Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved YOLOv5 for Transformer Nameplate Text Detection

Aiming at the characteristics of dense text and large font size span of transformer nameplate, a text detection algorithm of transformer nameplate improved YOLOv5 is proposed. Efficient Channel Attention is added to the C3 module of backbone to suppress the interference of a large amount of background information generated by multiple convolutions to the target information, and to enhance the focus on text targets during feature extraction; The feature map after Neck feature fusion uses the Coordinate Attention to update the weight. It obtains global information and is more sensitive to text targets; Introducing SIoU_Loss replaces CIoU_Loss to accelerate algorithm convergence. The feasibility was verified on the ICDAR 2013 dataset. The results show that YOLOv5 has better performance than some classical algorithms; The experiment is conducted on the self-made dataset. It shows that the improved algorithm mAP0.5 has reached 0.88,091, Precision has reached 0.97757, and mAP0.5: 0.95 has increased by 3.3 percentage points compared with the original algorithm, achieving excellent performance.
A Literature Review on Citrus Fruits and Leaves diseases detection using Deep Neural Network model

In Agriculture, decline in the yield of citrus fruits is mainly caused by diseases occurring in citrus fruits and leaves. Early detection of diseases involves usage of deep learning strategies by designing an automated detection system with a convolutional neural network (CNN) model. The diseased citrus fruits and leaves of Scab, Black Spot, Canker, Melanose, and Greening are separated from healthy leaves by combining and fusing several layers. The main purpose is to build a model which identifies the disease and allocates the corresponding disease class to the image. Deep learning classifiers uses many layers with optimal parameter set whereas classical feature representation methods are employed by Machine Learning classifiers to classify images.
Optimum Access-Point Constellation for Indoor Time Difference of Arrival Positioning

The availability of ubiquitous location services is a highly desirable feature for many upcoming applications in the realm of the Internet of Things (IoT), including asset tracking, navigation services especially in indoor environments such as shopping centers, or emergency people tracking in schools, hospitals, and prisons. While outdoor positioning is perfectly covered by Global Navigation Satellite Systems (GNSS), indoor positioning services, except for a few pioneer projects, did not hit the mainstream yet. At first, this is surprising, as the underlying technology has been known for years and solutions already exist based on WiFi or Bluetooth beacons. A major problem is, that the locations of those beacons need to be highly tailored to the specific indoor floorplan for high positioning accuracies, which directly contradicts the need for a cheap installment. Picking up this challenge, we present a methodology for fast optimization of access point locations in indoor environments, maximizing coverage and positioning accuracy that will facilitate a broad deployment of indoor positioning systems.
Calibration of Kodak fine grain positive and no‐screen film in the 4–52 keV x‐ray range

The response of Kodak fine grain positive and Kodak no‐screen (duplitized) film was measured as a function of x‐ray energy over the range of approximately 4–52 keV under a vacuum of 10−3−10−4 Torr. The x‐ray transmission properties were also determined. The calibrations were performed with fluorescence radiation excited by the bremsstrahlung from dc x‐ray machines. These two films can cover an exposure range of over three orders of magnitude. Some films, such as fine grain positive, exhibit an enhanced sensitivity if exposed under vacuum. Also, it was found that no‐screen film exhibits a sensitivity reversal at approximately 5 keV. Other films would be expected to show a similar response with the energy at which the reversal occurs determined by the characteristics of the emulsion.
Classification of Cell Diffraction Images Based on Deep Neural Network

With the continuous advancement of the life sciences and the advancement of ultra-high resolution technology, people can observe biological information such as organelles and molecules, and can understand their internal structure and interaction from the acquired cell diffraction images. However, cells contain various types of organelles, which have high heterogeneity, and the cell structures of different types of cells have certain differences. Therefore, the study of cell diffraction image classification is of great significance in many fields such as cell morphology and cell biology. The research task of image classification is to extract useful feature information from the image, and then distinguish the images of different attributes, and finally divide the image targets of different categories. Deep learning techniques are used in a variety of industries, including picture categorization, as a result of the development of deep learning. Among these, there has been a notable improvement in the accuracy of picture classification using deep neural networks. The classification accuracy can be further increased in the real cell diffraction image classification procedure, though. This study proposes a deep neural network-based classification strategy for cell diffraction pictures. For the cell diffraction images, some interference pictures are created by cell debris or impurities. In this work, the produced diffraction pictures are preprocessed using a clustering approach and a support vector machine (SVM). After that, a Gray Level Co-occurrence Matrix(GLCM) is used to extract the texture features of the diffraction image. This research proposes an enhanced deep neural network-based picture classification algorithm. Max-margin Minimum Classification Error (M3CE) is introduced during the training of deep neural networks, and cross-entropy is used to build the loss function. Finally, the Ramos and Jurkat cell experiments' findings support the high precision of the categorization approach.
IoT based Smart Cradle System

