Small Mobile Devices Research Articles

Applications of vibrational phenomena to evaluate the effect of hydrogen solution on the elasticity and fatigue properties of industrial metal plates

Understanding resonance phenomena in materials is crucial in many fields, from controlling noise in small mobile devices to designing seismic-safe large structures or in energy harvesting. Resonance depends on material elasticity and its shape, influenced by temperature. While controlling elastic properties can control vibrations, seeking practical alternatives is essential. This research focuses on introducing hydrogen into industrial metal materials as a method to control resonance conditions, which is particularly relevant in the context of the upcoming hydrogen society. Controlled amounts of hydrogen were introduced to industrial metal plates (Ti, W) using electrochemical methods to investigate changes in their resonance characteristics. The results indicate that the introduction of hydrogen into Ti resulted in a maximum 20% reduction in Young's modulus, while W exhibited a 10% decrease. Additionally, the recovery of their modulus by hydrogen desorption was confirmed. This confirms that the control of hydrogen allows the manipulation of the modulus of elasticity at constant temperature, controlling the resonance without changing the dimensions. Furthermore, similar evaluations were performed on high hardness steel. The results showed fatigue failure due to vibration, increased Young's modulus with the introduction of hydrogen, and an observed trend toward decreased life to failure.

Thermoelectric generators versus photovoltaic solar panels: Power and cost analysis

In the current study, the concept of building a power plant using thermoelectric generator (TEG) modules is investigated, both technically and economically. The hypothesized thermoelectric generation power plant is a modular system, consisting of a large array of electrically connected thermoelectric generator units for generating clean electricity with-out greenhouse gas (GHG) emissions, noise, or hazardous solid wastes. The envisioned thermoelectric generation power plant (TEGPP) considered here is assumed to utilize solar radiation as a heat source, and water as a heat sink. The viability of such a concept is examined in the current study based on available specifications of a high-output thermo-electric generator module released in the market (TEG1-24111-6.0). Benchmarking is car-ried out considering a high-efficiency photovoltaic (PV) panel in the market (SunPower SPR-MAX3-400), assuming that it operates under standard solar radiation of 1,000 W, and with a standard panel temperature of 25 C, causing it to give an output electric power of 400 W (DC or direct current). It is found that in order to have an electric power of a thermoelectric generator unit similar to that of a photovoltaic panel of equal surface area, the temperature at the hot side of the thermoelectric generator unit should be about 70 C if the cold-side temperature is 30 C. However; under this output power equiva-lence, the price of the thermoelectric generator unit is about 90 times that of a photovol-taic panel of equal size (based on prices of October 2023). At an elevated hot-side tem-perature of 300 C for the thermoelectric generator unit (with the cold-side temperature being still 30 C), the thermoelectric generator unit can generate electric power that is about 25 times the power generated by a photovoltaic panel of an equal geometric area. This big boost in the output power still does not counteract the large cost difference be-tween the thermoelectric generator technology and the photovoltaic technology, where the per-watt(electric) cost in the case of thermoelectric generators is 3.5 times its value in the case of photovoltaic panels. Thus, the TEG technology in its intensified generation mode is still relatively more expensive compared to the PV technology. The practical im-plications of the current study are excluding the thermoelectric generation (based on the Seebeck effect) concept from large-scale commercial power plants, and viewing thermoe-lectric generators as sources of small electric power for waste heat management or con-venient power sources for small mobile devices.

DFLM-YOLO: A Lightweight YOLO Model with Multiscale Feature Fusion Capabilities for Open Water Aerial Imagery

Object detection algorithms for open water aerial images present challenges such as small object size, unsatisfactory detection accuracy, numerous network parameters, and enormous computational demands. Current detection algorithms struggle to meet the accuracy and speed requirements while being deployable on small mobile devices. This paper proposes DFLM-YOLO, a lightweight small-object detection network based on the YOLOv8 algorithm with multiscale feature fusion. Firstly, to solve the class imbalance problem of the SeaDroneSee dataset, we propose a data augmentation algorithm called Small Object Multiplication (SOM). SOM enhances dataset balance by increasing the number of objects in specific categories, thereby improving model accuracy and generalization capabilities. Secondly, we optimize the backbone network structure by implementing Depthwise Separable Convolution (DSConv) and the newly designed FasterBlock-CGLU-C2f (FC-C2f), which reduces the model’s parameters and inference time. Finally, we design the Lightweight Multiscale Feature Fusion Network (LMFN) to address the challenges of multiscale variations by gradually fusing the four feature layers extracted from the backbone network in three stages. In addition, LMFN incorporates the Dilated Re-param Block structure to increase the effective receptive field and improve the model’s classification ability and detection accuracy. The experimental results on the SeaDroneSee dataset indicate that DFLM-YOLO improves the mean average precision (mAP) by 12.4% compared to the original YOLOv8s, while reducing parameters by 67.2%. This achievement provides a new solution for Unmanned Aerial Vehicles (UAVs) to conduct object detection missions in open water efficiently.

