Small Devices Research Articles

Jugaad Infrastructure: Minor infrastructure and the messy aesthetics of everyday life

AbstractJugaad is an Indian name for versatility and improvisation, a sensibility for improvisation, an ability for improvisation and an enabling of improvisation. This paper proposes the idea of Jugaad Infrastructure for versatile socio‐material infrastructure arrangements that inhabit and thrive in the messy aesthetics of everyday life. It does so by extending the focus of infrastructure geographies from ‘big stuff’ to little devices such as solar lamps that gain significance when deployed in big numbers. The paper advances two ideas. First, it argues that jugaad circumvents the formal–informal boundary set by designers. By piercing this boundary, jugaad affords more fluid socio‐material relationships involving infrastructures and their users. In so doing, jugaad affords versatility. Second, it develops the idea of Jugaad Infrastructure. Jugaad Infrastructure folds two things into it. First, infrastructures that are designed in ways that facilitate jugaad, albeit within firmly maintained boundaries and attempt to capitalise on people's aptitude for jugaad to take different forms, inhabit different spaces, enable different purposes and all this while somewhat retaining their shape. They are easy to maintain. This helps them travel to, function and stay in different places. In this way, small devices spread around in large numbers to become big infrastructure. Second, it represents the ensembles of fluid socio‐material relationships and resources involving infrastructures and their users through which infrastructures are tailored to ‘better’ fit everyday lives and needs. Jugaad Infrastructure inhabits the liminal spaces of struggle between designers claiming jugaad as a limited practice that leads to stable innovations and users deploying unlimited jugaadas an everyday practice of socio‐material flux. The paper is based on qualitative research conducted in India during 2012–2013, 2016 and 2017 using participant observations, discussions and interviews with users, entrepreneurs, market players and designers, in addition to documentary evidence from reports and websites.

Caries lesions diagnosis with deep convolutional neural network in intraoral QLF images by handheld device

ObjectivesThis study investigated the effectiveness of a deep convolutional neural network (CNN) in diagnosing and staging caries lesions in quantitative light-induced fluorescence (QLF) images taken by a self-manufactured handheld device.MethodsA small toothbrush-like device consisting of a 400 nm UV light-emitting lamp with a 470 nm filter was manufactured for intraoral imaging. A total of 133 cases with 9,478 QLF images of teeth were included for caries lesion evaluation using a CNN model. The database was divided into development, validation, and testing cohorts at a 7:2:1 ratio. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) were calculated for model performance.ResultsThe overall caries prevalence was 19.59%. The CNN model achieved an AUC of 0.88, an accuracy of 0.88, a specificity of 0.94, and a sensitivity of 0.64 in the validation cohort. They achieved an overall accuracy of 0.92, a sensitivity of 0.95 and a specificity of 0.55 in the testing cohort. The model can distinguish different stages of caries well, with the best performance in detecting deep caries followed by intermediate and superficial lesions.ConclusionsCaries lesions have typical characteristics in QLF images and can be detected by CNNs. A QLF-based device with CNNs can assist in caries screening in the clinic or at home.Trial registrationThe clinical trial was registered in the Chinese Clinical Trial Registry (No. ChiCTR2300073487, Date: 12/07/2023).

Long-Range Wireless Power Transfer for Moving Wireless IoT Devices

Wireless technologies are revolutionizing communications, with recent deployments, such as 5G, playing a key role in the future of the Internet of Things (IoT). Such progress is leading to an increasingly higher number of wirelessly connected devices. These require increased battery use and maintenance, consequently straining current powering solutions. Since most wireless systems rely on radiofrequency (RF) waves for communications and feature low-power technologies, it is increasingly feasible to develop and implement wireless power transfer solutions supported by RF. In this paper, a simultaneous wireless information and power transfer (SWIPT) solution targeting small mobile devices is presented. This solution uses beamforming to mitigate the path loss associated with the RF power propagation. It relies on an RF backscattering tracking algorithm to power moving devices. The feasibility to power wearable devices is demonstrated by tracking a walking individual (approximately 5 km/h) at a distance of 0.5 m while transferring a minimum of 6 dBm to a wearable device using 2 GHz RF signals. Simulations were used to determine the viability of such a solution to deliver useful power levels to a 1.2 × 1.4 m2 working area without exceeding specific absorption rate (SAR) limits.

