Search

Despite its excellent performance in microelectronic industry, silicon was not able to perform well in photonic devices arena. This is because the silicon has never been a good optical source mainly due to its indirect band gap structure. Many of the device functionalities in silicon have been reported, with an exception of, until recently, a reliable optical source. Silicon is a nonlinear material which makes use of its nonlinearities to realize various functionalities. This paper presents a theoretical treatment of generating and enhancing third-harmonic field which may be used as optical source, crystal state monitoring and all-optical signal processing applications., Comment: 6 pages, 3 figures, International Workshop on Information and Electronics Engineering (IWIEE) - 2012

Automated fetal brain extraction from full-uterus MRI is a challenging task due to variable head sizes, orientations, complex anatomy, and prevalent artifacts. While deep-learning (DL) models trained on synthetic images have been successful in adult brain extraction, adapting these networks for fetal MRI is difficult due to the sparsity of labeled data, leading to increased false-positive predictions. To address this challenge, we propose a test-time strategy that reduces false positives in networks trained on sparse, synthetic labels. The approach uses a breadth-fine search (BFS) to identify a subvolume likely to contain the fetal brain, followed by a deep-focused sliding window (DFS) search to refine the extraction, pooling predictions to minimize false positives. We train models at different window sizes using synthetic images derived from a small number of fetal brain label maps, augmented with random geometric shapes. Each model is trained on diverse head positions and scales, including cases with partial or no brain tissue. Our framework matches state-of-the-art brain extraction methods on clinical HASTE scans of third-trimester fetuses and exceeds them by up to 5\% in terms of Dice in the second trimester as well as EPI scans across both trimesters. Our results demonstrate the utility of a sliding-window approach and combining predictions from several models trained on synthetic images, for improving brain-extraction accuracy by progressively refining regions of interest and minimizing the risk of missing brain mask slices or misidentifying other tissues as brain.

It is acknowledged that the most common cause of dementia worldwide is Alzheimer's disease (AD). This condition progresses in severity from mild to severe and interferes with people's everyday routines. Early diagnosis plays a critical role in patient care and clinical trials. Convolutional neural networks (CNN) are used to create a framework for identifying specific disease features from MRI scans Classification of dementia involves approaches such as medical history review, neuropsychological tests, and magnetic resonance imaging (MRI). However, the image dataset obtained from Kaggle faces a significant issue of class imbalance, which requires equal distribution of samples from each class to address. In this article, to address this imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is utilized. Furthermore, a pre-trained convolutional neural network has been applied to the DEMNET dementia network to extract key features from AD images. The proposed model achieved an impressive accuracy of 98.67%., Comment: 7 pages, 3 figures, Presented at the 20-th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP) 21-22 February, 2024, Mazandaran University of Science and Technology, Babol, Iran

In metal Additive Manufacturing (AM), monitoring the temperature of the Melt Pool (MP) is crucial for ensuring part quality, process stability, defect prevention, and overall process optimization. Traditional methods, are slow to converge and require extensive manual effort to translate data into actionable insights, rendering them impractical for real-time monitoring and control. To address this challenge, we propose an Artificial Intelligence (AI)-based solution aimed at reducing manual data processing reliance and improving the efficiency of transitioning from data to insight. In our study, we utilize a dataset comprising dual-wavelength real-time process monitoring data and corresponding temperature maps. We introduce a deep learning model called the "Binocular model," which exploits dual input observations to perform a precise analysis of MP temperature in Laser Powder Bed Fusion (L-PBF). Through advanced deep learning techniques, we seamlessly convert raw data into temperature maps, significantly streamlining the process and enabling batch processing at a rate of up to 750 frames per second, approximately 1000 times faster than conventional methods. Our Binocular model achieves high accuracy in temperature estimation, evidenced by a 0.95 R-squared score, while simultaneously enhancing processing efficiency by a factor of $\sim1000x$ times. This model directly addresses the challenge of real-time MP temperature monitoring and offers insights into the encountered constraints and the benefits of our Deep Learning-based approach. By combining efficiency and precision, our work contributes to the advancement of temperature monitoring in L-PBF, thus driving progress in the field of metal AM.

Quantum networks and distributed quantum computers rely on entanglement generation between photons and long-lived quantum memories. For large-scale architectures, one of the most crucial parameters is the efficiency at which entanglement can be created and detected. Here, we maximize the efficiency for the detection of hybrid entanglement between a nuclear spin qubit in diamond with a photonic time-bin qubit. Our approach relies on an optimal implementation of the photonic qubit analyzer, for which we use a high-speed electro-optic deflector to direct photons deterministically along the optimal interferometer paths. This way, we completely eliminate all cases in which photons are randomly lost due to the propagation in the wrong interferometer path. In this first demonstration experiment, we use nitrogen-vacancy center in diamond, for which we immediately demonstrate the presence of the entanglement. An extension to other spin-photon entanglement systems is straightforward. Further, our scheme can be used in the framework of quantum repeater networks, including spectral and temporal multiplexing strategies. Our results thus pave the way for the future high-performance quantum networks.

The generation of controlled and arbitrarily tunable terahertz radiation, essential for many applications, has proven challenging due to the complexity of experimental setups and fabrication techniques. We introduce a new strategy involving control over a terahertz repetition rate integrated frequency comb, using Kerr-induced synchronization, that results in a terahertz-voltage-controlled oscillator. By modulating the reference laser, we can transfer any microwave waveform onto the microcomb repetition rate via a linear transfer function based on optical frequency division. The resulting frequency comb with a terahertz carrier can be created using integrated components, with a bandwidth constrained only by the synchronization bandwidth and high coherence resulting from the low-noise soliton microcomb in the Kerr-induced synchronized state.

