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In this paper, it is shown that the so‐called in‐situ electrical measurement technique is a valuable tool for understanding failure mechanisms in thick film dielectrics. The technique makes it possible to measure important electrical characteristics of thick film dielectric systems in the temperature range from room temperature up to 900°C. This information is essential to understand failure mechanisms and to optimise the system with respect to quality and reliability. Mainly two electrical properties have been investigated: (i) the electrical resistance of the dielectric as a function of temperature and (ii) the spontaneous electromotive force occurring at higher temperatures between two metal layers with the dielectric in between. A significant result of the work is the observation of a close correlation between the leakage current measured through the dielectric at elevated temperatures, and the ability of the dielectric to resist shorting and blistering effects during the preparation of circuits. Secondly, from in‐situ voltage measurements, it was confirmed that the mixed metallurgy system Au(bottom)‐dielectric‐Ag(top) acts at 850°C as a spontaneous battery, and the battery voltage (i.e., the spontaneous electromotive force) was measured. Depending on the type of dielectric, a battery voltage up to 200 mV between the two metal layers was observed. As a result of this spontaneous electromotive force, blistering occurs. The battery voltage was shown to be much smaller in unmixed metallurgy systems with Ag(bottom)‐dielectric‐Ag(top) or Au(bottom)‐dielectric‐Au(top). However, if an external voltage of 300 mV is applied to such a system during a temperature profile up to 850°C, blisters can also be induced. This shows unambiguously that blistering is a voltage driven effect.

A Cu‐Al bonding system exists when copper wire is bonded onto an aluminum bond pad using thermosonic wire bonding technology. Aged Cu‐Al bonding system was analyzed by measuring the intermetallics layer thickness and its correlation to electrical contact resistance. Result shows that the thickness of Cu‐Al intermetallics layer grows almost linearly to aging time. The activation energy needed for Cu atoms to diffuse into Al was calculated using Fick's law; Q=129.66 kJ/mole and D₀=1.628×10⁻⁴ m²/s. The calculation of activation energy and impurity diffusity using Model Kidson also shows linear relationship. Electrical resistance of Cu‐Al intermetallics layer was calculated from contact resistance of Cu‐Al bonding system. The result shows that the electrical resistance of Cu‐Al intermetallics layer increases linearly with intermetallics thickness. Its growth rate that was calculated using Model of Braunovic and Alexandrov is double of Model of Murcko.

Morton International Inc. have purchased Hoechst AG's printed circuit materials business including their Ozatec dry film and liquid primary imaging photoresists, Ozatec liquid photoimageable solder masks and related process equipment. Concurrently, Hoechst and its US subsidiary Hoechst Celanese Corporation purchased Morton's semiconductor photoresist business. Completion of these transactions was effective from 4 August 1993.

George MacMahon has been appointed Product Manager for Thick Film Materials by W. C. Heraeus, Hanau, Germany, to further increase the company's business throughout Europe and beyond. He will help to co‐ordinate technical support for the general sales effort, including the presentation of technical information at meetings and exhibitions.

Currently, ubiquitous smartphones embedded with various sensors provide a convenient way to collect raw sequence data. These data bridges the gap between human activity and multiple sensors. Human activity recognition has been widely used in quite a lot of aspects in our daily life, such as medical security, personal safety, living assistance and so on.

To provide an overview, the authors survey and summarize some important technologies and involved key issues of human activity recognition, including activity categorization, feature engineering as well as typical algorithms presented in recent years. In this paper, the authors first introduce the character of embedded sensors and dsiscuss their features, as well as survey some data labeling strategies to get ground truth label. Then, following the process of human activity recognition, the authors discuss the methods and techniques of raw data preprocessing and feature extraction, and summarize some popular algorithms used in model training and activity recognizing. Third, they introduce some interesting application scenarios of human activity recognition and provide some available data sets as ground truth data to validate proposed algorithms.

The authors summarize their viewpoints on human activity recognition, discuss the main challenges and point out some potential research directions.

It is hoped that this work will serve as the steppingstone for those interested in advancing human activity recognition.

