Miracle-dz/UniFAR-FTUs huggingface dataset:
Color Laser Microscope

A color laser microscope utilizing a new color laser imaging system has been developed for the visual inspection of semiconductors. The light source, produced by three lasers (Red; He-Ne, Green; Ar, Blue; He-Cd), is deflected horizontally by an AOD (Acoustic Optical Deflector) and vertically by a vibration mirror. The laser beam is focused in a small spot which is scanned over the sample at high speed. The light reflected back from the sample is reformed to contain linear information by returning to the original vibration mirror. The linear light is guided to the CCD image sensor where it is converted into a video signal. Individual CCD image sensors are used for each of the three R, G, or B color image signals. The confocal optical system with its laser light source yields a color TV monitor image with no flaring and a much sharper resolution than that of the conventional optical microscope. The AOD makes possible a high speed laser scan and a NTSC or PAL TV video signal is produced in real time without any video memory. Since the light source is composed of R, G, and B laser beams, color separation superior to that of white light illumination is achieved. Because of the photometric linearity of the image detector, the R, G, and B outputs of the system are most suitably used for hue analysis. The CCD linear image sensors in the optical system produce no geometrical distortion, and good color registration is available principally. The output signal can be used for high accuracy line width measuring. The many features of the color laser microscope make it ideally suited for the visual inspection of semiconductor processing. A number of these systems have already been installed in such a capacity. The Color Laser Microscope can also be a very useful tool for the fields of material engineering and biotechnology.
Phenomenological Modelling of Camera Performance for Road Marking Detection

With the development of autonomous driving technology, the requirements for machine perception have increased significantly. In particular, camera-based lane detection plays an essential role in autonomous vehicle trajectory planning. However, lane detection is subject to high complexity, and it is sensitive to illumination variation, appearance, and age of lane marking. In addition, the sheer infinite number of test cases for highly automated vehicles requires an increasing portion of test and validation to be performed in simulation and X-in-the-loop testing. To model the complexity of camera-based lane detection, physical models are often used, which consider the optical properties of the imager as well as image processing itself. This complexity results in high efforts for the simulation in terms of modelling as well as computational costs. This paper presents a Phenomenological Lane Detection Model (PLDM) to simulate camera performance. The innovation of the approach is the modelling technique using Multi-Layer Perceptron (MLP), which is a class of Neural Network (NN). In order to prepare input data for our neural network model, massive driving tests have been performed on the M86 highway road in Hungary. The model’s inputs include vehicle dynamics signals (such as speed and acceleration, etc.). In addition, the difference between the reference output from the digital-twin map of the highway and camera lane detection results is considered as the target of the NN. The network consists of four hidden layers, and scaled conjugate gradient backpropagation is used for training the network. The results demonstrate that PLDM can sufficiently replicate camera detection performance in the simulation. The modelling approach improves the realism of camera sensor simulation as well as computational effort for X-in-the-loop applications and thereby supports safety validation of camera-based functionality in automated driving, which decreases the energy consumption of vehicles.
Load-independent class E power inverters. I. Theoretical development

An analysis is presented for a class-E power inverter that exhibits any of the following characteristics: (1) load-independent, nonzero output voltage, but not load-independent efficiency; (2) load-independent nonzero efficiency, but not load output voltage; (3) simultaneous load-independent, nonzero output voltage and load-independent nonzero efficiency. Solutions for a class-E inverter with load-independent, nonzero output voltage and 100% efficiency are given. The infinite set of solutions is reduced by practical considerations. Circuit parameters for practical load-independent class-E inverters are provided. >
Effect of Automated Drip Irrigation System on Yield and Water Use Efficiency of Tomato (Solanum lycopersicum L.)

Field experiment was conducted to evaluate the performance of different automated drip irrigation on tomato crop under sandy clay loam soil in Tamil Nadu Agricultural University during kharif 2019 and kharif 2020. Five treatments comprising 4 different automated drip irrigation systems are time based drip irrigation, volume based drip irrigation, soil moisture sensor based irrigation, switching tensiometer based irrigation and one is conventional method of irrigation were tested. The results revealed that tensiometer based drip irrigation recorded higher fruit yield of 95.11 and 96.21 t ha-1 and water use efficiency of 21.10 and 25.42 t ha-mm-1 resulting in increment over conventional method of irrigation. However, the above treatment was followed by soil moisture sensor based drip irrigation in tomato. Tensiometer based drip irrigation helps to save the water up to 54.91 and 60.55 % compared to conventional method of irrigation during kharif 2019 and 2020.
SAW Sensor Array Data Fusion for Chemical Class Recognition of Volatile Organic Compounds