Certain cradles are created with Internet of things (IoT)’ integration, but there are still some features that could be added to monitor the health of babies. Parents need tomanage their professional work and also should take care of their children that increases the work load for the parent and effects their professional lives as well as their children’s lives. So here we are adopting a modern-day cradle system due to thelack of features in traditional cradle systems. The proposed system allows mothers to manage both their career and personal lives. There is a need to build a smart cradle that will allow parents to focus on their work and provides minimal protection to their infants. The proposed method produces a system that is both safe and efficient.
Beyond 5G Communication Networks Enabling Massive IOT

The future of humanity is dependent upon the IOT(Internet of things) and 5G network, which means most of the machine and mobile phones and devices are operating through the IOT(Internet of things) and to transfer the data from one port to another port through the 5G network and it is seen that it play an very crucial role in this area. To use this IOT(Internet of things) have to create an advanced intelligent environment into the world which is running through the human command, voice like example amazon ALEXA device, most of AC system which operated with the phone and audio. When a large number of the device is operated with some device then some large number interaction between the heterogonous device and increase the demand like strong connection, exceptionally high data throughput, low latency, creating applications that play critical roles in IOT(Internet of things), as a result of this need 5G technology is an important facilitator for IOT(Internet of things) technology. this work gives all the theoretical information about the IOT(Internet of things) and 5G technology, also give the detail that how the technology is changing day by day due to this technology and it is going to be large scale in future for every single child and old person, so this work helps all gathering information about Latest technologies and work in this field.
Deep Learning-based Plant Leaf Disease Detection

In this study, three deep learning models (CNN, VGG16, and VGG19) were compared for the objective was to detect plant diseases, and to accomplish this, a dataset comprising 9,127 images of plants annotated with disease labels was used to train and evaluate the model’s using accuracy, F1 score, recall, and precision as performance metrics. The results show that CNN achieved the highest overall performance in the classification task, with an accuracy of 0.97 and an F1 score of 0.95. However, the VGG16 and VGG19 models also demonstrated strong performance, with accuracies of 0.96 and 0.95, respectively. The use of deep learning for plant illness interpretation has many advantages, such as automatically extracting relevant features from the input images and scalability. However, there are also limitations such as overfitting, computational resources, and the need for high-quality annotated data. Additional investigation is necessary to assess the efficacy of deep learning models under different conditions and to develop methods for addressing these challenges.The research discusses the importance of evaluating models using multiple performance metrics to gain a comprehensive understanding of their capabilities. Additionally, it highlights potential areas for future research, including the use of transfer learning techniques, the evaluation of different deep learning architectures, and the use of additional performance metrics. The research further discusses the practical applications of deep learning models in image classification tasks, with a focus on plant leaf disease detection. The results of this research have the capacity to enhance the efficiency and efficacy of identifying and treating diseases in the agricultural sector. In general, this comparative analysis offers valuable perspectives on the functionality of deep learning models for image classification duties, with implications that extend to various sectors.
Research on the Adaptive OFDM Hydroacoustic Communication

Due to the complexity and variability of hydroacoustic channels with long delay multipath propagation, Doppler shift, spectral distortion and non-smoothness, adaptive techniques are used to efficiently use the hydroacoustic channel information to change some parameters of the transmit signal to adapt to the changing channel and thus improve the communication performance. The efficiency of underwater communication systems may be further enhanced by combining the high communication rate, frequency band usage, and multipath resistance of orthogonal frequency division multiplexing (OFDM) technology with multi-carrier wireless communication. We review the development process of adaptive techniques in hydroacoustic communication, and introduce AM based on channel information, power allocation adaptive techniques, joint adaptive bit, modulation and power allocation, combined with reinforcement learning adaptive techniques, respectively. And we analyze the research results of using them in recent years to solve the effects of rapidly changing multipath propagation, fast time-varying, and extended propagation delay in hydroacoustic communication. Our analysis concludes that a multi-carrier OFDM system using link-level adaptive techniques has greater flexibility and can achieve better system performance than a conventional single-carrier system. The basic parameters that can be adjusted include modulation, coding, transmit power, spread spectrum gain and signaling bandwidth. Inaccurate channel state information (CSI) can lead to incorrect modulation schemes, resulting in inefficient communication. Therefore, it is important to effectively obtain accurate CSI as the basis for efficient modulation selection, regardless of the AM method used.
Non-cooperative satellite pose estimation based on CPMs