Wave power calculation of a large periodic array of bottom-hinged paddle wave energy converters using Floquet–Bloch theory

In this study we consider a wave energy farm consisting of a large number of bottom-hinged paddles. The paddles are arranged in a doubly-periodic manner, with a finite number of identical rows and each row containing an infinite number of paddles. Each paddle is attached to its own damper and spring, allowing power to be generated through its pitching motion. Unlike previous studies into paddle arrays, we envisage compact arrays consisting of small devices arranged across a large number of rows. The mathematical analysis of this proposed configuration is best suited to methods that exploit the periodicity of the array. It is shown that the velocity potential everywhere within the array can be described by an expansion in terms of suitably-defined Floquet–Bloch eigenmodes and this allows the solution to the scattering problem involving waves incident upon the array to be determined by a simple low-order system of equations. The method used in this study is exact within the setting of linearised water waves and has all of the advantages of classical low-frequency multi-scale homogenisation without the low-frequency restriction. It may also be regarded as a natural extension of the well-known wide-spacing approximation without the large separation restriction. Although the focus of this paper is on the mathematical approach to the solution of the problem, we provide a range of results to illustrate the performance of this proposed wave energy converter concept.

Using proximity loggers in studies of sheep behavior: an estimation of the daily frequency of water consumption

BackgroundProximity loggers are a biotelemetry technology used to quantify animal–animal interactions non-invasively. They are small devices can be affixed to a target animal and detects and records if another device is within a certain distance, which can provide unprecedented insights into individual behavior and interactions with other animals or other aspects of the environment. Proximity loggers also can record the duration and distance between the two or more individuals detected, and have been used to study a diverse array of interactions. Studying drinking behavior in sheep is important for several critical reasons, such as improving animal welfare, enhancing productivity, managing resources efficiently, and ensuring sustainable agricultural practices. The objective of this study was to test the capacity for Bluetooth Low Energy proximity loggers to quantify drinking habits in sheep (n = 7 adult females). At all times, they had access to water from two drinking troughs. Animals wore neck collars that contained a mobile logger, and two stationary loggers were placed under the drinking troughs. To store the records downloaded from the stationary loggers, a gateway was placed close to the troughs. The mobile loggers were programmed as beacons to transmit their ID to the stationary loggers, which were programmed as receivers to scan the proximity of the mobile loggers, record the time, the IDs of the mobile loggers, and the received signal strength of the contact with the mobile loggers.ResultsEwes visited the drinking troughs 2.4 ± 0.5 times per day (rank 1.4–2.9), most frequently (42%) between 0800 and 1000 h, and 84% occurred in the daytime. Mean length of each water intake was 1.72 ± 0.30 min. The cosinor analysis of the distribution of drinking events revealed a significant (P < 0.0001) 24-h circadian rhythm in drinking events, and the acrophase (time of the highest proportion of events) occurred at 1054 h.ConclusionsConsidering that studying water intake in sheep is crucial for optimizing their health, welfare, and management, the proximity loggers were demonstrated to be appropriate for documenting the drinking habits of sheep, and probably, would be suitable for studies of other behaviors that involve approaching other animals or objects.

Feature engineering and machine learning for electrochemical detection of rabies virus in graphene-based biosensors