Present study deals the development of data fusion based artificial intelligence unit for the chemical sensor array based electronic nose (E-Nose) system. We focus particularly on feature level fusion of model surface acoustic wave (SAW) sensor array response for chemical class identification of volatile organic compounds (VOCs). Three methods are used for feature extraction namely: principal component analysis (PCA); independent component analysis (ICA) and kernel principal component analysis (KPCA). Fused features are generated with three unsupervised fusion schemes and validated in combination with support vector machine (SVM) classifier. Study is concluded by the analysis of 12 model SAW sensor array data sets. It suggests that amongst the three feature fusion schemes; feature fusion by summation result highest class recognition rate of VOCs.
Assessing the Safety Benefit of Advanced Vehicular Technology for Protecting Pedestrians

Pedestrian safety is of keen interest because the vulnerability of pedestrians leads to severe injuries in pedestrian–vehicle crashes. Various countermeasures for protecting pedestrians have been developed in the field of traffic and vehicular safety engineering. The pedestrian protection airbag system (PPAS) is one of the recently developed vehicular technologies for pedestrian protection. PPAS is a promising, passive safety technology for reducing the severity of pedestrian injury. The purpose of this study was to evaluate the safety benefits of PPAS. A probabilistic pedestrian fatality model and a set of linear regression models were employed to estimate the effectiveness of PPAS in reducing the severity of pedestrian injury. Head injury criteria data, which represented the level of severity of pedestrian head injury, were obtained from indoor experimental collisions. The number of pedestrian fatalities was estimated for two cases on the basis of the pedestrian fatality model: one with PPAS and one without PPAS. In addition, market penetration rates (MPRs) were applied in analyzing the safety benefits in various scenarios. Approximately 30.30% to 68.94% of pedestrian fatalities would be saved with 100% MPR of PPAS. The outcomes of this study could be used to enhance the functionality of PPAS for reducing fatalities and establishing relevant traffic safety policies.
Ranking of Sensitive Positions using Statistical and Correlational Analysis

Abstract Condition Based Monitoring of a machine refers to the checking of various parameters and signatures of the machine and then predicting the machine’s current health status. The slight changes in the machine operating condition are carefully analyzed to know if the machine is having any fault. For monitoring the changes, we acquire acoustic data i.e. generated while machine is running. In order to collect acoustic data, a number of sensors are placed near various positions of the machine surface. Acquiring data from large number of sensor positions is not economically viable. It would always be preferable to have a monitoring system that acquires data quickly and efficiently, without compromising on the robustness of the system. Therefore there is a need to locate some special positions on the machine, termed as “sensitive positions”, which are expected to exhibit the fault characteristics in a much better way than others. This paper presents a novel method for ranking sensitive positions for a machine based on statistical parameters analysis. While the final list of required number of sensitive positions is generated, the cross-correlation amongst the positions is also taken into consideration to avoid redundancy. Furthermore, a standalone application for implementing the same has been developed on Android platform. The proposed scheme and application can be used for variety of other applications that work on similar principle of acquiring data from multiple sensors.
Experience with gas insulated substations in the USA

This paper summarises investigations on the reliability of GIS in the USA, including a primary pilot survey of US utility experience over a 20 year period. The early years of GIS introduction were associated with problems with first generation technology, and the resultant service data shows that such difficulties may have contributed to overall perceptions on GIS reliability. A review of the design features of early GIS is thus presented and related to the design and processing approaches adopted for proven modern technology. In order to relate these observations to experience, the paper includes comparative analyses with the broader based reliability data acquired from a large number of operational GIS around the world supporting the very high reliability that can be expected from modern GIS. Implications for GIS design and advantages for modern practical application are summarized. <<ETX>>
Development of Monitoring Tool to Support Lean Manufacturing Implementation

—Lean is an approach that is carried out continuously to identify and eliminate waste or other activities that do not provide added value. Many companies have implemented lean concepts and got the benefits such as reduced cycle time, reduced operational costs, improved product quality, and other benefits. Although it provides many benefits, there are some weaknesses in implementing lean, one of which is due to the lack of monitoring. After the initial implementation, the process has never been reviewed and is rarely checked to ensure its implementation. Monitoring lean implementation requires tools that can help monitor work more efficiently. There are no specific tools that can be used to control and oversee lean implementation in the company. For this reason, this research will develop a tool for monitoring lean implementation. The tools to be developed adopting the logic of several methods, namely Quality Function Deployment (QFD), the framework of three matrix houses in lean implementation, and the House of Risk (HOR). This Lean monitoring tool is used to ensure that the corrective actions recommended for lean implementation are implemented. The Lean monitoring tool consists of three matrices that offer the ability to identify potential failures, the root causes of failures, and solutions to deal with lean implementation failuress.
A DIELECTROPHORESIS MICROPUMP FOR ON-CHIP PARTICLES TRAPPING AND BLOOD DRIVING IN A VIRTUAL CHANNEL

