The detection of plant disease using deep learning models has a gain a significant success in computer vision tasks, but even though most models obtain good performance, the application of these models on resource-limited edge devices is challenging. Having a trade-off between accuracy outputs and low computational costs becomes crucial . This research uses transfer learning on the PlantVillage dataset to evaluate six representative architectures. From well know CNNs to the last trending Vit. Beside accuracy, we measure the most important deployment metrics : model size, FLOPs, Parameters, and CPU inference latency. The finding shows us that Swin-Tiny has the highest accuracy ( 98.65%) yet needs huge resources (105 MB, 4.37 GFLOPs) whereas, the lightweight CNN (EfficentNet-B0) gives the best accuracy trade-off. Provides high level accuracy (97.10%) with low computational complexity (15.76 MB, 0.38 GFLOPs).
The Internet of Vehicles (IoV) enhances modern transportation but introduces significant cyber security vulnerabilities and privacy risks. Traditional centralized Intrusion Detection Systems (IDS) are inadequate for this environment, as they could compromise driver privacy. This study proposes a novel federated learning (FL) framework that enables collaborative cyber-attack detection across vehicles without sharing private data. We implemented and evaluated advanced FL algorithms (FedAvg, FedProx, SCAFFOLD, and FedNova) with a deep neural network (DNN) architecture. The framework is tested under a realistic non-identically and independently distributed (non-IID) data, where the federated clients are designed to detect specific in-vehicle Controller Area Network (CAN) attacks. Our experimental validation demonstrates that FedNova achieved an optimal F1-score of 81.67% for attack detection while maintaining complete data isolation. These findings establish that FL has the potential to be used as an IDS model in the IoV networks, while still preserving user privacy.
A physical layer addressing scheme that enables efficient multiple access is presented for wireless networks with multiple-input multiple-output (MIMO) communications tuned for maximizing beamforming gain. The method assigns unique, specially designed preamble sequences to each network node, which serve as physical addresses and enable this way selective training of the beamforming weights of each link. Communication is initiated using a previously proposed channel estimation procedure for SVD beamforming, where a source node transmits the preamble of the intended destination. For this scheme to be effective, the set of preambles must exhibit excellent correlation properties: low peak sidelobes in their autocorrelation to ensure accurate packet detection, and low peak cross-correlation to prevent a node from incorrectly detecting a preamble addressed for another. We propose an algorithm to find a set of binary sequences that jointly optimizes these properties. The performance of the addressing scheme is evaluated through extensive simulations. Receiver Operating Characteristic (ROC) curves are generated, demonstrating the trade-off between the probability of missed detection and the probability of false alarm with the proposed preambles. Results for a 4 × 4 MIMO system show that at a signal-to-noise ratio of 2 dB, a detection threshold can be chosen to achieve both missed detection and false alarm probabilities below 10−2, confirming the viability of the proposed method.
Let $\mathcal{T} = {\mathbb{F}_{{p^s}}} + {u_1}{\mathbb{F}_{{p^s}}} + {u_2}{\mathbb{F}_{{p^s}}} + {u_3}{\mathbb{F}_{{p^s}}} + u_1^2{\mathbb{F}_{{p^s}}}$ be a finite non-chain ring. In this paper, we propose a method of constructing quantum codes and LCD codes from constacyclic codes over $\mathcal{T}$. The first codes are obtained via the Gray map and the Calderbank-Shor-Steane construction from Euclidean dualcontaining constacyclic codes over $\mathcal{T}$. The second codes are obtained as the gray image of consta cyclic codes of LCD codes over $\mathcal{T}$.
Millimeter-wave phased array systems rely on periodic beam training intervals to maintain directional alignment, which interrupts data transmission and reduces effective throughput. To address this limitation, this paper introduces a dual-band architecture in which a narrow Steering Band performs continuous angular scanning using a Time-Varying Phased Array (TVPA), while a Primary Band sustains uninterrupted high-rate communication. The TVPA waveform produces a deterministic and range-invariant angular sweep, allowing receivers to identify their optimal sector without requiring suspension of the data link. The overhead comparison is carried out for a 2.16 GHz channel with Steering Band allocations from 25 to 400 MHz, and beam training durations from 100 to 104 µs across sweep intervals of 10 ms to 1 s. The analysis demonstrates a consistent trend in which the spectral cost of maintaining a continuous Steering Band remains fixed, while the cost of periodic sweep-based training increases as training duration becomes non-negligible. These results show that continuous steering can preserve directional alignment while avoiding the beam-training interruptions, offering a promising alternative to sector sweep procedures in directional wireless systems.
