Institution Of Engineers Research Articles

Personalized feature extraction for manufacturing process signature characterization and anomaly detection

Manufacturing process signatures reflect the process stability and anomalies that potentially lead to detrimental effects on the manufactured outcomes. Sensing technologies, especially in-situ image sensors, are widely used to capture process signatures for diagnostics and prognostics. This imaging data is crucial evidence for process signature characterization and monitoring. A critical aspect of process signature analysis is identifying the unique patterns in an image that differ from the generic behavior of the manufacturing process in order to detect anomalies. It is equivalent to separating the “unique features” and process-wise (or phase-wise) “shared features” from the same image and recognizing the transient anomaly, i.e., recognizing the outlier “unique features”. In state-of-the-art literature, image-based process signature analysis relies on conventional feature extraction procedures, which limit the “view” of information to each image and cannot decouple the shared and unique features. Consequently, the features extracted are less interpretable, and the anomaly detection method cannot distinguish the abnormality in the current process signature from the process-wise evolution. Targeting this limitation, this study proposes personalized feature extraction (PFE) to decouple process-wise shared features and transient unique features from a sensor image and further develops process signature characterization and anomaly detection strategies. The PFE algorithm is designed for heterogeneous data with shared features. Supervised and unsupervised anomaly detection strategies are developed upon PFE features to remove the shared features from a process signature and examine the unique features for abnormality. The proposed method is demonstrated on two datasets (i) selected data from the 2018 AM Benchmark Test Series from the National Institute of Standards and Technology (NIST), and (ii) thermal measurements in additive manufacturing of a thin-walled structure of Ti–6Al–4V. The results highlight the power of personalized modeling in extracting features from manufacturing imaging data.

Local Fourier features for handwriting digit images classification

Multiple choice questions (MCQ) are effective in normative assessment and offline testing is still relevant due to the lack of efficient mass infrastructures and maintenance. For the automatic correction of MCQ paper form and reporting of the grade, it is generally necessary to read and recognize a handwriting digit in a box. This paper focuses on local feature extraction in the frequency domain using Fourier transform. The pre-process begins with the extraction of the fields from the entity map, followed by the application of 2D fast Fourier transform (2DFFT) and the reduction of computed coefficients to obtain the corresponding final local characteristic in the representation. The experimental results of the Modified National Institute of Standards and Technology (MNIST) handwriting digits dataset show that the local characteristics extracted in the frequency domain used as input to a support vector machine (SVM) classifier are efficient in terms of 99.51% accuracy. The proposed system successfully helped in the reporting of all the marks for seven subjects in a class of 98 students during the automatic correction of the MCQ exam papers.

ScriptCalc: Handwriting Recognition with Mathematical Equation Solving

Abstract: The present paper proposes a novel algorithm for recognition of handwritten digits. For this, the present paper classified the digits into two groups: one group consists of blobs with/without stems and the other digits with stems only. The blobs are identified based on a new concept called morphological region filling methods. This eliminates the problem of finding the size of blobs and their structuring elements. The digits with blobs and stems are identified by a new concept called ‘connected component’. This method completely eliminates the complex process of recognition of horizontal or vertical lines and the property called ‘concavities’. The digits with only stems are recognized, by extending stems into blobs by using connected component approach of morphology. The present method has been applied and tested with various handwritten digits from modified NIST (National Institute of Standards and Technology) handwritten digit database (MNIST), and the success rate has been given. The present method is also compared with various existing methods. Once the digits are recognized, they are assembled back into their respective positions within the equation. Mathematical equation solving techniques are then applied to evaluate the expression and obtain the result

Gender equality in education: an analysis of entrepreneurial behavior in technological colleges in a developing country

ABSTRACT The article analysed the differences in the influence of entrepreneurial characteristics on entrepreneurial intention according to gender in technological higher education education in a developing country. The methodology used was quantitative, with multivariate data analysis through structural equation modelling. The sample consisted of 604 students of technological higher education from the São Paulo State Technological Colleges. The results proved that the female gender had a more intense influence on self-efficacy and persistence influence on entrepreneurial intention. The male gender had a more significant influence on the relationship between sociability and entrepreneurial intention. For technological higher education institutions to implement an effective entrepreneurial education, it is fundamental to encourage entrepreneurial characteristics differently between genders. Developing and fostering entrepreneurship among women in higher technological education requires a deeper understanding of the female entrepreneurial profile.