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.

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.

Research on the design and balancing strategy with multi-section lithium battery protection IC

This study uses 0.5um BCD technology to design a multi section lithium battery protection chip suitable for small mobile electronic devices such as Bluetooth earphones and smart bracelets. By providing a complete system structure diagram, the principles and design methods of several key circuits in the chip were introduced. This chip can effectively monitor the voltage of lithium batteries, take protective measures against abnormal situations such as overcharging, discharging, and overcurrent, and has a balancing module with dynamic power consumption management function. The balancing module adopts a passive balancing architecture, and the balancing algorithm used is an average based voltage detection algorithm. Using this algorithm can make the balancing system structure simple and reduce system power consumption, reduce the differences between batteries within the group, and thus improve the uniformity of the battery pack. The overcharging detection accuracy of the designed chip is ±25mV, the overcharging detection accuracy is 50mV, and the static working current of the chip is 7uA.

A Lightweight Remote Sensing Aircraft Object Detection Network Based on Improved YOLOv5n

Due to the issues of remote sensing object detection algorithms based on deep learning, such as a high number of network parameters, large model size, and high computational requirements, it is challenging to deploy them on small mobile devices. This paper proposes an extremely lightweight remote sensing aircraft object detection network based on the improved YOLOv5n. This network combines Shufflenet v2 and YOLOv5n, significantly reducing the network size while ensuring high detection accuracy. It substitutes the original CIoU and convolution with EIoU and deformable convolution, optimizing for the small-scale characteristics of aircraft objects and further accelerating convergence and improving regression accuracy. Additionally, a coordinate attention (CA) mechanism is introduced at the end of the backbone to focus on orientation perception and positional information. We conducted a series of experiments, comparing our method with networks like GhostNet, PP-LCNet, MobileNetV3, and MobileNetV3s, and performed detailed ablation studies. The experimental results on the Mar20 public dataset indicate that, compared to the original YOLOv5n network, our lightweight network has only about one-fifth of its parameter count, with only a slight decrease of 2.7% in mAP@0.5. At the same time, compared with other lightweight networks of the same magnitude, our network achieves an effective balance between detection accuracy and resource consumption such as memory and computing power, providing a novel solution for the implementation and hardware deployment of lightweight remote sensing object detection networks.

Parallel-pipelined-memory Blowfish FPGA-based radio system with improved power-throughput for secured IoT network

The Internet of Things (IoT) has emerged as a disruptive force, transforming industries, cities, and daily life in a time of unprecedented connection. This outcome has called for robust security, reliability, and efficient power management for the IoT network. The security of small mobile devices within the IoT network has become a pressing concern due to the multitude of wireless vulnerabilities that threaten data transmission, the harsh wireless environment for data transmission, and the need for low-power consumption devices. This study proposed the development of a new parallel-pipelined-memory (P2M) Blowfish field programmable gate array (FPGA)-based radio system as a prototype of a secured mobile device for IoT application. The FPGA platform verified the proposed system, indicating a minimum of 64 % power-throughput improvement. In this case, the hardware utilisation was reduced by 3.5 % compared to the recently published works. Meanwhile, the real-time transmission analysis of the suggested P2M Blowfish radio system in a non-line-of-sight (NLOS) indoor environment indicated that the transmitted data over 2.4 GHz ZigBee standard at 10 dBm radio frequency (RF) power level demonstrated the optimum signal quality with received signal strength and signal-to-noise ratio (SNR) of −34.58 dBm and 39.06 dB, respectively. This feature also included a 6.25 × 10−3 minimum bit error rate (BER) at 61 m as the maximum communication range. Thus, the P2M Blowfish FPGA-based radio system could improve the security, reliability, and power efficiency of IoT networks.