Electrochemical biosensors are small analytical devices that convert a biological response into a processable signal with high sensitivity, ease of operation, cost-effectiveness, and miniaturization capability. The current study proposes enhancing the reliability of the electrochemical detection of enveloped viruses, such as the rabies virus using feature engineering and machine learning. Portable detection was achieved using a graphene-based microfluidic sensor and the current was recorded from electrochemical experiments using a portable potentiostat. After data mining and feature engineering, a dataset obtained from staircase cyclic voltammetry was used in different machine learning models. The features from the voltammogram were extracted following theoretic aspects to build a dataset with uncovering patterns and valuable information. Correlation-based and recursive feature elimination algorithms were used for feature engineering. Our analysis showed that the best F-measure score a model trained was obtained through a support vector machine with 0.9830 for the diagnostic tasks following the proposed feature engineering pipeline. On the other hand, the preprocessing pipeline which is commonly used in many studies obtained a score of 0.9394 using a decision tree model. In addition, the proposed method generates features with higher interpretability and lower model complexity than conventional feature reduction methods such as PCA. Our study shows the potential of machine learning models in the field of electrochemical biosensor development with a focus on feature engineering based on electroanalytical analysis, which can be extended to develop new tools for disease diagnosis in the future.

EMPLOYING ELECTROMAGNETIC ACTUATOR TO HARVEST ENERGY THROUGH A VARIABLE-LENGTH PENDULUM

This article explores the application of electromagnets in energy harvesting via a variable-length pendulum system. Harnessing the principles of electromagnetism, the study investigates the efficiency and feasibility of utilizing electromagnetic forces to extract energy from the motion of a pendulum with variable length, employing sinusoidal excitation as a means for vibrating machinery. This approach enables a more significant number of oscillations, consequently leading to a higher power output. The research investigates various aspects, including the design, implementation, and performance analysis of the electromagnet-based energy harvesting system. Through theoretical modeling, the article provides insights into the potential of electromagnets to generate sustainable energy from oscillatory motion. The findings show that up to 0.25W can be generated, providing power for small devices such as phone chargers and sensor units. The findings contribute to advancing renewable energy technologies and offer promising avenues for developing efficient and environmentally friendly energy harvesting mechanisms.

Loop Closure Detection with CNN in RGB-D SLAM for Intelligent Agricultural Equipment

Loop closure detection plays an important role in the construction of reliable maps for intelligent agricultural machinery equipment. With the combination of convolutional neural networks (CNN), its accuracy and real-time performance are better than those based on traditional manual features. However, due to the use of small embedded devices in agricultural machinery and the need to handle multiple tasks simultaneously, achieving optimal response speeds becomes challenging, especially when operating on large networks. This emphasizes the need to study in depth the kind of lightweight CNN loop closure detection algorithm more suitable for intelligent agricultural machinery. This paper compares a variety of loop closure detection based on lightweight CNN features. Specifically, we prove that GhostNet with feature reuse can extract image features with both high-dimensional semantic information and low-dimensional geometric information, which can significantly improve the loop closure detection accuracy and real-time performance. To further enhance the speed of detection, we implement Multi-Probe Random Hyperplane Local Sensitive Hashing (LSH) algorithms. We evaluate our approach using both a public dataset and a proprietary greenhouse dataset, employing an incremental data processing method. The results demonstrate that GhostNet and the Linear Scanning Multi-Probe LSH algorithm synergize to meet the precision and real-time requirements of agricultural closed-loop detection.

The smartphone that cannot be abandoned - the phenomenon of nomophobia among students

Digital technologies are widespread and common in modern life and are a fundamental form of human communication. The smartphone, a small device that can easily fit in a pocket, makes it possible to meet many of our daily needs. Although a smartphone makes people’s life easier, it also often becomes a source of anxiety, which in the long term can negatively affect psychological well-being. There can also be other consequences of nomophobia, for example, excessive attachment to the smartphone can become the cause of a systematic reduction of the real presence of other people in our lives. In this way, communication and dialogue with another person are disrupted and may even cease to exist at all. The purpose of the research was to identify the phenomenon of nomophobia among students. Differences were sought in the severity of nomophobia by gender, type of sport practised (individual versus team), taking up or not taking up professional activity, and correlations between the individual dimensions of nomophobia. A total of 102 students at the Józef Piłsudski University of Physical Education in Warsaw studying Sport participated in the study, including 23 women and 79 men. The average age of the students was 19.3 years. There were 80 people in the study group who practise sport. The average training experience was 8.5 years. The Nomophobia Questionnaire (NMP-Q) (Yildirim, Correia, 2015) was used to collect empirical data. Gender differences were observed in terms of the overall nomophobia rate and one of its dimensions, which is the inability to communicate. Significantly higher scores were obtained by the female students surveyed. Factors that do not differentiate the severity of nomophobia among the students surveyed are: the type of sport played (individual versus team), taking up or not taking up a job. There are positive correlations between the individual dimensions of nomophobia.

