Amar Y. Jaffar

Prostate cancer is the second most commonly diagnosed malignancy in men, yet interpretation of multiparametric MRI (mpMRI) suffers from substantial inter-observer variability and depends on scarce expert radiologists. Expert readers follow a hierarchical, coarse-to-fine visual strategy: they first localize the prostate gland and then direct focused attention to tissue within it. Inspired by this biological selective-attention mechanism, we propose a segmentation-guided framework in which a modality-aware 3D Vision Transformer (ViT) first delineates the prostate gland from T2-weighted, ADC, and DWI volumes, and the resulting anatomical mask then constrains downstream cancer detection. Global self-attention models long-range anatomical context, analogous to the integration of peripheral and foveal information in human vision. Detection was evaluated through two pathways: gray-level co-occurrence matrix (GLCM) texture features with classical classifiers, and a ViT classifier. On 200 patients from the public PROSTATEx cohort, the proposed segmentation model achieved a Dice Similarity Coefficient of [0.903 ± 0.033] versus [0.898 ± 0.034] for an optimized U-Net. Ablation experiments confirmed that anatomical guidance improved both pathways. These results show that emulating expert visual search strategies yields accurate and interpretable computer-aided prostate cancer diagnosis.

Prostate cancer is the second most commonly diagnosed malignancy in men, yet interpretation of multiparametric MRI (mpMRI) suffers from substantial inter-observer variability and depends on scarce expert radiologists. Expert readers follow a hierarchical, coarse-to-fine visual strategy: they first localize the prostate gland and then direct focused attention to tissue within it. Inspired by this biological selective-attention mechanism, we propose a segmentation-guided framework in which a modality-aware 3D Vision Transformer (ViT) first delineates the prostate gland from T2-weighted, ADC, and DWI volumes, and the resulting anatomical mask then constrains downstream cancer detection. Global self-attention models long-range anatomical context, analogous to the integration of peripheral and foveal information in human vision. Detection was evaluated through two pathways: gray-level co-occurrence matrix (GLCM) texture features with classical classifiers, and a ViT classifier. On 200 patients from the public PROSTATEx cohort, the proposed segmentation model achieved a Dice similarity coefficient of [0.905] versus [0.831] for an optimized U-Net. The segmentation-guided ViT achieved an AUC of [0.987] and an accuracy of [0.998] for clinically significant cancer detection. Ablation experiments confirmed that anatomical guidance improved both pathways. These results show that emulating expert visual search strategies yields accurate and interpretable computer-aided prostate cancer diagnosis.

This paper presents an interval-observer-based actuator fault detection and isolation (FDI) method for underactuated autonomous underwater vehicles (AUVs) under bounded hydrodynamic uncertainty and time-varying ocean currents. A locally frozen linear time-invariant (LTI) representation enables deterministic set-membership analysis, and the robust adaptive propagated interval observer (RAPIO) propagates admissible center–radius state bounds within a Lyapunov framework. Adaptivity is introduced through a reinforcement learning (RL)-augmented uncertainty-bound modulation mechanism, where an offline-trained agent scales a nonnegative channel-wise slack term without modifying the scheduled observer-gain rule or the nominal center predictor. Under the stated observer and disturbance-envelope conditions, positivity, stability, and diagnostic-channel inclusion hold for any bounded learning signal. Actuator loss-of-effectiveness (LoE) faults are represented through the actuator-effectiveness channel and detected through interval-consistency violations, enabling axis-wise isolation of surge, yaw-rate, and pitch-rate actuator faults. The same schedule-blind decision layer is additionally evaluated with structurally distinct additive-bias and stuck/jam actuator models. All stuck/jam events are detected, and bias-magnitude sweeps identify channel-wise 100%-detection boundaries with zero false alarms. A structured 72-case scenario sweep shows reliable detection, strong false-alarm rejection, and acceptable detection delays compared with benchmark observers.

