Real-time people counting and indoor positioning are essential features for energy-aware and privacy-preserving smart environments. However, achieving low-latency inference under strict power and bandwidth constraints remains challenging, particularly with conventional high-resolution RGB or depth-based vision systems. In this work, we present a fully co-optimized sensing and inference pipeline that combines a low-resolution thermopile infrared sensor with a hardware-tailored detection architecture. A 3232 passive infrared array is used to collect a custom indoor dataset, and a compact pre-processing chain reduces frame size by over 80% while preserving silhouette information. The resulting binary inputs are processed by a quantization- and pruning-aware YOLOv3-tiny detector, achieving accurate and compact representation suitable for constrained edge devices. To support deployment on In-Memory Accelerators (IMC), we experimentally characterize multi-level cell (MLC) behavior in a fabricated 28 nm FeFET array, including programming voltage characteristics and memory window analysis. Controlled multi-level current accumulation is further validated at crossbar array level, confirming robust analog dot-product behavior under realistic operating conditions. The proposed pipeline demonstrates a holistic approach to embedded perception, spanning sensor data capture, model compression, and experimentally validated non-volatile analog hardware acceleration.
Multiferroic domain walls in functional oxides exhibit properties distinct from the bulk and are increasingly exploited as active elements in nanoelectronic and photonic devices. Deterministic control of domain populations has typically remained limited to local control, or removal with temperature. Here we demonstrate continuous, reversible manipulation of the ferroelastic domain structure in single-crystal LaAlO_3 using in-situ uniaxial strain. Combining atomic force microscopy, X-ray diffraction, and Raman spectroscopy with first-principles calculations we map the complete microscopic evolution of the twin domain population through the strain-driven transition from the rhombohedral R3̅c ground state toward the predicted orthorhombic Fmmm phase. Applied strains below 0.5% produce pronounced surface flattening and large-scale domain reorganisation, establishing uniaxial strain as a technically accessible control parameter for ferroelastic domain engineering. These results open a route to active, real-time programming of domain architectures in LaAlO_3-based heterostructures, with implications for strain-tunable superconducting interfaces, nanoscale phonon-polariton optics, and ultrafast lattice control.
Because of hardly known film properties, precursor savings and hardware modifications of the atomic layer deposition (ALD) equipment, the bis(diethylamino)silane (SiBDEA)/ozone-oxygen (O3/O2) silicon oxide (SiO2) process requires re-optimization. Using the design of experiments (DoEs), the old double pulse (ODP) process was transformed into a new single pulse (NSP) process for SiO2 deposition. This NSP process achieves film properties comparable to the ODP process, while reducing precursor consumption by 50%. However, different from previous works, no ALD window was observed across the temperature range of 150 C-degrees-300( degrees)C. The coverage ability on high aspect ratio (HAR) structures (1:32) was also demonstrated. It was determined that at least 50 cycles are necessary for effective protection using the NSP process, which corresponds to a thickness of approximately 4.5 nm. Keeping the precursor saving from NSP process, while enhancing film properties, we investigate another new double pulse (NDP) process. The measurements indicate that the NDP process improves film properties like homogeneity and optical performance.
5G+ (5G and beyond) mobile networks are increasingly complex, making their monitoring and management challenging. This paper presents a PhD research focused on automating 5G+ network performance assessment through a family of Multimodal Data Analysis Methods (MDAM) in 5G+ cellular networks. We identify limitations of unimodal approaches, describe the design-science methodology adopted, and present the Module for Automated and Context-Preserving Telecommunication Network State Analysis (MACNSA) – a modular analytics platform hosting MDAM. The first MDAM implemented within MACNSA is an agentic RAG-based system (Agentic-RAG-MDAM) that fuses numerical KPI data with textual feature documentation. The evaluation protocol, based on both traditional information retrieval and LLM-as-a-judge metrics, is described. Preliminary experiments on real operator data demonstrate that Agentic-RAG-MDAM significantly reduces per-feature analysis time compared to traditional human expert approaches. Current limitations and planned future work are discussed.
Counter Unmanned Aerial Systems (C-UAS) is an emerging area of active research and development especially since the Ukraine war. A parallel stream of research and development is also taking place addressing the defensive survivability of friendly unmanned platforms against hostile C-UAS threats. While extensive literature addresses offensive C-UAS capabilities, the complementary challenge of defending autonomous platforms remains profoundly underdeveloped. We call this research and innovation direction Counter-Counter Unmanned Aerial Systems ($\mathbf{C}^{\mathbf{2}}$-UAS). In this paper we present aspects and directions in radio $\mathbf{C}^{\mathbf{2}}$-UAS domains. For $\mathbf{R F}$ systems, we address radar crosssection reduction through phase centre manipulation, geometric shaping and advanced materials. We also discuss communications link resilience against jamming through anti-jamming techniques and low probability of intercept/detection (LPI/LPD) waveform design, and navigation resilience through Global Positioning Systems (GPS) anti-spoofing mechanisms and multi-constellation Global Navigation Satellite System (GNSS) redundancy. We identify critical research gaps, emerging technologies, and future research directions especially by a brain-inspired architecture to foster $\mathbf{C}^{\mathbf{2}}$-UAS. This work establishes the theoretical and practical foundations for $\mathrm{C}^{2}$-UAS as a distinct research discipline with profound strategic importance.
