Topo-ADV: Generating Topology-Driven Imperceptible Adversarial Point Clouds

Deep neural networks for 3D point cloud understanding have achieved remarkable success in object classification and recognition, yet recent work shows that these models remain highly vulnerable to adversarial perturbations. Existing 3D attacks predominantly manipulate geometric properties such as point locations, curvature, or surface structure, implicitly assuming that preserving global shape fidelity preserves semantic content. In this work, we challenge this assumption and introduce the first topology-driven adversarial attack for point cloud deep learning. Our key insight is that the homological structure of a 3D object constitutes a previously unexplored vulnerability surface. We propose Topo-ADV, an end-to-end differentiable framework that incorporates persistent homology as an explicit optimization objective, enabling gradient-based manipulation of topological features during adversarial example generation. By embedding persistence diagrams through differentiable topological representations, our method jointly optimizes (i) a topology divergence loss that alters persistence, (ii) a misclassification objective, and (iii) geometric imperceptibility constraints that preserve visual plausibility. Experiments demonstrate that subtle topology-driven perturbations consistently achieve up to 100% attack success rates on benchmark datasets such as ModelNet40, ShapeNet Part, and ScanObjectNN using PointNet and DGCNN classifiers, while remaining geometrically indistinguishable from the original point clouds, beating state-of-the-art methods on various perceptibility metrics.

Vehicle to vehicle path loss modeling at millimeter wave band for crossing cars

Fifth generation (5G) new radio is now offering sidelink capability, which allows direct vehicle-to-vehicle (V2V) communication. Millimeter wave (mmWave) enables low-latency mission-critical V2V communications, such as forward crash warning, between two vehicles crossing on a road without dividers. In this article, we present a measurement-based path loss (PL) model for V2V links operating at 59.6 GHz mmWave when two vehicles approach from opposite sides and cross each other. Our model outperforms other existing PL models and can reliably model both approaching and departing vehicle scenarios.

Synthesis of omnidirectional path loss model based on directional model and multi-elliptical geometry

Millimeter wave (mmWave) technology offers high throughput but has a limited radio range, necessitating the use of directional antennas or beamforming systems such as massive MIMO. Path loss (PL) models using narrow-beam antennas are known as directional models, while those using omnidirectional antennas are referred to as omnidirectional models. To standardize the analysis, omnidirectional PL models for mmWave ranges have been introduced, including TR 38.901 by 3GPP, which is based on measurements from directional antennas. However, synthesizing these measurements can be complex and time-consuming. This study proposes a numerical approach to derive an omnidirectional model from directional data using multi-elliptical geometry. We assessed the effectiveness of this method against existing PL models for mmWaves that are available in the literature.

Power angular spectrum versus Doppler spectrum -- Measurements and analysis

In this paper, we present an empirical verification of the method of determining the Doppler spectrum (DS) from the power angular spectrum (PAS). Measurements were made for the frequency of 3.5 GHz, under non-line-of-sight conditions in suburban areas characteristic of a university campus. In the static scenario, the measured PAS was the basis for the determination of DSs, which were compared with the DSs measured in the mobile scenario. The obtained results show that the proposed method gives some approximation to DS determined with the classic methods used so far.

Variability of radio signal attenuation by single deciduous tree versus reception angle at 80 GHz

Vegetation significantly affects radio signal attenuation, influenced by factors such as signal frequency, plant species, and foliage density. Existing attenuation models typically address specific scenarios, like single trees, rows of trees, or green spaces, with the ITU-R P.833 recommendation being a widely recognized standard. Most assessments for single trees focus on the primary radiation direction of the transmitting antenna. This paper introduces a novel approach to evaluating radio signal scattering by a single deciduous tree. Through measurements at 80 GHz and a bandwidth of approximately 2 GHz, we analyze how total signal attenuation varies with the reception angle relative to the transmitter-tree axis. The findings from various directional measurements contribute to a comprehensive attenuation model applicable to any reception angle and also highlight the impact of bandwidth on the received signal level.

Spectral efficiency for mmWave downlink with beam misalignment in urban macro scenario

In this paper, we analyze the spectral efficiency for millimeter wave downlink with beam misalignment in urban macro scenario. For this purpose, we use a new approach based on the modified Shannon formula, which considers the propagation environment and antenna system coefficients. These factors are determined based on a multi-ellipsoidal propagation model. The obtained results show that under non-line-of-sight conditions, the appropriate selection of the antenna beam orientation may increase the spectral efficiency in relation to the direct line to a user.

THz Channels for Short-Range Mobile Networks: Multipath Clusters and Human Body Shadowing

The THz band (0.1-10 THz) is emerging as a crucial enabler for sixth-generation (6G) mobile communication systems, overcoming the limitations of current technologies and unlocking new opportunities for low-latency and ultra-high-speed communications by utilizing several tens of GHz transmission bandwidths. However, extremely high spreading losses and other interaction losses pose significant challenges to establishing wide-area communication coverage, while human body shadowing further complicates maintaining stable communication links. Although point-to-point (P2P) fixed wireless access in the THz band has been successfully demonstrated, realizing fully mobile and reliable wireless access remains a challenge due to numerous issues to be solved for highly directional communication. To provide insights into the design of THz communication systems, this article addresses the challenges associated with THz short-range mobile access networks. It offers an overview of recent findings on the environment-dependence of multipath cluster channel properties and the impact of human body shadowing, based on measurements at 300 GHz using a double-directional high-resolution channel sounder and a motion capture-integrated channel sounder.

MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from μWatts to MWatts for Sustainable AI

Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper introduces MLPerf Power, a comprehensive benchmarking methodology with capabilities to evaluate the energy efficiency of ML systems at power levels ranging from microwatts to megawatts. Developed by a consortium of industry professionals from more than 20 organizations, MLPerf Power establishes rules and best practices to ensure comparability across diverse architectures. We use representative workloads from the MLPerf benchmark suite to collect 1,841 reproducible measurements from 60 systems across the entire range of ML deployment scales. Our analysis reveals trade-offs between performance, complexity, and energy efficiency across this wide range of systems, providing actionable insights for designing optimized ML solutions from the smallest edge devices to the largest cloud infrastructures. This work emphasizes the importance of energy efficiency as a key metric in the evaluation and comparison of the ML system, laying the foundation for future research in this critical area. We discuss the implications for developing sustainable AI solutions and standardizing energy efficiency benchmarking for ML systems.