Miracle-dz/UniFAR-FTUs huggingface dataset:
State Preserving Dynamic DRAM Bank Re-Configurations for Enhanced Power Efficiency

Power efficiency is one of the grand challenge problems facing computer architecture in recent years. Driven by the growth towards green computing, it is imperative to design architectures that can provide maximum power savings while incurring minimal overhead on real estate and performance. Towards this we propose a state-preserving mechanism for dynamically configuring DRAM banks based on utilization. We propose a novel mechanism via which it is possible to dynamically power down and power on banks while taking the bank utilization and overall performance into account. Over extensive experimentation using memory intensive applications, we can observe a power savings of up to 12.31% while incurring an average performance loss of 0.82% over baseline executions.
Video-based Parent-Child Relationship Prediction

In this paper, we investigate the problem of video-based parent-child relationship prediction via human face analysis. Most existing kinship verification methods predict the parent-child relationship from single images, which cannot effectively utilize videos of human faces for kinship verification. Recently, there have been a few methods for parent-child relationship prediction based on face videos, but all of them only perform pairwise comparisons between human faces between a single parent and a single child. Thus, they cannot effectively combine information about both the father’s and the mother’s faces when judging the kin relationship. In this paper, we propose a new dtaaset, Familyship Face Videos in the Wild (FFVW), which was captured both in wild conditions and standard reference, to deal with this issue. The inputs of FFVW are three separate videos of a family. To our best knowledge, our paper is the first attempt at addressing this problem. In our pre-processing step, we extract four key frames from each video, before doing facial recognition and alignment. Finally, we use a convolutional neural network to make the prediction. Overall, the effectiveness of this approach is verified by experimental results, which show that our dataset outperforms previous approaches to parent-child relationship prediction.
Integrated Approach for Heat Envelope Identification and Energy Efficiency Analysis in Buildings Using Drone Thermal Imagery and Deep Learning Techniques

In this paper, we present an automated approach that leverages deep learning techniques and drone thermal imagery to quantify heat envelopes and estimate the total heat loss over a designated period. The proposed approach aims to significantly accelerate the assessment of multiple buildings to a short timeframe compared to manual energy auditing. An infrared (IR) camera-equipped drone is deployed to capture high-resolution thermal and visible band images of buildings. A model based on darknet deep learning (DL) framework, you only look once (YOLO), was developed. This model processes the high-resolution thermal images, extracts feature maps, and identifies the heat envelopes in the buildings. Additionally, a user-friendly application to extract temperature values at points of interest was created. By utilizing the extracted temperature data, we compute an estimate of the total heat loss. This automated approach provides valuable insights and a deeper understanding of energy consumption, enabling more informed decision-making in energy management.
Improved Time-Saving Face Detection in Video

Human face detection from video sequences is a difficult problem in computer vision. Face detection is the process of determining the location of a face or faces in each frame of a video. Face detection in real-time videos is an ongoing under-investigated research problem. This paper proposes a new method for face detection in videos that significantly reduces the time required to detect faces. The method targets a Search Area Boundary (SAB) in the next frames that are determined based on the position of the detected faces in the current frame. The model will look for the face in a box that is slightly larger than the SAB from the previous frame. This means that it will not search the entire video frame for the face, so it will take less time to find the face. After a few frames, the method will begin searching the entire frame for new faces. The results show that the proposed method can save up to 73.5% of the time, especially when the SAB is small, with an accuracy degradation of no more than 7% in the best case.
Implementation of Chatbot for ITSM Application Using IBM Watson

In the current scenario, the end user of Information Technology Service Management (ITSM) application in software companies has to keep on searching the solution for problems he is facing or else finally generate a ticket since he cannot collaborate with the system by asking questions and getting relevant answers. As a solution to this, we aim to design a chatbot that will be specifically tailored for software firm employees. The chatbot can process input using Natural Language Processing (NLP) and can generate a relevant response which will help the end user to solve his query. The chatbot makes decisions itself to answer user's query with the help of the IBM Watson Conversation APIs. It will also remember the context of the conversation and perform tasks such as creation of ticket on behalf of the user.
Guideline for Thoracolumbar Pedicle Screw Placement Assisted by Orthopaedic Surgical Robot