Advanced Volatile Memory Forensics through Autopsy Integration

The main goal of this study is to design a novel plugin for the Autopsy forensic framework to enable forensic analysts to identify and extract volatile memory from small-scale digital devices. This includes network peripherals and Internet of Things devices, smartphones, and industrial-control systems. Given the importance of volatile memory to digital crime and cybersecurity investigations, an accurate and reliable tool is needed to non-destructively acquire forensic copies of the evidence. In the context of small-scale devices, this study is of acute importance to bridge the gap that exists in current forensic research and forensic practice, using separate tools can be challenging due to compatibility issues and the complexity of managing multiple system. In conclusion, the developed Autopsy plugin, which has been termed the MemoryIntegrator, seamlessly harmonizes with Autopsy forensic framework and is designed to work together with Volatility tool, specializing in detailed memory analysis. Consequently, the following main outcomes result from the experimentation and application of the plugin: Promotes the default forensic activity of Autopsy by providing the analysts with a way to swiftly and directly harvest and evaluate volatile data from diverse small scale digital devices. The implementation of the plugin ensures that the integrity of the memory data is maintained throughout the extraction and analysis process. This is facilitated by cryptographic hash validations that confirm that there are no changes in the data from the extraction to the point of analysis. The plugin maintains the integrity of the memory data from the time of extraction to the time of analysis using cryptographic hash validations which verifies that these data has not been manipulated at this point. MemoryIntegrator outmatched all the forensic tools herewith because conducting forensic test back at home verified its superiority in terms of the extraction of data from memory speed and the authenticity and formula which it uses in analysis. In the modern world, this is critical to investigate digital crimes and incidences that affect cybersecurity.

Preparation of Au–Ni bimetallic-coated monodisperse PS@Ni/Au microspheres with excellent conductivity and their application in acrylate-based anisotropic conductive adhesives

Anisotropic conductive adhesives (ACAs) provide an efficient and easy method of interconnecting small electronic devices compared to traditional solder joint connections. In this paper, PS@Ni/Au metal composite microspheres with excellent electrical conductivity in the size of 2.76 μm were prepared by chemical plating technique and in situ redox method. The effects of PS@Ni metal composite microspheres with different shell thicknesses and compactness on the preparation of Ni–Au bimetallic shell layers were investigated, and the effects of different Au:PS@Ni mass ratios on the volumetric conductivity of PS@Ni/Au metal composite microspheres were discussed. The composite microspheres were characterized by scanning electron microscopy (SEM), thermogravimetric analysis (TGA), energy spectrometry (EDS), and X-ray diffraction (XRD). Thick and compact Ni metal shells are the key to preparing PS@Ni/Au metal composite microspheres with the complete degree of encapsulation. When the mass ratio of Au:PS@Ni was 0.15:1, the conductivity was as high as 1.3 × 105 S cm−1. The acrylate pressure-sensitive containing 3.1–8.3 vol% conductive particles presented an obvious anisotropy. When the mass ratio of the conductive particles was higher than 8.3 vol%, the conductive particles appear to be significantly aggregated. The research ideas and methods have great application potential for replacing the traditional solder joint connections.