Zinc sulfide ZnS thin films with variable structural, optical, electrical, and thermoelectric properties were obtained by changing the source to substrate SSD distance in the physical vaporthermal coating PVTC system. The films crystallized into a zinc blede cubic structure with 111 preferred orientation.

InGaN quantum wells (QWs) grown on c-plane sapphire substrate experience strain due to the lattice mismatch. The strain generates a strong piezoelectric field in QWs that contributes to THz emission under ultrafast excitation. Physical parameters such as QW width, period number, and Indium concentration can affect the strength of the piezoelectric field and result in THz emission. Experimental parameters such as pump fluence, laser energy, excitation power, pump polarization angle, and incident angle can be tuned to further optimize the THz emission. This review summarizes the effects of physical and experimental parameters of THz emission on InGaN QWs. Comparison and relationship between photoluminescence properties and THz emission in QWs are given, which further explains the origin of THz emission in InGaN QWs.

In the era of Industry 4.0, Additive Manufacturing (AM), particularly metal AM, has emerged as a significant contributor due to its innovative and cost-effective approach to fabricate highly intricate geometries. Despite its potential, this industry still lacks real-time capable process monitoring algorithms. Recent advancements in this field suggest that Melt Pool (MP) signatures during the fabrication process contain crucial information about process dynamics and quality. To obtain this information, various sensory approaches, such as high-speed cameras-based vision modules are employed for online fabrication monitoring. However, many conventional in-depth analyses still cannot process all the recorded data simultaneously. Although conventional Image Processing (ImP) solutions provide a targeted tunable approach, they pose a trade-off between convergence certainty and convergence speed. As a result, conventional methods are not suitable for a dynamically changing application like MP monitoring. Therefore, this article proposes the implementation of a Tunable Deep Image Processing (TDIP) method to address the data-rich monitoring needs in real-time. The proposed model is first trained to replicate an ImP algorithm with tunable features and methodology. The TDIP model is then further improved to account for MP geometries and fabrication quality based on the vision input and process parameters. The TDIP model achieved over 94% estimation accuracy with more than 96% R2 score for quality, geometry, and MP signature estimation and isolation. The TDIP model can process 500 images per second, while conventional methods taking a few minutes per image. This significant processing time reduction enables the integration of vision-based monitoring in real-time for processes and quality estimation.

Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to poor performance in providing low latency services. Edge-to-cloud computing overcomes the limitations of end-devices by offloading their computations to nearby edge devices or remote cloud servers. Although this proves to be sufficient for many applications, optimal placement of latency sensitive AR/VR services in edge-to-cloud infrastructures (to provide desirable service response times and reliability) remain a formidable challenging. To address this challenge, this paper develops a Multi-Objective Genetic Algorithm (MOGA) to optimize the placement of AR/VR-based services in multi-tier edge-to-cloud environments. The primary objective of the proposed MOGA is to minimize the response time of all running services, while maximizing the reliability of the underlying system from both software and hardware perspectives. To evaluate its performance, we mathematically modeled all components and developed a tailor-made simulator to assess its effectiveness on various scales. MOGA was compared with several heuristics to prove that intuitive solutions, which are usually assumed sufficient, are not efficient enough for the stated problem. The experimental results indicated that MOGA can significantly reduce the response time of deployed services by an average of 67\% on different scales, compared to other heuristic methods. MOGA also ensures reliability of the 97\% infrastructure (hardware) and 95\% services (software).

Kerr-induced synchronization (KIS) [1] involves the capture of a dissipative Kerr soliton (DKS) microcomb [2] tooth by a reference laser injected into the DKS resonator. This phase-locking behavior is described by an Adler equation whose analogous form describes numerous other physical systems [3], such as Josephson junctions [4]. We present an AC version of KIS whose behavior is similar to microwave-driven Josephson junctions, where periodic synchronization occurs as so-called Shapiro steps. We demonstrate consistent results in the AC-KIS dynamics predicted by the Adler model, Lugiato-Lefever equation, and experimental data from a chip-integrated microresonator system. The (integer) Shapiro steps in KIS can simply be explained as the sideband created through the reference laser phase modulation triggering the synchronization. Notably, our optical system allows for easy tuning of the Adler damping parameter, enabling the further observation of fractional-Shapiro steps, where the synchronization happens at a fraction of the driving microwave frequency. Here, we show that the comb tooth is indirectly captured thanks to a four-wave mixing Bragg-scattering process, leading to sub-comb mode-locking, and we demonstrate this experimentally through noise considerations. Our work opens the door to the study of synchronization phenomena in the context of microresonator frequency combs, synthesis of condensed-matter state analogues with DKSs, and the use of the fractional Shapiro steps for flexible and tunable access to the KIS regime.

Large Language Models (LLMs) generate responses to questions; however, their effectiveness is often hindered by sub-optimal quality of answers and occasional failures to provide accurate responses to questions. To address these challenges, a fine-tuning process is employed, involving feedback and examples to refine models. The objective is to enhance AI models through continuous feedback loops, utilizing metrics such as cosine similarity, LLM evaluation and Rouge-L scores to evaluate the models. Leveraging LLMs like GPT-3.5, GPT4ALL, and LLaMA2, and Claude, this approach is benchmarked on financial datasets, including the FinanceBench and RAG Instruct Benchmark Tester Dataset, illustrating the necessity of fine-tuning. The results showcase the capability of fine-tuned models to surpass the accuracy of zero-shot LLMs, providing superior question and answering capabilities. Notably, the combination of fine-tuning the LLM with a process known as Retrieval Augmented Generation (RAG) proves to generate responses with improved accuracy.