The pedicle screw placement procedure is the most commonly used technique for spinal fixation and can provide reliable three‐column stabilization. Accurate screw placement is necessary in clinical practice. To avoid screw malposition, which may decrease the stiffness of the screw‐rod construct or increase the likelihood of neural and vascular injuries, the surgeons must fully understand the regional anatomy. Deformities, such as scoliosis, kyphosis or congenital anomalies, may complicate the application of the pedicle screw placement technique and increase the chance of screw encroachments. Incidences of pedicle screw malposition vary in different districts and hospitals and with surgeons and techniques. Today, the minimally invasive spinal surgery is well developed. However, the narrow corridors and limited views for surgeons increase the difficulty of pedicle screw placement and the possibility of screw encroachment. Evidenced by previous studies, robotic surgery can provide accurate screw placement, especially in settings of spinal deformities, anatomical anomalies, and minimally invasive procedures. Based on the consensus of consultant specialists, the literature review and our local experiences, this guideline introduces the robotic system and describes the workflow of robot‐assisted procedures and the precautions to take during procedures. This guideline aims to outline a standardized method for robotic surgery for thoracolumbar pedicle screw placement.
VBBMS: A Novel Buffer Management Strategy for NAND Flash Storage Devices

NAND flash storage devices are gaining popularity in consumer electronic devices, such as digital cameras, tablet PCs, and smartphones, due to their attractive features. However, write operations significantly limit the performance of the flash storage in consumer electronics devices due to their long latency and heavy overhead of garbage collection. Hence, NAND flash storage devices generally use RAM memory as a buffer to improve their write performance and to extend their lifetime. This paper proposes a novel buffer management algorithm (BMA), called as virtual-block-based buffer management scheme (VBBMS), which can make full use of both temporal and spatial localities at virtual-block level. The main difference with the existing BMAs is that the management granularity of VBBMS is a virtual block, while the management granularity of existing BMAs is a page or a block. In addition, VBBMS divides RAM into random request service region (RRSR), which is responsible for handling random requests, and sequential request service region (SRSR), which is responsible for handling sequential requests. When replacement happens, VBBMS selects victim virtual-block according to LRU policy in RRSR and first-in first-out (FIFO) policy in SRSR. VBBMS has been extensively evaluated under various realistic workloads, and our benchmark results show that VBBMS can improve buffer hit ratio, reduce response time, and erase times, compared to five existing BMAs.
Ultra-High Q Self-Ovenized AlScN-on-Si X-Lamé Resonator for Stable Clock Generation

This paper presents an aluminum-scandium nitride on silicon (AlScN-on-Si) bulk acoustic wave resonator with ultra-high Q and quadratic temperature characteristic of frequency. For the first time, the piezoelectrically transduced resonator provides an f × Q approaching the phononic limit in Si, while providing higher coupling and bias-free operation that enable surpassing electrostatic counterparts for realization of stable clocks. The AlScN-on-Si resonator operates in a cross-sectional Lamé mode at 64.4 MHz, with a Q of 142,012 (f × Q ≈ 0.92 × 1013). Benefiting from doped-Si and shear nature of the mode, a temperature-stable frequency is measured with a turn-over (TTO) at ~47°C. Further, the resonator benefits from a novel self-ovenization approach based on Joule heating of attached phononic bandgap tethers, enabling stabilization of frequency within 0.55 part-per-billion drift over -20°C to 40°C. The resonator is used to implement an oscillator, showing three orders of magnitude reduction in Allan Deviation when applying self-ovenization for stabilization.
APG-RRT: Sampling-Based Path Planning Method for Small Autonomous Vehicle in Closed Scenarios

To address the shortcomings of the classical RRT (Rapidly exploring Range Tree) path planning algorithm, such as long planning time and path curvature in some narrow and complex environments, an improved APG-RRT (Adaptive Path Guide RRT) algorithm is proposed. First, a guiding path is introduced in the sampling stage, and a node on the preset guiding path is selected first to expand the node tree so as to guide the algorithm to plan the exploration process. Secondly, the selection weight of the loading guidance path is dynamically adjusted according to the probability of collision between obstacles during the exploration process, and a safe and feasible path point is generated in the path expansion stage by combining the expansion information of the obstacles. Finally, in the path post-processing stage, combined with the vehicle kinematic constraints, the triangular inequality method is used to remove redundant path points, making the path more smooth, so as to fulfill the specific operational needs of the vehicle. The results of the simulation experiment demonstrate that the suggested method exhibits superior planning efficiency compared to the previous algorithm, resulting in a higher quality final path. At the same time, the verification of the algorithm’s feasibility and effectiveness is conducted through real car testing.
Identification of Factors Associated With Variation in US County-Level Obesity Prevalence Rates Using Epidemiologic vs Machine Learning Models

Key Points Question Which factors are associated with county-level variation in obesity prevalence, and how can they be identified using epidemiologic and machine learning methods? Findings This cross-sectional study of 3138 US counties found significant county-level variation in obesity prevalence, with US Census region, median household income, and percentage of population with some college education being most strongly associated with obesity prevalence. Machine learning models explain two-thirds more variation in obesity but were less interpretable than multivariate linear regression models. Meaning Machine learning models of county-level demographic, socioeconomic, health care, and environmental factors explain significantly more variation in obesity prevalence while being less interpretable.
Measurement of human body blocking for inter-WBAN interference in user-dense environments using 2 by 2 elements patch array antenna at 15GHz