End-to-end model for automatic seizure detection using supervised contrastive learning

Epilepsy is a chronic neurological disorder characterized by recurrent and unpredictable seizures, caused by abnormal electrical activity in cerebral neurons. Given that it is one of the most common neurological disorders globally, the efficient and accurate automatic seizure detection is urgently needed in the diagnosis of epilepsy to reduce the workload of continuous electroencephalogram (EEG) monitoring. Current deep learning based seizure detection approaches usually employ cross-entropy loss as objective function, which generally suffer from inadequate utilization of sample labels and poor classification margins, resulting in decreased performance in seizure detection. In this study, we propose an end-to-end automatic seizure detection framework based on supervised contrastive learning, which effectively utilizes labeled EEG to cluster similar samples while separating dissimilar ones. A supervised contrastive learning loss is employed to optimize classification boundaries by making full use of EEG labels. We employ long-term continuous EEG for evaluation. Given the presence of various noise and interferences, assessment on long-term continuous EEG proves to be more challenging. Post-processing techniques such as smoothing filter, threshold judgment, and collar technique are further adopted to diminish the artifact impacts on seizure detection performance. The proposed method is evaluated on the publicly available Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, achieving an event-based sensitivity of 99.71% and a false detection rate (FDR) of 0.35/h.

Surface optimization of trapezoidal thread spindles—tribological analysis and application

Trapezoidal threaded spindles are ideal for supporting heavy loads and are therefore frequently used in heavy-duty lifting systems. Both the forming thread rolling process and the machining thread whirling process can be used to manufacture these lead screws. In order to improve the low efficiency of trapezoidal screw drives, it is crucial to understand and minimize friction losses. As part of a cooperation project between the Institute of Production Engineering and Machine Tools (IFW) and Bornemann Gewindetechnik, research was carried out into adapting the whirling process to optimize the tribological properties of threaded spindles. The process developed makes it possible to reduce the coefficient of friction in trapezoidal screw drives and thus also the required torque. By using whirled microtextures, the friction loss can be reduced by 25.5% and adhesive wear can be significantly reduced.

Constrained Device Performance Benchmarking with the Implementation of Post-Quantum Cryptography

Advances in quantum computers may pose a significant threat to existing public-key encryption methods, which are crucial to the current infrastructure of cyber security. Both RSA and ECDSA, the two most widely used security algorithms today, may be (in principle) solved by the Shor algorithm in polynomial time due to its ability to efficiently solve the discrete logarithm problem, potentially making present infrastructures insecure against a quantum attack. The National Institute of Standards and Technology (NIST) reacted with the post-quantum cryptography (PQC) standardization process to develop and optimize a series of post-quantum algorithms (PQAs) based on difficult mathematical problems that are not susceptible to being solved by Shor’s algorithm. Whilst high-powered computers can run these PQAs efficiently, further work is needed to investigate and benchmark the performance of these algorithms on lower-powered (constrained) devices and the ease with which they may be integrated into existing protocols such as TLS. This paper provides quantitative benchmark and handshake performance data for the most recently selected PQAs from NIST, tested on a Raspberry Pi 4 device to simulate today’s IoT (Internet of Things) devices, and provides quantitative comparisons with previous benchmarking data on a range of constrained systems. CRYSTALS-Kyber and CRYSTALS-Dilithium are shown to be the most efficient PQAs in the key encapsulation and signature algorithms, respectively, with Falcon providing the optimal TLS handshake size.

Tools and methods to study and replicate experiments addressing human social cognition in interactive scenarios.