Artificial Intelligence Chatbot Platform: AI Chatbot Platform for Educational Recommendations in Higher Education

AI chatbots platform for educational recommendations in higher education is a tool intended to promote learning through the “Line” application, an application which is compatible with a variety of responsive web designs. This platform can be used to assess the decision to study in higher education based mainly on the user’s aptitudes. Consequently, this study explores the perspectives with regard to the development of the AI chatbot platform and the assessment of the decision to study in higher education. The study is mainly related to the use of AI technologies and digital innovations with the aim of creating a decision support tool that can operate on a small mobile device and can be accessed anywhere and anytime, for admission to the Railway Technical School, Bangkok, Thailand. The participants in this research are divided into two groups: 1) six experts chosen by means of purposive sampling, all of whom are from different higher education institutions and who specialize in information system development, and 2) 65 students interested in studying at the Railway Technical School, all of whom were chosen by means of voluntary selection. The participants in both groups are well protected in terms of confidentiality and anonymity; in other words, the identities of the participants were neither indicated nor mentioned in the research instruments. The research results show that: 1) The overall efficiency of the AI chatbot platform in terms of three aspects (function requirement, functions, and usability) were assessed as being at a very high level. 2) According to the results of decision assessment based on the aptitudes of AI chatbot platform users, 86.15% of the said users made their decision based on the suggestions of the platform. 3) The satisfaction with regard to the AI chatbot platform is at very high level. According to the above results based on the analysis of the users’ aptitudes and abilities, it was found that the AI chatbot platform can be used as a tool for educational recommendations in higher education. The platform is also able to analyze users’ questions accurately and precisely in order to provide answers that meet the users’ needs.

Analysis on bimodal complex permeability spectrum of a noise suppression sheet and single constituent flakes

In recent years, highly integrated active components inside small mobile communication devices have led to serious electromagnetic interference problems. The countermeasure to this problem is covering a noise source with a noise suppression sheet (NSS) which is composed of ferromagnetic flakes and polymers. The NSS absorbs effectively electromagnetic waves due to a large imaginary part of relative permeability μr″ at the specific frequency region. This function is attributed to the bimodal μr″ spectrum of NSS, but its physical origin has not been revealed so far. In this study, the bimodal μr″ spectrum of NSS has been analyzed and its physical mechanism has been discussed. The μr″ spectrum of a constituent single magnetic flake was measured by using a newly developed high-sensitivity permeability measurement system, which showed a bimodal spectrum quite similar to that of NSS. This fact indicates that the physical origin of the bimodal μr″ spectrum is attributed to the magnetization dynamics of each single constituent flake. After measuring the μr″ spectra of single flakes with various materials, such as Fe, Fe-Al, Fe-Co-V, and Fe-Si-Al alloys, these bimodal μr″ spectra were quantitatively explained by the magnetization dynamics of the flux closure states inside the flake. The magnetic domain structure of a single flake was observed by a micro-beam X-ray magnetic circular dichroism microscopy. The result confirmed that the segmented magnetic domains form the local flux closure states inside the flake.

Solar-Powered Coin-Operated Mobile Charging Station for Sustainable Energy Access and Resilience

This study centers on the creation of a cutting-edge coin-operated mobile gadget charging station, harnessing the inexhaustible power of solar energy via an integrated storage battery. The primary objective is to champion solar energy as a sustainable off-grid power source with boundless growth potential. Moreover, this innovative solution extends its utility to the commercial realm, where users can access it for a nominal fee, ensuring their mobile gadgets remain charged for specified periods. In addition to its commercial viability, this charging station also serves as a crucial lifeline during unforeseen calamities and prolonged power outages, offering an essential emergency charging station. Its self-sufficient design allows it to continually charge mobile devices in the presence of sunlight. Whether it's day or night, the coin-operated mobile gadget charging station stands ready to power up small electronics and mobile devices, ensuring uninterrupted connectivity.

Lightweight multi-scale attention-guided network for real-time semantic segmentation

The wide application of small mobile devices makes the demand for lightweight real-time semantic segmentation algorithm become more and more intense, which makes it become one of the most popular research topics in the field of computer vision. However, some current methods blindly pursue low parameter numbers and high inference speeds, resulting in excessively low model accuracy and a loss of practical value. Therefore, a lightweight multi-scale attention-guided network for real-time semantic segmentation(LMANet) based on asymmetric encoder-decoder is proposed in this paper to solve the above dilemmas. In the encoder, we propose multi-scale asymmetric residual(MAR) modules to extract local spatial information and context information to enhance feature expression. In the decoder, we design an attention feature fusion(AFF) module and an attention pyramid refining (APR) module. AFF module guides the fusion of low-level and middle-level feature information through high-level semantic information, and finally refines the fusion result through APR module. In addition, we improve the segmentation performance of the model with the help of the attention modules in the network. Our network is tested on two complex urban road datasets. The experimental results show that LMANet achieves 70.6% mIoU and 66.5% mIoU on Cityscapes and Camvid datasets at 112FPS and 333FPS respectively, only 0.95 M parameters without any pre-training or pre-processing. Compared with most of existing state-of-the-art models, our network not only guarantees reasonable inference speed and parameter quantity, but also improves the accuracy as much as possible, which makes it more practical.