Wireless body area network (WBAN) has inter-WBAN interference problem in a user-dense environments. The WBAN using directional antennas can suppress inter-WBAN interference as well as gain intra-WBAN communication signal power. In this paper, we measure human body blocking using fabricated antennas in user-dense condition at user density of 1.0 person per square meter and random user locations in the measurement area. From the result, the interference power that varies with user location and antenna directivity is confirmed.
Research for Parameters Optimization of Echo State Network

The artificial intelligence method represented by neural network is widely used in the field of nonlinear system estimation. In order to solve the problems that the hidden nodes of neural network are difficult to determine and the calculation is complex, Jaeger proposed an echo state network model for nonlinear system prediction. Aiming at the difficulty of multi-parameter selection of echo state network for nonlinear system prediction, the genetic algorithm and cross-validation method are used to optimize the network parameters. The simulation show that the genetic algorithm optimizes parameters of the echo state network to prediction accuracy is better than the cross-validation. The cross-validation is superior to the genetic algorithm in optimizing efficiency.
Deep Style Transfer for Generation of Photo-realistic Synthetic Images of CNT Forests

Carbon nanotubes (CNTs) are promising nano-materials with diverse applications in various fields, ranging from electronics and energy storage to biomedical applications. Characterization of CNT forest structures and prediction of material properties through image analytics are critical for new material design and discovery. Artificial intelligence and machine learning (AI/ML) driven approaches offer promising solutions towards these goals. These data-driven approaches rely on large amounts of annotated data for robust training. However, growing, imaging, and annotation of CNT forests are complex, time consuming, expensive processes limiting availability of such data. In this paper, we propose a novel neural style transfer pipeline to generate photo-realistic and structure-aware synthetic images of CNT forests by imposing scanning electron microscopy (SEM) like appearance to physics-based simulation results. The proposed pipeline relies on a novel wavelet-based style transfer network and a selective decision fusion mechanism that allows better preservation of the thin curvilinear structures of CNTs, particularly their intricate details and connectivity, without introducing unwanted artifacts. Experimental results and quantitative analysis demonstrate refined style transfer that generates photo-realistic images that can be used to train AI/ML- driven CNT forest image analysis systems.
Study of a Single-Input Single-Output Sensing Scheme for Three Analytes Via Nonlinear Mode Localization

In this paper, we propose a single-input single-output sensing scheme for three analytes via nonlinear mode localization that is experimentally verified using a coupled triple cantilever array. By introducing electrostatic nonlinear breaking of structural symmetry, this scheme can distinguish the two analytes absorbed on the hanging wall on both sides. Single input and single output can be realized by electrostatically driving one side cantilever and piezoelectrically outputting the response of the other side cantilever, and greatly simplifying the structure. In addition, driving one side cantilever to the nonlinear resonance area can ensure high resolution. Finally, the sensing error is greatly reduced by decoupling the complex relationship between multiple modes and multiple analytes. This work not only broadens the theoretical content of modal localization, but also opens new approaches for quantitative detection of multiple analytes.
Understanding Customer Experiences Through Social Media Analysis of Three Giants of Soft Drink Industry

On social media websites, there is a huge amount of data that is available for free and can be used in a variety of ways. For the case of competitive intelligence, there are a variety of ways like analysis of user-generated content as well as analysis of competitor's data through text mining, sentiment analysis and quantitative analysis. This case study applies a verified framework based on text mining and sentiment analysis on this problem to analyze the twitter content of three giants of soft drink industry namely Coca Cola, Pepsi and Fanta. There has been a deep relationship found among number of tweets, nature of tweets and the customer engagement recorded respectively. The potential implications and suggestions have been discussed for the companies to better develop their social media competitive intelligence model.
Detection of Pitch Frequency of Indian Classical Music Based on Hilbert-Huang Transform for Automatic Note Transcription

The pitch detection is an integral element of automatic music transcription system. Empirical Mode Decomposition (EMD) technique plays a key part in the pitch detection. With this technique, any complicated data set comprising of frequency-amplitude points can be decomposed into small number of finite Intrinsic Mode Functions (IMF). The IMF logic is in accordance with a well-behaved and well proven Hilbert transform. In this paper, a step by step algorithm for detecting pitch period from classical music signal based on Hilbert-Huang transform (HHT) is proposed. Traditional windowing methods have two limitations namely overlapping of windows and an assumption of stationary pitch period within a window. In contrast, HHT shows no limitations on window selection and allows pitch period changing within windows. It also can be used to monitor the variation of the pitch. To validate the proposed method, the pure tone of standard pitch is used. The results show that the variation of the pitch period can be accurately detected. This demonstrates the successful application of Hilbert-Huang transform for pitch detection from Indian classical music signal.
$H_{\infty}$ Static Output-Feedback Control Design for Discrete-Time Systems Using Reinforcement Learning