The realization of accurate pose estimation of satellite targets has emerged as one of the research goals as human attention to space exploration has increased. In this paper, we suggest a more accurate estimation technique for non-satellite targets in space. We use the pose machine to extract the key points of the image to form an initialized pose matrix using point pair relationships and initialize the point cloud alignment. First, we extract the pixel coordinates of significant spots from satellite pictures. And then, a rough estimation of the satellite pose at this precise moment is achieved by using the mapping relationship between the picture and real spatial points. To provide an accurate result, the final pose is refined using point cloud data. Our method provides a fast initial attitude input using image features, avoids subsequent point cloud iterative alignment from falling into local optimum, and resolves the satellite attitude ambiguity due to symmetry. Our results show that the method can obtain satellite poses accurately and quickly.
Industry 4.0: Developing Center of Excellence for PLC Lab in the field of Automation

Nowadays, industry 4.0 presents a modern and innovative paradigm with integrated up-to-date technology. The Asean Factori 4.0 projects under the funding of the Erasmus+ programme targeted to provide the training of the joined staff, students, and lecturers from the involved universities. This project contributes to the trainees for the state-of-the-art knowledge related to industry 4.0, automation, technologies, applications, and skills in utilizing the programable logic control (PLC) and its use cases for automation projects. This paper presents the key enabler technologies for industry 4.0 applications and developing Center of Excellence (CE) for PLC Lab in the fields of automation. Furthermore, we describe the learning courses and training contents under the training periods. The significant tools, frameworks, software, theoretical and practical methods are delivered to the involvements. Finally, a practical elevator project utilized the CE PLC Lab conducted by students have presented.
Hybrid Building Occupancy Estimation using Thermal Imaging and Environmental Sensing

Estimating building occupancy is a fundamental aspect of effective building management. By predicting building occupancy over time, we may reduce energy consumption, and contribute to energy efficiency in buildings. Building Occupancy Estimation (BOE) can be useful to compute building management metrics, such as the number of users in each room during work hours or in the entire building overnight. This paper presents a Hybrid Building Occupancy Estimation (HBOE) method that uses thermal imaging and environmental sensing to deliver a qualitative occupancy metric. Results show that the chosen approach represents a cost-effective solution, delivering an in- dicative BOE value based on thermal imaging and environmental sensing.
Optimal Rack-Coordinated Updates in Erasure-Coded Data Centers: Design and Analysis

Erasure coding has been extensively deployed in today's data centers to tackle prevalent failures, yet it is prone to substantial cross-rack traffic for parity updates. In this article, we propose a new rack-coordinated update mechanism to suppress the cross-rack update traffic, which comprises two successive phases: a delta-collecting phase that collects data delta chunks, and another selective parity update phase that renews the parity chunks based on the update pattern and parity layout. We further design <inline-formula><tex-math notation="LaTeX">${\sf RackCU}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">RackCU</mml:mi></mml:math><inline-graphic xlink:href="shen-ieq1-3234215.gif"/></alternatives></inline-formula>, an optimal rack-coordinated update solution that achieves the theoretical lower bound of the cross-rack update traffic. We also perform reliability analysis, demonstrating that <inline-formula><tex-math notation="LaTeX">${\sf RackCU}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">RackCU</mml:mi></mml:math><inline-graphic xlink:href="shen-ieq2-3234215.gif"/></alternatives></inline-formula> can attain a lower data loss probability via shortening the update procedure. We conduct extensive evaluations, in terms of large-scale simulation and real-world data center experiments. We show that <inline-formula><tex-math notation="LaTeX">${\sf RackCU}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">RackCU</mml:mi></mml:math><inline-graphic xlink:href="shen-ieq3-3234215.gif"/></alternatives></inline-formula> can reduce 16.5-77.1% of the cross-rack update traffic and hence improve 24.9-772.0% of the update throughput.
Machine and Deep learning in Biometric Authentication: A Review

Biometric authentication is getting privileged as it allows authentication of a legitimate user without entering a personal identification number or password. Only a glance of any physical characteristics at a camera proves the user’s identity making it secure from shoulder suffering attacks and theft. This review will explain the various biometric authentication techniques available for user authentication. Biometric authentication is generally based on two categories 1. Based on physical traits 2. Based on behavioral traits. A comprehensive comparison of all methods is discussed with their merits and demerits. Based on the comparison, we will provide insight into how machine learning algorithms help develop biometric authentication systems. We will also explore the various applications based on biometric authentication.
A New Area of the Mouse Anterior Hypothalamus Involved in Septohypothalamic Circuit

