at the ankle joint during early stance. These results suggest that individuals may rely more on early compensations at the ankle than compensations at the hip or knee throughout stance. Future work should include populations with mobility impairments and investigate if different compensatory strategies or interventions can influence the outcomes.