Continuous liquid pumping at a steady and tunable flow rate is demonstrated by creating a proper pressure difference across an electric-field-formed virtual microchannel using dielectrophoresis (DEP). Because the liquid is regulated with no physical channel walls or mechanical movable parts, the fabrication and packaging of the device is simple while the issues of liquid leakage and dead volume are eliminated. With appropriate electrode designs, particles suspended in the pumped liquid can be manipulated by DEP simultaneously. The continuous liquid pumping technique was successfully demonstrated for DI water and human blood.
Evaluation of Anesthetic Specific EEG Dynamics during State Transitions between Loss and Return of Responsiveness

Purpose: electroencephalographic (EEG) information is used to monitor the level of cortical depression of a patient undergoing surgical intervention under general anesthesia. The dynamic state transitions into and out of anesthetic-induced loss and return of responsiveness (LOR, ROR) present a possibility to evaluate the dynamics of the EEG induced by different substances. We evaluated changes in the EEG power spectrum during anesthesia emergence for three different anesthetic regimens. We also assessed the possible impact of these changes on processed EEG parameters such as the permutation entropy (PeEn) and the cerebral state index (CSI). Methods: we analyzed the EEG from 45 patients, equally assigned to three groups. All patients were induced with propofol and the groups differed by the maintenance anesthetic regimen, i.e., sevoflurane, isoflurane, or propofol. We evaluated the EEG and parameter dynamics during LOR and ROR. For the emergence period, we focused on possible differences in the EEG dynamics in the different groups. Results: depending on the substance, the EEG emergence patterns showed significant differences that led to a substance-specific early activation of higher frequencies as indicated by the “wake” CSI values that occurred minutes before ROR in the inhalational anesthetic groups. Conclusion: our results highlight substance-specific differences in the emergence from anesthesia that can influence the EEG-based monitoring that probably have to be considered in order to improve neuromonitoring during general anesthesia.
Very Early Onset of Therapy-Related Acute Myeloid Leukemia with 11q23 Rearrangement Presenting with Unusual PET Findings after R-DA-EPOCH for Primary Mediastinal Large B-Cell Lymphoma

Background: R-DA-EPOCH is an effective regimen for PMLBCL, which permits the omission of consolidative radiotherapy in the majority of patients. Patient: We describe a 27-year-old female patient, who achieved a complete remission after treatment with six cycles of R-DA-EPOCH (up to the final level). At 6 months after the end of treatment, PET/CT revealed an unexpected, diffusely increased 18FDG uptake by the bone marrow. Simultaneously, pancytopenia with monocytosis was observed. Result: The patient was diagnosed with therapy-related myelodysplastic syndrome, which rapidly evolved into acute myeloid leukemia (t-MDS/AML) with MLL rearrangements. She achieved a complete remission after induction therapy, received an allogenic transplant and remains disease-free 2 years later. Conclusions: The extremely early onset of t-MDS/AML, together with the unexpected PET/CT findings make this case unique and highlights the need for the accurate estimation of the possible dose-dependent risk of t-MDS/AML after R-DA-EPOCH in the real-life setting in patients with PMLBCL.
Intelligent Hybrid Coalition for Distributed Sensor Network Clustering

As DSN nodes are intelligent, multi-agent paradigms are more im- portant to adapt to sensing circumstance tactically and semantically. So we pro- pose IHCF(Intelligent Hybrid Coalition Formation) model for constructing dynamic clusters of sencing nodes and synthesizing their sensing signals/informaiton under two regimes(task sharing, result sharng). In this model, a cooperation layer including negotiation and heuristic clustering processes is designed. Latent features and meanings in the cluster are emerged with the help of IHCF scheme. To do this, projection tecnhiques usning dimension reduction is applied. It shows more efficient and adaptable perfor- mance enough to compete with existing 2-tier classifier systems. Currently IHCF is designed for sensing areas of object recognition(e.g. face)
Optimal train control on various track alignments considering speed and schedule adherence constraints