While the hypothalamus is now classiﬁed into more than ten compartments, uncharacterized areas remain. In this study, we show a new area in the anterior hypothalamus (AH) of mice, a triangular-shaped area between the paraventricular hypothalamic nucleus (PVN) and the fornix, which is enriched in chondroitin sulfate proteoglycans (CSPGs). We designated this region perifornical area of the AH (PeFAH) based on its anatomical location. In Nissl staining, the PeFAH was distinguishable as an area of relatively low density. Immunohistochemical and DNA microarray analyses indicated that PeFAH contains sparsely distributed calretinin-positive neurons and densely clustered enkephalin-positive neurons. Furthermore, the PeFAH was shown to have bidirectional neural connections with the lateral septum (LS). We conﬁrmed enkephalinergic projections from PeFAH neurons to the LS, and inversely, calbindin-positive LS neurons as aﬀerents to the PeFAH. Finally, c-Fos expression analysis revealed that the activity of PeFAH neurons tended to be increased by psychological stressors but not by homeostatic stressors. These ﬁndings of neuronal subtypes and projections suggest that the region of the densely clustered encephalin-positive neurons in the PeFAH is comparable with the perifornical nucleus previously identiﬁed in rats.
An ensemble approach: Combining Wisdom from Truth Discovery Methods

With the rapid development of the Internet, there is a large amount of conflicting data on the network. It is becoming increasingly important to identify the true information. Truth discovery becomes a hot research topic, it identifies object truth while estimating source reliability. Although many methods have been proposed, they are all applicable to specific scenarios, and none of them can be applied to all scenarios or always better than others. That is, there is no one-fit-all solution. In addition, objects have different difficulties. Accurately assessing the difficulty of each object is conducive to improving the accuracy of the truth discovery. In order to solve the above problem, we propose a distributed ensemble truth discovery model (DETD), which combines different results output by base truth discovery algorithms, while considering the difficulty of each object and computing the reliability of base algorithms. Experimental results show that the proposed DETD model can effectively estimate the truths of objects on two real-world datasets.
A Simple, Linear Circuit for Measuring fF Range of Capacitances

There are numerous applications wherein capacitive sensing is performed such as, measurement of flow rates and environmental factors, dielectric testing, composition evaluation, proximity sensing, etc. Sometimes, the change brought on the capacitance of the sensor by the measurand may be in femto-Farad (fF) range. The circuitry required to measure changes this low can be very expensive and complex. So, a unique circuit was designed by the authors in one of their previous works that can detect such minute variations in capacitance without much complexity. This circuit does a differencing operation first, followed by a differential measurement to increase the sensitivity as well as to obtain linear outputs with respect to the capacitance measured. As a continuation, this paper presents the performance analysis of this circuit after some design modifications. The experimental results obtained were linear as per the theory of operation of this circuit and its sensitivity and repeatability were very good. However, a few parameters need improvement. But, as the results look promising on-the-whole, with suitable corrections, this circuit can be utilized for a wide variety of applications.
ChatGPT and Generative AI Guidelines for Addressing Academic Integrity and Augmenting Pre-Existing Chatbots

Chat Generative Pretrained Transformer (Chat-GPT) and related Generative AI models are leading a paradigm shift in the acceptance and application of Artificial Intelligence (AI) across all disciplines and industry sectors. Despite the criticisms of an ‘intelligence without knowledge or reasoning or the notions of truth’, ChatGPT is highly effective at human-like conversation with seemingly sophisticated and useful responses to questions, summarization, classification, extraction and generation tasks. Unlike similar large AI models in the modalities of image, audio and video, text-based conversation is straightforward and familiar to a large audience of regular users of the Internet and smartphone applications. This is further accentuated by the large-scale adoption of ‘standard’ chatbot technologies for trivial conversations in task-specific automation, across every industry sector. This rare combination of highly effective human-like conversation, familiarity of foundational technology and versatility of intelligent application, has led to several challenges and opportunities in leveraging generative AI. A primary challenge is its impact on the academic integrity of scholarly work, where AI-generated content can be useful and detrimental in both teaching and research. On the other hand, ChatGPT presents a unique opportunity in augmenting preexisting (‘standard’) chatbots with human-like conversation for advanced intelligent automation, across all application domains. Although diametrically opposed, the challenge of addressing academic integrity and the opportunity of augmenting pre-existing chatbots are grounded in the conversational AI capabilities of ChatGPT and similar generative AI models. In this paper, we investigate these formative capabilities and present guidelines for leveraging ChatGPT and similar generative AI models.