In the last decade, scientists investigating human social cognition have started bringing traditional laboratory paradigms more "into the wild" to examine how socio-cognitive mechanisms of the human brain work in real-life settings. As this implies transferring 2D observational paradigms to 3D interactive environments, there is a risk of compromising experimental control. In this context, we propose a methodological approach which uses humanoid robots as proxies of social interaction partners and embeds them in experimental protocols that adapt classical paradigms of cognitive psychology to interactive scenarios. This allows for a relatively high degree of "naturalness" of interaction and excellent experimental control at the same time. Here, we present two case studies where our methods and tools were applied and replicated across two different laboratories, namely the Italian Institute of Technology in Genova (Italy) and the Agency for Science, Technology and Research in Singapore. In the first case study, we present a replication of an interactive version of a gaze-cueing paradigm reported in Kompatsiari et al. (J Exp Psychol Gen 151(1):121-136, 2022). The second case study presents a replication of a "shared experience" paradigm reported in Marchesi et al. (Technol Mind Behav 3(3):11, 2022). As both studies replicate results across labs and different cultures, we argue that our methods allow for reliable and replicable setups, even though the protocols are complex and involve social interaction. We conclude that our approach can be of benefit to the research field of social cognition and grant higher replicability, for example, in cross-cultural comparisons of social cognition mechanisms.

The TESS-Keck Survey. VII. A Superdense Sub-Neptune Orbiting TOI-1824* *Some of the data presented herein were obtained at Keck Observatory, which is a private 501(c)3 nonprofit organization operated as a scientific partnership among the California Institute of Technology, the University of California, and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of

We confirm a massive sub-Neptune-sized planet on a P = 22.8 days orbit around the star TOI-1824 (T eff = 5200 K, V = 9.7 mag). TESS first identified TOI-1824 b (formerly TOI-1824.01) as an object of interest in 2020 April after two transits in Sector 22 were matched with a single transit in Sector 21. TOI-1824 was subsequently targeted for ground-based Doppler monitoring with Keck-HIRES and APF-Levy. Using a joint model of the TESS photometry, radial velocities, and Ca ii H and K emission measurements as an activity indicator, we find that TOI-1824 b is an unusually dense sub-Neptune. The planet has a radius R p = 2.63 ± 0.15 R ⊕ and mass M p = 18.5 ± 3.2 M ⊕, implying a bulk density of 5.6 ± 1.4 g cm−3. TOI-1824 b's mass and radius situate it near a small group of “superdense sub-Neptunes” (R p ≲ 3 R ⊕ and M p ≳ 20 M ⊕). While the formation mechanism of superdense sub-Neptunes is a mystery, one possible explanation is the constructive collision of primordial icy cores; such giant impacts would drive atmospheric escape and could help explain these planets' apparent lack of massive envelopes. We discuss TOI-1824 b in the context of these overdense planets, whose unique location in the exoplanet mass–radius plane make them a potentially valuable tracer of planet formation.

Talicabtagene Autoleucel: First Approval.

Talicabtagene autoleucel (NexCAR19™) is a chimeric antigen receptor (CAR) T-cell therapy being developed by the Indian Institute of Technology, Bombay (IIT-B) and Immunoadoptive Cell Therapy (ImmunoACT) for the treatment of relapsed/refractory B-cell malignancies. Talicabtagene autoleucel contains autologous T cells from the patient, which have been modified to express a humanized anti-CD19 CAR that targets B cells. A single intravenous dose of talicabtagene autoleucel was associated with high response rates in pooled results from a phase I and phase II trial in patients with relapsed/refractory B-cell malignancies. Talicabtagene autoleucel was approved in India for the treatment of relapsed/refractory B-cell lymphomas and relapsed/refractory B-cell acute lymphoblastic leukaemia on 13 October 2023. This article summarizes the milestones in the development of talicabtagene autoleucel leading to this first approval for relapsed/refractory B-cell lymphomas and relapsed/refractory B-cell acute lymphoblastic leukaemia.