The methodology discussed in this dissertation contributes to the field of transit operational control to reduce energy consumption. Due to the recent increase in gasoline cost, a significant number of travelers are shifting from highway modes to public transit, which also induces higher transit energy consumption expenses. %%%% This study presents an approach to optimize train motion regimes for various track alignments, which minimizes total energy consumption subject to allowable travel time, maximum operating speed, and maximum acceleration/deceleration rates. The research problem is structured into four cases which consist of the combinations of track alignments (e.g., single vertical alignment and mixed vertical alignment) and the variation of maximum operating speeds (e.g., constant and variable). The Simulated Annealing (SA) approach is employed to search for the optimal train control, called "golden run". %%%% To accurately estimate energy consumption and travel time, a Train Performance Simulation (TPS) is developed, which replicates train movements determined by a set of dynamic variables (e,g., duration of acceleration and cruising, coasting position, braking position, etc.) as well as operational constraints (e.g., track alignment, speed limit, minimum travel time, etc.)%%%% The applicability of the developed methodology is demonstrated with geographic data of two real world rail line segments of The New Haven Line of the Metro North Railroad: Harrison to Rye Stations and East Norwalk to Westport Stations. The results of optimal solutions and sensitivity analyses are presented. The sensitivity analyses enable a transit operator to quantify the impact of the coasting position, travel time constraint, vertical dip of the track alignment, maximum operating speed, and the load and weight of the train to energy consumption. %%%% The developed models can assist future rail system with Automatic Train Control (ATC), Automatic Train Operation (ATO) and Positive Train Control (PTC), or conventional railroad systems to improve the planning and operation of signal systems. The optimal train speed profile derived in this study can be considered by the existing signal system for determining train operating speeds over a route. %%%%
A Structured Approach Towards Big Data Identification

Big data is a ”relative” concept. It is the combination of data, application, and platform properties. The term big data has been used with almost every problem involving large size, real time, and heterogeneous data. However, these data attributes are not enough to identify big data by ignoring the application and platform properties for finding processing thresholds. The equivocated identification of big data can lead to an inefficient use of optimization techniques, resulting into global inefficiency, reduced system performance, increasing power consumption, requiring greater effort on the part of the programming team, and misallocation of the hardware resources required for the task. In this regard, a structured approach has been presented for identification of big data. The approach is based on three equations that categorize the Volume, Velocity, and Variety characteristics by relating data, application, and platform properties. The 3Vs identification is necessary for enabling the relevant optimization techniques. In addition to 3Vs identification, it is required to discriminate whether the big data is due to 1V, 2Vs or 3Vs, as the involvement of more Vs increases the problem complexity. In this regard, the classification of big data into strong, moderate or weak level has been proposed . To evaluate the proposed methods, a set of well-known applications have been experimented and categorized, depicting a saving of up to 58% main memory and 44% disk reads, as well as prescribing lower clock rate, lesser cores, sequential programming, and non adaptive processing & storage formats. Moreover, four case studies reported as big data have been analyzed according to the proposed system. The proposed method is able to categorize two case studies as weak low big data presenting only volume, the third case is weak medium due to velocity, whereas in the fourth case no V is involved. Also, the proposed equations reduce the computation and human resources up to 75% of Spark cluster execution. In this manner, the proposed work can save the unnecessary investments by relevant prescriptions. Furthermore, the proposed equations can be integrated into different tools for assisting selective offloading of big data workloads to appropriate software and hardware solutions.
A Risk Based Approach to Node Insertion Within Social Networks

Abstract : Social Network Analysis (SNA) is a primary tool for counter-terrorism operations, ranging from resiliency and in fluence to interdiction on threats stemming from illicit overt and clandestine network operations. In an ideal world, SNA would provide a perfect course of action to eliminate dangerous situations that terrorist organizations bring. Unfortunately, the covert nature of terrorist networks makes the effects of these techniques unknown and possibly detrimental. To avoid potentially harmful changes to enemy networks, tactical involvement must evolve, beginning with the intelligent use of network in filtration through the application of the node insertion problem. The framework for the node insertion problem includes a risk-benefit model to assess the utility of various node insertion scenarios. This model incorporates local, intermediate and global SNA measures, such as Laplacian centrality and assortative mixing, to account for the benefit and risk. Application of the model to the Zachary Karate Club produces a set of recommended insertion scenarios. A designed experiment validates the robustness of the methodology against network structure and characteristics. Ultimately, the research provides an SNA method to identify optimal and near-optimal node insertion strategies and extend past node utility models into a general form with the inclusion of benefit, risk, and bias functions.
Incorporating Within-Household Interactions into Mode Choice Model with Genetic Algorithm for Parameter Estimation

The procedure for estimating a household model of mode choice is described. The tour-based mode choice model incorporates interpersonal interactions within the household explicitly in an agent-based random utility modeling framework. Household interactions include vehicle allocation, ridesharing to joint activities, and drop-off and pickup. Because of the complex nature of the model decision structure, choice probabilities are simulated from direct generation of random utilities rather than through an analytical probability expression. The computational requirements for the simulation are large. Therefore a grid of computers is used in parallel to perform the necessary calculations and a genetic algorithm is used for parameter estimation. A brief description of the model, the full model results, and a discussion of the computational techniques used in parameter estimation are presented.