Afeka College of Engineering

Collisions between over-height vehicles and low-clearance bridges cause infrastructure damage and pose safety risks. Existing detection systems rely primarily on optical sensors, which suffer from performance degradation in adverse weather conditions. This paper presents an alternative approach based on a 94 GHz millimeter-wave radar that achieves velocity-independent height measurement. The proposed technique exploits the ratio of Doppler shifts from two scattering centers on a vehicle, specifically the roof and the wheel-road interface. This ratio depends only on the measurement geometry, as the unknown vehicle velocity cancels algebraically, enabling direct height computation without speed measurement. The paper provides a closed-form height estimation model, analyzes the trade-off between frequency resolution and geometric constancy during integration, and presents experimental validation using a scaled laboratory testbed. An optical tracking system is used solely for ground-truth validation in the laboratory and is not required for operational deployment. Results across six test cases with heights ranging from 20 cm to 46 cm demonstrate an average absolute error of 0.60 cm and relative errors below 3.3 percent. A scaling analysis for representative full-scale geometries indicates that at highway speeds of 80 km/h, integration times in the millisecond range (approximately 3-18 ms for representative 20-50 m measurement standoff) are feasible; warning distance can be extended independently by upstream radar placement. The expected advantage in fog, rain, and dust is based on established W-band propagation characteristics; dedicated adverse-weather and full field validation (including multipath, clutter, and multi-vehicle scenarios) remain future work.

Alignment-free signals in coding sequences provide a scalable route to taxonomic inference, quality control of large phylogenies, and rapid screening of genomic data. We present a model-agnostic framework that represents genomes or coding-sequence collections using codon usage (64-D) and codon-pair usage (4096-D) profiles, and we introduce Taxonomic Consistency (TC), a simple, rank-aware external index, for evaluating supervised predictions or unsupervised clustering against the hierarchical taxonomy. Across multiple taxonomic ranks (e.g., Domain, Order, etc.), compact supervised models and standard clustering methods are assessed with both internal (Silhouette) and external (TC) validation. In large-scale experiments, handling class imbalance and applying principled normalization had a greater impact on performance than sequence-level preprocessing, and codon usage profiles yielded the highest TC and coherent unsupervised structure, while codon-pair features provided complementary resolution within specific clades. We release open, versioned code and reproducible workflows to facilitate adoption in ecological and evolutionary pipelines. Taken together, alignment-free codon and codon-pair representations, paired with TC for transparent external validation, offer a practical complement to tree-based methods-especially for rapid screening, sanity checks, and exploratory analyses at higher ranks.

Abstract We study the low‐lying zeros of a family of ‐functions attached to the complex multiplication elliptic curve , for each odd and square‐free integer . Specifically, upon writing the ‐function of as for the appropriate Grössencharakter of conductor , we consider the collection of ‐functions attached to , , where for each integer , denotes the primitive character inducing . We observe that of the ‐functions in have negative root number. is thus not one of the essentially homogeneous families of the universality conjecture of Sarnak, Shin and Templier [33], with unitary, symplectic or orthogonal (odd or even) symmetry type. By computing the one‐level density in the family of ‐functions in with conductor at most , we find that naturally decomposes into subfamilies: more specifically, a collection of symplectic ( for , even) and orthogonal ( for , odd) subfamilies. For each such subfamily, we moreover compute explicit lower order terms in decreasing powers of .

Federated Learning (FL) is a distributed machine learning framework that enables model training while preserving user data privacy. However, the heterogeneity of the distributed clients regarding, e.g., system performance, data quality and network conditions, makes client selection a critical factor in optimising the performance of FL. We propose a fair and efficient client selection algorithm (FeFL) based on dynamic contribution evaluation. The algorithm optimises the client selection process by evaluating data quality, device performance, and their impact on model accuracy. FeFL introduces a dynamic contribution evaluation model that adjusts the weights of various contributions based on different training stages, enabling the selection of the most contributing clients at minimal cost. Additionally, the waiting factor introduced in FeFL ensures fairness in client selection. Experimental results on real-world datasets demonstrate that the algorithm significantly improves model accuracy and convergence speed under both Independent and Identically Distributed (IID) and non-Independent and Identically Distributed (non-IID) conditions while exhibiting greater robustness and stability in managing data heterogeneity.

We introduce an adaptation of integral approximation operators to set-valued functions (SVFs, multifunctions), mapping a compact interval [a, b] into the space of compact non-empty subsets of Rd. All operators are adapted by replacing the Riemann integral for real-valued functions by the weighted metric integral for SVFs of bounded variation with compact graphs. For such a SVF F, we obtain pointwise error estimates for sequences of integral operators at points of continuity, leading to convergence at such points to F. At points of discontinuity of F, we derive estimates, which yield the convergence to a certain set described in terms of the metric selections of F. To obtain these estimates we refine and extend known results on approximation of real-valued functions by integral operators. Our analysis uses recently defined one-sided local quasi-moduli at points of discontinuity and several notions of local Lipschitz property at points of continuity. We also provide a global approach for error bounds. A multifunction F is represented by the set of all its metric selections, while its approximation (its image under the operator) is represented by the set of images of these metric selections under the operator. A bound on the Hausdorff distance between these two sets of single-valued functions in L1 provides our global estimates. The theory is applied to concrete operators: the Bernstein–Durrmeyer operator and the Kantorovich operator.

This paper analyses the past and anticipated developments in collaborative smart production work environment and points at the required skills to best utilize and flourish in this newly formed work environment. The paper identifies the new work requirements using the job type and its related required technologies and maps the work requirements to the set of skills that may fulfill these requirements. An important notion in this paper is that a shopfloor usually involves several different work environments, each with its unique set of work requirements and associated skills. Thus, tailoring a subset of skills to these set of requirements is the suggested strategy. We use a small example of assembly shopfloor for illustrating the proposed approach. Finally, we propose future research related to this study.