A comprehensive review on battery thermal management system for better guidance and operation

AbstractBatteries are essential to mobilization and electrification as they are used in a wide range of applications, from electric vehicles to small mobile devices. All these devices are powered with AC or DC inside their systems, so they require different battery systems depending on their technical requirements. Batteries show unique characteristics depending on their types, and their needs vary based on their performance, ambient conditions, and so forth. One of the main demands for them is thermal stability. For batteries, thermal stability is not just about safety; it's also about economics, the environment, performance, and system stability. This paper has evaluated over 200 papers and harvested their data to build a collective understanding of battery thermal management systems (BTMSs). These studies are specifically designed to solve different problems. This paper has been prepared to show what these systems are, how they work, what they have been designed for, and under what conditions they should be applied. The BTMSs have been evaluated based on their method, method tools, discharge rate, maximum temperature, temperature difference values, and ambient and inlet temperatures. After evaluating over 200 studies, the results indicate that the passive BTMSs are not useful the cases where the temperature reaches higher values suddenly, especially for system systems that require higher discharge rates. On the other hand, active cooling methods do not manage the temperature difference in the battery cells. However, hybrid cooling methods address both cases admirably by compensating for both of their weaknesses and bringing out their advantages. The general optimum temperature for lithium battery batteries is 55°C. Even though there are many other parameters that need to be considered before making a decision for a BTMS design, the best performance for an optimum system seems to be methods 34, 38, and 22 as they are able to provide lower maximum temperature and temperature difference in the cells.

Hybrid Aerogel Triboelectric Nanogenerator Based on the Synergistic Effect of Solid-Solid/Gas-Solid Triboelectricity and Piezoelectric Polarization.

Aerogels, due to their unique interconnected 3D networks, and large number of air-filled pores, extend the structural characteristics and physicochemical properties of the nanoscale to the macro level. However, aerogels made from a single component can hardly meet the needs of multifunctional energy harvesting/supply situations. Here, a BaTiO3-based hybrid aerogel (BTO HA) with 3D network structure was prepared. When the BTO HA is used as the electrode of triboelectric nanogenerator (BTO HA-TENG), high electrical output performances were obtained, which is due to the synergistic effect of solid-solid contact electrifications between the two electrification layers, the gas-solid contact electrifications between the inner surface of BTO HA and the air filled in the aerogel pores, and the piezoelectricity of the doped BaTiO3 nanoparticles. The BTO HA-TENG exhibited excellent fatigue resistance and structural stability after 12,000 cycles of alternatively contact/separation tests, and it can not only provide stable power supply for commercial capacitors, drive small mobile electronic devices but also can be used as a self-powered sensor to monitor human motion signals. Compared with traditional TENGs depending on surface charge transfer, the BTO HA-TENG exhibited its unique advantage that it can generate and transfer triboelectric charges by 3D volume, which boost TENGs' electrical output performances.

Distill-DBDGAN: Knowledge Distillation and Adversarial Learning Framework for Defocus Blur Detection

Defocus blur detection (DBD) aims to segment the blurred regions from a given image affected by defocus blur. It is a crucial pre-processing step for various computer vision tasks. With the increasing popularity of small mobile devices, there is a need for a computationally efficient method to detect defocus blur accurately. We propose an efficient defocus blur detection method that estimates the probability of each pixel being focused or blurred in resource-constraint devices. Despite remarkable advances made by the recent deep learning-based methods, they still suffer from several challenges such as background clutter, scale sensitivity, indistinguishable low-contrast focused regions from out-of-focus blur, and especially high computational cost and memory requirement. To address the first three challenges, we develop a novel deep network that efficiently detects blur map from the input blurred image. Specifically, we integrate multi-scale features in the deep network to resolve the scale ambiguities and simultaneously modeled the non-local structural correlations in the high-level blur features. To handle the last two issues, we eventually frame our DBD algorithm to perform knowledge distillation by transferring information from the larger teacher network to a compact student network. All the networks are adversarially trained in an end-to-end manner to enforce higher order consistencies between the output and the target distributions. Experimental results demonstrate the state-of-the-art performance of the larger teacher network, while our proposed lightweight DBD model imitates the output of the teacher network without significant loss in accuracy. The codes, pre-trained model weights, and the results will be made publicly available.