Challenges of Innovation in the Health Economic-Industrial Complex: A Case Study on Implementation of Research Results in a Brazilian Science and Technology Institution

In addition to advancing scientific knowledge, science and technology institutions (STIs) must translate research outcomes into practical applications, thereby addressing societal needs and challenges. Consequently, this study examines the outcomes produced by a public STI, employing the morphology of the Health Economic-Industrial Complex (HEIC) as an analytical framework. Notably, 77.8% of these outcomes have benefited populations marginalized by public policies. The research underscores a robust correlation between the institution's capacity for technological innovation and the chemical and biotechnological industrial subsystems, particularly in diagnostics and diagnostic services. Significantly, the primary impact of these innovations manifests in formulating and enhancing public health policies, thereby directly influencing governmental initiatives aimed at bolstering the Unified Health System.

Early STEAM Education Practice: Application of Graph Theory through Teaching Assistants

In the age of Society 5.0, which is the concept of a future society developed by the Japanese government, science, technology, engineering, art, and mathematics (STEAM) human resources with the skills to grasp things from multiple perspectives and solve problems will be required. Furthermore, Society 5.0 indicates that the National Institute of Technology (KOSEN) will become the STEAM center for elementary and junior high school students, as part of the efforts to establish a system that supports STEAM education. Since 2019, we have practiced STEAM education as part of “Liberal Arts Special Lectures” for 4th-year students of the main course (1st year of the undergraduate course). In these lectures, the teachers of liberal arts subjects present themes using their specialties, such as mathematics, debate, and economics. Collaborative learning between students from various departments led them to deep learning, which was a fusion of knowledge and creation. However, there are few opportunities to give back to society, particularly a platform to disseminate the acquired mathematics ability. Therefore, we aim to realize early STEAM education and give back to society by creating STEAM teaching materials on graph theory in open courses for junior high school students between the ages of 13 and 15 with help from teaching assistants who studied graph theory in the liberal arts special lectures. Moreover, through a course on graph theory, the course will not only arouse students' interest in mathematics, but also provide them with clues to develop their multifaceted ideas. Keywords: society 5.0, graph theory, open course for junior high school students, teaching assistants, liberal arts special lecture

A 0.012‐mm2 0.244‐pJ/bit successive approximation register analog‐to‐digital converter‐based true random number generator for Internet of Things applications in a 65‐nm complementary metal–oxide–semiconductor

SummaryThis paper proposes a true random number generator (TRNG) based on a successive approximation register (SAR) analog‐to‐digital converter (ADC). A top‐plate sampling SAR ADC structure and a new MSB‐isolation switch scheme (MISS) are combined to achieve low‐power operation and small area occupancy at the same time. When the SAR ADC has completed its conversion, a secondary firing of the comparator measures the remaining signal. This 1‐bit quantized residue serves as the true random sequence, thus eliminating the need for extra circuitry in the TRNG. A 65‐nm prototype of the proposed TRNG was found to occupy an area of 0.012 mm2 and successfully passed all the National Institute of Standards and Technology (NIST) tests without post‐processing. It achieved a figure‐of‐merit of 0.244 pJ/bit for a 1 Mb/s random sequence with a 0.8‐V power supply. Its resilience to power noise injection attacks was also verified.

Photoelectric synapses based on all-two-dimensional ferroelectric semiconductor heterojunction

Abstract Photoelectric synapses are attaching intensive attention due to its low power consumption and adaptive learning. However, traditional ferroelectric field effect transistors are not conducive to the integrated application in artificial intelligence systems. Here, we design the all two-dimensional photoelectric synapse device based on WSe2/MoS2/α-In2Se3 ferroelectric van der Waals heterojunction, which has high memory capacity (memory on/off = 105) and synaptic function. In addition, we simulate an artificial neural network to modify the handwritten digit recognition of the National Institute of Standards and Technology. In particular, the recognition rates are 92.4% and 93.6% for electrical synapse and photoelectric synapse, respectively. This work provides an effective strategy for achieving stable integration of neuromorphic computing.