HT-WSO: A hybrid meta-heuristic approach-aided multi-objective constraints for energy efficient routing in WBANs

Generally, Wireless Body Area Networks (WBANs) are regarded as the collection of small sensor devices that are effectively implanted or embedded into the human body. Moreover, the nodes included in the WBAN have large resource constraints. Hence, reliable and energy-efficient data transmission plays a significant role in the implementation and in constructing of most of the merging applications. Regarded to complicated channel environment, limited power supply, as well as varying link connectivity has made the construction of WBANs routing protocol become difficult. In order to provide the routing protocol in a high energy-efficient manner, a new approach is suggested using hybrid meta-heuristic development. Initially, all the sensor nodes in WBAN are considered for experimentation. In general, the WBAN is comprised of mobile nodes as well as fixed sensor nodes. Since the existing models are ineffective to achieve high energy efficiency, the new routing protocol is developed by proposing the Hybrid Tunicate-Whale Swarm Optimization (HT-WSO) algorithm. Subsequently, the proposed work considers the multiple constraints for deriving the objective function. The network efficiency is analyzed using the objective function that is formulated by distance, hop count, energy, path loss, and load and packet loss ratio. To attain the optimum value, the HT-WSO derived from Tunicate Swarm Algorithm (TSA) and Whale Optimization Algorithm (WOA) is employed. In the end, the ability of the working model is estimated by diverse parameters and compared with existing traditional approaches. The simulation outcome of the designed method achieves 13.3%, 23.5%, 25.7%, and 27.7% improved performance than DHOA, Jaya, TSA, and WOA. Thus, the results illustrate that the recommended protocol attains better energy efficiency over WBANs.

The Educational Recommendation System with Artificial Intelligence Chatbot: A Case Study in Thailand

The educational recommendation system with an artificial intelligence (AI) chatbot is a system designed to operate on smartphones and is intended for use at the College of Industrial Technology in Thailand. Compatible with both iOS and Android operating systems, the educational recommendation system in this research is capable of providing educational recommendations and valuable information about the engineering degree program. It includes information about the Electronics Engineering Technology Department, relevant bodies of knowledge that can enhance awareness and public relations, and a proactive introduction to information. The main objectives of this study are to develop the capacity to analyze educational issues and provide useful and accurate information for educational recommendations, as well as to explore the possibilities for enhancing this system. The results of the study address the three research questions and demonstrate that the process and structure of the educational recommendation system with an AI chatbot can be utilized to create educational recommendation tools in higher education. These tools are accessible anywhere and at any time through small mobile devices. The system is also compatible with popular social media applications and is capable of analyzing user questions precisely and accurately to find the optimal answers that can meet the user’s needs. However, there are still gaps in the research that need to be investigated in the future. These studies should include exploring a broader range of applications among the general population and educational settings to assess the effectiveness of the educational recommendation system with an AI chatbot. Additionally, they should examine the skills that impact the utilization of educational technology in various contexts.

Effect of molecular rotation and concentration on the adsorption of pentacene molecules on two-dimensional monolayer transition metal dichalcogenides

Heterostructures composed of pentacene (PEN) molecules and transition metal dichalchogenides (TMDs) are promising materials for small, flexible and lightweight photovoltaic devices and various other optoelectronic applications. The effects of changing concentration and orientation of adsorbed PEN molecules on two-dimensional monolayer substrates of TMDs, namely MoS2, MoSe2, WS2 and WSe2, were investigated using first-principles calculations based on density functional theory. We examined the structural and electronic properties of the corresponding PEN/TMD heterostructures and compared these between differing PEN concentrations and the orientations of PEN with respect to the underlying substrate crystal structure. We analyze the band alignment of the heterostructures and demonstrate a concentration-dependent staggered-to-straddling (typeII-I) band gap transition in PEN/MoSe2.

Medium access control protocol based on time division multiple access scheme for wireless body area network

In recent years, the demand for wireless body area network (WBAN) technology has increased, driven by advancements in medical and healthcare applications. WBAN consists of small, low-power, and heterogeneous sensor devices attached inside or outside the body for continuous health monitoring. Medium access control (MAC) is pivotal in addressing WBAN challenges by ensuring reliability and energy efficiency under a dynamic environment caused by body movement. Therefore, to tackle these challenges, this paper presents a MAC protocol based on time division multiple access (TDMA) to enhance the WBAN performance. The proposed TDMA-MAC protocol employs a one-periodic scheduled-based access method to provide reliable data transmission while satisfying the WBAN requirements. The proposed protocol is compared to the IEEE 802.15.6 MAC, enhanced packet scheduling algorithm MAC (EPSA-MAC), and concurrent MAC (C-MAC) protocols based on the performance metrics of packet delivery ratio (PDR), network throughput, energy consumption, and average delay. The simulation results show that the TDMA-MAC protocol outperforms its competitors as it could achieve up to 98% PDR, 30% enhanced throughput, 30% energy optimization, and 20% improvement in average delay.