This paper provides necessary and sufficient conditions for the existence of the static output-feedback (OPFB) solution to the <inline-formula> <tex-math notation="LaTeX">$H_{\infty } $ </tex-math></inline-formula> control problem for linear discrete-time systems. It is shown that the solution of the static OPFB <inline-formula> <tex-math notation="LaTeX">$H_{\infty } $ </tex-math></inline-formula> control is a Nash equilibrium point. Furthermore, a Q-learning algorithm is developed to find the <inline-formula> <tex-math notation="LaTeX">$H_{\infty } $ </tex-math></inline-formula> OPFB solution online using data measured along the system trajectories and without knowing the system matrices. This is achieved by solving a game algebraic Riccati equation online and using the measured data. A simulation example shows the effectiveness of the proposed method.
Cephalometric studies of the mandible, its masticatory muscles and vasculature of growing Göttingen Minipigs—A comparative anatomical study to refine experimental mandibular surgery

Over many decades, the Göttingen Minipig has been used as a large animal model in experimental surgical research of the mandible. Recently several authors have raised concerns over the use of the Göttingen Minipig in this research area, observing problems with post-operative wound healing and loosening implants. To reduce these complications during and after surgery and to improve animal welfare in mandibular surgery research, the present study elucidated how comparable the mandible of minipigs is to that of humans and whether these complications could be caused by specific anatomical characteristics of the minipigs’ mandible, its masticatory muscles and associated vasculature. Twenty-two mandibular cephalometric parameters were measured on CT scans of Göttingen Minipigs aged between 12 and 21 months. Ultimately, we compared this data with human data reported in the scientific literature. In addition, image segmentation was used to determine the masticatory muscle morphology and the configuration of the mandibular blood vessels. Compared to data of humans, significant differences in the mandibular anatomy of minipigs were found. Of the 22 parameters measured only four were found to be highly comparable, whilst the others were not. The 3D examinations of the minipigs vasculature showed a very prominent deep facial vein directly medial to the mandibular ramus and potentially interfering with the sectional plane of mandibular distraction osteogenesis. Damage to this vessel could result in inaccessible bleeding. The findings of this study suggest that Göttingen Minipigs are not ideal animal models for experimental mandibular surgery research. Nevertheless if these minipigs are used the authors recommend that radiographic techniques, such as computed tomography, be used in the specific planning procedures for the mandibular surgical experiments. In addition, it is advisable to choose suitable age groups and customize implants based on the mandibular dimensions reported in this study.
Image to Text: Comprehensive Review on Deep Learning Based Unsupervised Image Captioning

The task of image captioning has seen considerable success using deep neural networks. This assessment offers a thorough overview of the most cutting-edge approaches for deep learning-based unsupervised image captioning. In order to bridge the gap between computer vision and natural language processing, the effort of creating meaningful textual captions for images is known as image captioning. Without using annotated image-caption pairings for training, unsupervised approaches try to provide captions. The review examines a variety of techniques, including generative adversarial networks (GANs), pre-training, transfer learning, and convolutional neural networks (CNNs) for extracting visual features, as well as recurrent neural networks (RNNs) or transformer models for language modelling. Researchers and practitioners can benefit from this survey's observations, classification of methodologies, and recommendations for the future of deep learning-based unsupervised image captioning.
Modified Butterworth-Van Dyke Circuit Model for the Lithium Niobate Piezoelectric Acoustically Excited Antenna with Electrode Effects

Based on the mechanism of acoustic resonance, the piezoelectric acoustically excited antenna (PAEA) has recently received much interest as a newly miniaturized low-frequency (LF) antenna. The electrode effects on the performance of the lithium niobate PAEA were studied for the first time in this paper, and the equivalent circuit analysis was subsequently completed. All of the findings indicate that the modified Butterworth-Van Dyke (MBVD) circuit model can adequately demonstrate the resonant characteristics of PAEA while taking the electrode effects into account and that it is not essential to infer by the intricate finite element method (FEM).
Design of E-Shaped Microstrip Filter-Antenna for 4G-LTE Applications

A design of a filter-antenna for 1.8 GHz 4G-LTE has been proposed. The filter-antenna configuration adopts an E-shaped microstrip antenna and square open-loop bandpass filter that utilize coupled line as the integration technique. The integration of antenna and filter demonstrated a resonant frequency at 1.8GHz with 9MHz bandwidth with 4.5dBi gain.