Identification of Population-Specific Novel Protein Biomarkers and Possible Therapeutic Targets in Gliomas by Proteomics Approach

Abstract Objective To analyze the differential proteomic profile of gliomas in patients from South India and to identify novel protein glioma biomarkers and possible therapeutic targets to tailor the treatment to individual patients. Material and Methods We have prospectively analyzed the differential proteomic profile of 34 patients with glioma imaging characteristics and compared them with that of normal brain tissue. This research was conducted at the Institute of Neurosurgery, Madras Medical College, in technical collaboration with the Indian Institute of Technology, Madras, over 1 year. Statistical Analysis Biological variate analysis (I-ANALYSIS OF VARIANCE (ANOVA)) was used, with p-value less than 0.05 being significant. Results Twenty proteins (10 upregulated and 10 downregulated) were differentially expressed in tumor tissue. The expression of three pro-apoptotic proteins was downregulated and the expression of three anti-apoptotic proteins was upregulated with statistical significance. The cellular functions of the 20 differentially regulated proteins were subjected to pathway analysis revealing significant alterations in heme biosynthesis, deoxyribonucleic acid (DNA) replication, fibroblast growth factor (FGF) signaling, and epidermal growth factor (EGF0 receptor signaling in glioma. Conclusion KRT18, PRS4, and EF1A2 are anti-apoptotic proteins and are significantly upregulated in gliomas. EARS2, COX5A, and LSM3 are pro-apoptotic proteins, and are significantly downregulated in gliomas. This subverts the apoptotic pathways resulting in prolonged cell survival. This study's statistically significant dysregulation of these six proteins was unique, suggesting that they might be considered population-specific biomarkers and possible therapeutic targets for patients from South India. Abnormalities of heme biosynthesis at the proteomic level were identified in this study, which has not been very well studied previously.

(Invited) III-Nitride Ultraviolet LEDs and Lasers for Applications in Biology and Medicine

Solid-state ultraviolet (UV) light emitters are being developed for emerging biomedical applications such as surface sterilization, air/water purification, and medical treatments as a replacement for mercury vapor lamps. While these UV emitters offer many advantages over mercury lamps, such as more compact form factors, tunable emission wavelengths, and greatly increased lifetimes, they also suffer from a number of issues which hinder their implementation in many potential applications. UV emitters are based on AlGaN heterostructures which usually have very poor external quantum efficiencies (EQEs) of less than 10%, with EQEs decreasing with emission wavelength. This makes it very challenging to realize high efficiency UV emitters whose high energy photons are capable of inactivating viruses, bacteria, and other pathogens. This work discusses recent developments made by the Zhang Research Group at the Rochester Institute of Technology in improving the efficiencies of UV LEDs and lasers.

Design of a New Neuro-Generator with a Neuronal Module to Produce Pseudorandom and Perfectly Pseudorandom Sequences

This paper presents the design of a new neuro-generator of pseudorandom number type PRNG Pseudorandom Number Generator, which produces complex sequences with an adequate bit distribution. The circuit is connected to a neuronal module with six impulse neurons with different behaviors: spike frequency adaptation, phasic spiking, mixed mode, phasic bursting, tonic bursting and tonic spiking. This module aims to generate a non-periodic signal that becomes the clock signal for one of the LFSRs Linear Feedback Shift Register that the neuro-generator has. To verify its correct operation, the neuro-generator was subjected to a series of tests where the frequencies of the impulse neurons were modified. This modification allows the generation of a greater number of pulses at the output of the neuronal module, to obtain sequences with different characteristics that pass different NIST statistical tests (National Institute of Standards and Technology of U.S.). The results show that the new neuro-generator maintains pseudo-randomness in the sequences obtained with different frequencies and it can be implemented on a reconfigurable FPGA Field Programmable Gate Array Virtex 7 xc7vx485t-2ffg1761 device. Therefore, it can be used for applications such as biological systems.