Optimisation of Double Floating Bodies for Small Unmanned Marine Devices Based on a New Hydraulic Flexible WEMP System

Optimisation of Double Floating Bodies for Small Unmanned Marine Devices Based on a New Hydraulic Flexible WEMP System

High-performance flexible magnetic textile fabricated using porous Juncus effusus fiber for biomechanical energy harvesting

Mechanical energy produced by human motion is ubiquitous, continuous, and usually not utilized, making it an attractive target for sustainable electricity-harvesting applications. In this study, flexible magnetic Juncus effusus (M-JE) fibers were prepared from plant-extracted three-dimensional porous Juncus effusus (JE) fibers decorated with polyurethane and magnetic particles. The M-JE fibers were woven into fabrics and used for mechanical energy harvesting through electromagnetic induction. The M-JE fabric and induction coil, attached to the human wrist and waist, yielded continuous and stable voltage (2 V) and current (3 mA) during swinging. The proposed M-JE fabric energy harvester exhibited good energy harvesting potential and was capable of quickly charging commercial capacitors to power small electronic devices. The proposed M-JE fabric exhibited good mechanical energy harvesting performance, paving the way for the use of natural plant fibers in energy-harvesting fabrics.

The use of gadgets in the student environment and their impact on the health of students

In the information 21st century, almost everyone interacts with technical devices, including gadgets. A gadget is a small device designed to make life easier and better. Gadgets most often include smartphones, computers, tablets, headphones, smartphone speakers, smart watches and much more. In this article, we will focus on the most common gadgets - a smartphone and a computer, and also consider their effect on the student's body. The results of a social survey of students of the Admiral F. F. Ushakov State Maritime University on the indicator of smartphone screen time used and their well-being from this are presented. The authors show that the abuse of time spent in gadgets negatively affects the state of both physical and mental health of students. The article provides recommendations for a painless interaction.

Detection of Buried Nonlinear Targets Using DORT

Ground-penetrating radars (GPR) based on a variety of techniques have been proposed to improve the performance of buried target (e.g., landmines, threat devices) detection. However, the small radar cross section (RCS) of small electronic devices poses difficulties for target detection, especially when they are buried in lossy and inhomogeneous media. This paper presents a novel buried nonlinear target detection method based on the decomposition of the time-reversal operator (DORT) that uses a multistatic system to overcome the limitations of conventional GPR. Using harmonic radar, which detects the harmonic responses scattered from electronic devices, and DORT processing, which enables focusing/imaging of the detected target, the detection performance is verified by conducting simulation and measurements. The overall results demonstrate that the proposed method achieves accurate detection of buried targets with small RCS.

Internal Nuclear Propulsion System for Future Space Applications

Internal nuclear pulse propulsion (INPP) represents a novel approach to space propulsion, where controlled nuclear detonations occur within the spacecraft itself, offering potential advantages in safety and propulsion control compared to conventional external detonation designs like Project Orion. INPP relies on the same fundamental principles of nuclear pulse propulsion, utilizing the momentum generated by nuclear explosions to propel the spacecraft forward. Proposed INPP designs involve strategically placing small nuclear devices within the spacecraft's structure, detonating them in sequence to produce controlled impulses for propulsion. However, significant engineering challenges, such as radiation shielding, precise detonation control, and safety considerations, must be addressed to realize the full potential of INPP. Despite these hurdles, ongoing research and development efforts aim to harness the transformative capabilities of INPP, promising to revolutionize space exploration by enabling efficient and rapid interstellar travel while ensuring the safety of spacecraft and crew.