The fast growth of smart farming technologies and Internet of Things (IoT) sensors has transformed the agricultural sector, but has brought some fundamental problems, such as data privacy, scalability, and trust in distributed systems. In this paper, AgriChain-FL, a federated blockchain architecture, is introduced and guarantees privacy-preserving, transparent, and energy-efficient collaboration in agricultural ecosystems. The framework integrates federated learning (FL) of decentralized model training with a hybrid Practical Byzantine Fault Tolerance-Proof of Authority (PBFT-PoA) blockchain to allow aggregating data safely, auditing, and participation through incentives. The proposed system was developed based on the SmartFarm Sensor Dataset. Models are trained individually at each local farm node. The hybrid consensus protocol is more scalable, latency-reduced, and uses less energy. The experimental results indicated that AgriChain-FL was more accurate (92.4%), had a throughput of 2350 TPS, and a block latency of 1.0 s, which is also more favorable to analogous frameworks in learning and blockchain performance. Privacy leakage and energy cost were minimized by 73% and 45% respectively. AgriChain-FL is a successful balance between federated intelligence and blockchain trust that provides a privacy-conscious and sustainable smart farming platform. These findings confirm its possibilities as the basis of secure, decentralized, and energy-aware digital agricultural systems.

The number theoretic transform (NTT) is widely used to accelerate polynomial multiplication in modern cryptographic systems, including homomorphic encryption. In standard NTT computations, the forward NTT (FNTT) and inverse NTT (INTT) are implemented using the Cooley–Tukey (CT) and Gentleman–Sande (GS) butterfly structures, respectively. Additionally, the INTT requires an inverse‑scaling operation after the transform. These FNTT, INTT, and inverse‑scaling operations exhibit an inherent structural heterogeneity. Most configurable NTT architectures force these heterogeneous operations onto a single shared datapath, resulting in complex control logic and inefficient hardware utilization. In this paper, we optimize arithmetic resource allocation in configurable NTT hardware to address the structural heterogeneity of FNTT, INTT, and inverse‑scaling operations. We propose a compile‑time configurable NTT architecture supporting polynomial sizes from 2¹² to 2¹⁶ and modulus sizes up to 64 bits through a structurally aware butterfly datapath. The design employs three modular multipliers (one dedicated to each CT butterfly, GS butterfly, and INTT scaling operation). We further introduce an area‑efficient modular multiplier that integrates a parallel integer polynomial multiplier with an optimized Montgomery‑based reduction. Finally, we propose benchmarking metrics based on the area‑delay product for modular multipliers and the logic‑normalized coefficient throughput for the NTT accelerator. The architecture is implemented in Verilog HDL using the Vivado design environment and evaluated up to the post‑place‑and‑route stage on an Artix‑7 FPGA device. Comprehensive performance results and comparisons with state‑of‑the‑art designs confirm that the proposed architecture achieves significantly lower area with acceptable clock‑cycle overhead, demonstrating its suitability for area‑constrained applications.

Lattice-based post-quantum cryptography relies on fast polynomial multiplication. The Number-Theoretic Transform (NTT) is the key operation that enables this acceleration. To provide high throughput and low latency while keeping the area overhead small, hardware implementations of the NTT is essential. This is particularly true for resource-constrained devices. However, existing NTT accelerators either achieve high throughput at the cost of large area overhead or provide compact designs with limited pipelining and low operating frequency. Therefore, this article presents a compact, seven-stage pipelined NTT accelerator architecture for post-quantum cryptography, using the CRYSTALS–Kyber algorithm as a case study. The CRYSTALS–Kyber algorithm is selected due to its NIST standardization, strong security guarantees, and suitability for hardware acceleration. Specifically, a unified three-stage pipelined butterfly unit is designed using a single DSP48E1 block for the required integer multiplication. In contrast, the modular reduction stage is implemented using a four-stage pipelined, lookup-table (LUT)-only Barrett reduction unit. The term “LUT-only” refers strictly to the reduction logic and not to the butterfly multiplication. Furthermore, two dual-port BRAM18 blocks are used in a ping-pong manner to hold intermediate and final coefficients. In addition, a simple finite-state machine controller is implemented, which manages all forward NTT (FNTT) and inverse NTT (INTT) stages. For validation, the proposed design is realized on a Xilinx Artix-7 FPGA. It uses only 503 LUTs, 545 flip-flops, 1 DSP48E1 block, and 2 BRAM18 blocks. The complete FNTT and INTT with final rescaling require 1029 and 1285 clock cycles, respectively. At 200 MHz, these correspond to execution times of 5.14 µs for the FNTT and 6.42 µs for the INTT.