Fuzzy Classification Approach to Select Learning Objects Based on Learning Styles in Intelligent E-Learning Systems

In e-learning systems, even though the automatic detection of learning styles is considered the key element in the adaptation process, it does not represent the main goal of this process at all. Indeed, to accomplish the task of adaptation, it is also necessary to be able to automatically select the learning objects according to the detected styles. The classification techniques are the most used techniques to automatically select the learning objects by processing data derived from learning object metadata. By using these classification techniques, considerable results are obtained via several approaches and consist of mapping the learning objects into different teaching strategies and then mapping these strategies into the identified learning styles. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they do not directly map learning object metadata elements to learning style dimensions. Moreover, they do not consider the fuzzy nature of learning objects. Indeed, any learning object can be suitable for different learning styles at varying degrees of suitability. This highlights the need to find a way to remedy this shortcoming. Our work is part of the automatic selection of learning objects. So, we will propose an approach that uses the fuzzy classification technique to select learning objects based on learning styles. In this approach, the metadata of each learning object that complies with the Institute of Electrical and Electronics Engineers (IEEE) standard are stored in a database as an Extensible Markup Language (XML) file. The Fuzzy C Means algorithm is used, on one hand, to assign fuzzy suitability rates to the stored learning objects and, on the other hand, to cluster them into the Felder and Silverman learning styles model categories. The experiment results show the performance of our approach.

Memristive Switching Mechanism in Colloidal InP/ZnSe/ZnS Quantum Dot-Based Synaptic Devices for Neuromorphic Computing.

Quantum dots (QDs) have garnered a significant amount of attention as promising memristive materials owing to their size-dependent tunable bandgap, structural stability, and high level of applicability for neuromorphic computing. Despite these advantageous properties, the development of QD-based memristors has been hindered by challenges in understanding and adjusting the resistive switching (RS) behavior of QDs. Herein, we propose three types of InP/ZnSe/ZnS QD-based memristors to elucidate the RS mechanism, employing a thin poly(methyl methacrylate) layer. This approach not only allows us to identify which carriers (electron or hole) are trapped within the QD layer but also successfully demonstrates QD-based synaptic devices. Furthermore, to utilize the QD memristor as a synapse, long-term potentiation/depression (LTP/LTD) characteristics are measured, resulting in a low nonlinearity of LTP/LTD at 0.1/1. On the basis of the LTP/LTD characteristics, single-layer perceptron simulations were performed using the Extended Modified National Institute of Standards and Technology, verifying a maximum recognition rate of 91.46%.

Screening of Metal Reduction Potential for Thermochemical Hydrogen Storage

The screening of all non-radioactive metals without lanthanides for thermochemical hydrogen storage was performed based on physical chemistry calculations. The thermodynamic data were collected from the NIST (National Institute of Standards and Technology) public data repository, which was followed by calculations regarding the change in enthalpy, entropy, Gibbs free energy and equilibrium reaction temperature. The results were critically evaluated based on thermodynamic parameters, viable metals were identified, and their hydrogen storage densities and energy–enthalpy ratios were evaluated. The elements viable for controlled thermochemical hydrogen storage via the reversible reduction and oxidation of metal oxides and metals are manganese (Mn), iron (Fe), molybdenum (Mo) and tungsten (W). Manganese has the largest theoretical potential for hydrogen storage with reversible reduction and oxidation of metal oxides and metals. The second candidate is iron, while the other two (Mo and W) have much lower potential. More research efforts should be dedicated to experimental testing of the identified metals (Mn, Fe, Mo and W) and their different oxides for thermochemical hydrogen storage capabilities both on laboratory and pilot scales. Ferromanganese alloy(s) might also prove itself as an efficient and affordable thermochemical hydrogen storage material. Our theoretical investigation expanded the knowledge on thermochemical hydrogen storage and is accompanied with a brief literature review revealing the lack of experimental studies, especially on oxidation of metals with water vapor occurring during the hydrogen release phase of the cycle. Consequently, accurate modelling of transport, kinetics and other phenomena during hydrogen storage and release is scarce.