A new method of studying the accelerated ageing of interconnection materials is applied to a high‐stability thick film resistor system (the Du Pont HS‐80 system). The new method, referred to hereafter as the in‐situ method, allows measurement of the electrical resistance of a thick film resistor to a resolution of a few ppm during accelerated ageing. With the in‐situ technique, the electrical resistance measurements are performed at the elevated ageing temperature during the ageing treatment, whereas with the conventional ageing method the resistance measurements are carried out at room temperature, between subsequent annealing steps. The measuring resolution obtainable with the in‐situ method is orders of magnitude better than with the conventional method. The ageing kinetics can therefore be studied on a shorter time scale and in greater detail than with the conventional method. In this paper, the authors use the in‐situ method to study the accelerated ageing of the Du Pont HS‐80 thick film resistor system, encapsulated with a proper glaze. It will be shown that kinetics of the resistance drift observed in this system cannot be described by an Arrhenius‐type equation. The ageing data can only be interpreted in terms of a kinetic model incorporating a spectrum of activation energies for the ageing process. Such a model is given, and is shown to provide a good explanation of the observed ageing behaviour. The physical process that causes the observed ageing is most probably diffusion of silver from the contacting terminals into the amorphous matrix of the thick film resistor.

Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform.

The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set.

Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation.

This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.

The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.

This study aims to reduce data bias during human activity and increase the accuracy of activity recognition.

A convolutional neural network and a bidirectional long short-term memory model are used to automatically capture feature information of time series from raw sensor data and use a self-attention mechanism to learn select potential relationships of essential time points. The proposed model has been evaluated on six publicly available data sets and verified that the performance is significantly improved by combining the self-attentive mechanism with deep convolutional networks and recursive layers.

The proposed method significantly improves accuracy over the state-of-the-art method between different data sets, demonstrating the superiority of the proposed method in intelligent sensor systems.

Using deep learning frameworks, especially activity recognition using self-attention mechanisms, greatly improves recognition accuracy.

The physically-demanding and repetitive nature of construction work often exposes workers to work-related musculoskeletal injuries. Real-time information about the ergonomic consequences of workers' postures can enhance their ability to control or self-manage their exposures. This study proposes a digital twin framework to improve self-management ergonomic exposures through bi-directional mapping between workers' postures and their corresponding virtual replica.

The viability of the proposed approach was demonstrated by implementing the digital twin framework on a simulated floor-framing task. The proposed framework uses wearable sensors to track the kinematics of workers' body segments and communicates the ergonomic risks via an augmented virtual replica within the worker's field of view. Sequence-to-sequence long short-term memory (LSTM) network is employed to adapt the virtual feedback to workers' performance.

Results show promise for reducing ergonomic risks of the construction workforce through improved awareness. The experimental study demonstrates feasibility of the proposed approach for reducing overexertion of the trunk. Performance of the LSTM network improved when trained with augmented data but at a high computational cost.

Suggested actionable feedback is currently based on actual work postures. The study is experimental and will need to be scaled up prior to field deployment.

This study reveals the potentials of digital twins for personalized posture training and sets precedence for further investigations into opportunities offered by digital twins for improving health and wellbeing of the construction workforce.

This paper aims to deal with the human activity recognition using human gait pattern. The paper has considered the experiment results of seven different activities: normal walk, jogging, walking on toe, walking on heel, upstairs, downstairs and sit-ups.

In this current research, the data is collected for different activities using tri-axial inertial measurement unit (IMU) sensor enabled with three-axis accelerometer to capture the spatial data, three-axis gyroscopes to capture the orientation around axis and 3° magnetometer. It was wirelessly connected to the receiver. The IMU sensor is placed at the centre of mass position of each subject. The data is collected for 30 subjects including 11 females and 19 males of different age groups between 10 and 45 years. The captured data is pre-processed using different filters and cubic spline techniques. After processing, the data are labelled into seven activities. For data acquisition, a Python-based GUI has been designed to analyse and display the processed data. The data is further classified using four different deep learning model: deep neural network, bidirectional-long short-term memory (BLSTM), convolution neural network (CNN) and CNN-LSTM. The model classification accuracy of different classifiers is reported to be 58%, 84%, 86% and 90%.

The activities recognition using gait was obtained in an open environment. All data is collected using an IMU sensor enabled with gyroscope, accelerometer and magnetometer in both offline and real-time activity recognition using gait. Both sensors showed their usefulness in empirical capability to capture a precised data during all seven activities. The inverse kinematics algorithm is solved to calculate the joint angle from spatial data for all six joints hip, knee, ankle of left and right leg.

This work helps to recognize the walking activity using gait pattern analysis. Further, it helps to understand the different joint angle patterns during different activities. A system is designed for real-time analysis of human walking activity using gait. A standalone real-time system has been designed and realized for analysis of these seven different activities.

The data is collected through IMU sensors for seven activities with equal timestamp without noise and data loss using wirelessly. The setup is useful for the data collection in an open environment outside the laboratory environment for activity recognition. The paper also presents the analysis of all seven different activity trajectories patterns.