Мобилография – медиадағы жаңа құбылыс

Today's media not only spread information, but also create interesting news that we can watch, listen to and read by viewing our audiovisual creations using our small mobile devices. This trend usually coincides with modern searches in the media sphere. Therefore, mobile journalism is gaining momentum not only abroad, but also in Kazakhstan. Young journalists who are passionate about the possibilities of new media, and those who want to work in the field of media in the future, react especially to this. They prove that, as before, there is no need to sit in front of large portable computers, monitors. Through their small mobile devices in their hands, they can shoot, edit and create high-quality media products. Our article says that mobilography is one of such modern phenomena. We can say that this is a new direction in electronic journalism. At first it appeared as a genre of photography and in a short time gained popularity among millions of amateur photographers. And this was followed by skeptical interest, creating an image among photography professionals. Now, in this process, television filming has also begun to be done.

Using Artificial Intelligence-Enhanced Sensing and Wearable Technology in Sports Medicine and Performance Optimisation.

Wearable technologies are small electronic and mobile devices with wireless communication capabilities that can be worn on the body as a part of devices, accessories or clothes. Sensors incorporated within wearable devices enable the collection of a broad spectrum of data that can be processed and analysed by artificial intelligence (AI) systems. In this narrative review, we performed a literature search of the MEDLINE, Embase and Scopus databases. We included any original studies that used sensors to collect data for a sporting event and subsequently used an AI-based system to process the data with diagnostic, treatment or monitoring intents. The included studies show the use of AI in various sports including basketball, baseball and motor racing to improve athletic performance. We classified the studies according to the stage of an event, including pre-event training to guide performance and predict the possibility of injuries; during events to optimise performance and inform strategies; and in diagnosing injuries after an event. Based on the included studies, AI techniques to process data from sensors can detect patterns in physiological variables as well as positional and kinematic data to inform how athletes can improve their performance. Although AI has promising applications in sports medicine, there are several challenges that can hinder their adoption. We have also identified avenues for future work that can provide solutions to overcome these challenges.

Opto-ultrasound biosensor for wearable and mobile devices: realization with a transparent ultrasound transducer.

Mobile and wearable healthcare electronics are widely used for measuring bio-signals using various fusion sensors that employ photoplethysmograms, cameras, microphones, ultrasound (US) sensors, and accelerometers. However, the consumer demand for small form factors has significantly increased as the integration of multiple sensors is difficult in small mobile or wearable devices. This study proposes two novel opto-US sensors, namely (1) a wearable photoplethysmography (PPG)-US device and (2) a PPG sensor built-in mobile smartphone with a US sensor, seamlessly integrated using a transparent ultrasound transducer (TUT). The TUT exhibits a center frequency of 6 MHz with a 50% bandwidth and 82% optical transparency in visible and near-infrared regions. We developed an integrated wearable PPG-US device to demonstrate its feasibility and coupled the TUT sensor with a smartphone. We measured the heart rates optically and acoustically in human subjects and quantified the oxygen saturation optically by passing light through the TUT. The proposed proof-of-concept is a novel sensor fusion for mobile and wearable devices that require a small form factor and aim to improve digital healthcare. The results of this study can form the basis for innovative developments in sensor-based high-tech industrial applications, such as automobiles, robots, and drones, in addition to healthcare applications.

Model optimization strategies based on deep neural networks Learning and application of pruning optimization algorithms

Deep learning is becoming increasingly important in the context of machine learning, with notable performance achieved in many experiments and attempts. However, if one wants to port a deep learning network from a computationally powerful CPU platform to a small embedded mobile device, it will be constrained by various aspects such as power consumption storage. This paper addresses this problem by simplifying the model, i.e. by model compression. Specifically, by collating work on compression pruning techniques from recent years, the importance of the parameter weights of the data model in the training phase is analysed and then the model is tested with new data in the inference phase to reduce the parameter weights and avoid over-parameterisation, thus achieving model optimisation. Ultimately, an effective idea for model pruning was derived: using the absolute magnitude of the parameters and feature outputs to measure their importance, while analysing the effect of parameter clipping on the loss function. This paper provides a summary distillation of relevant model compression work in recent years to provide a reference for research enthusiasts in the field of pruning and to pave the way for further research.

New Asynchronous Channel-Hopping Sequences for Cognitive-Radio Wireless Networks

In this paper, one family of identification-(ID)-based and one family of non-ID-based asynchronous channel-hopping (CH) sequences are constructed for supporting cognitive-radio wireless networks (CRWNs) in a homogeneous channel setting. The novelty lies in the use of two-dimensional (2-D) algebraic algorithms to properly arrange the linear- and quadratic-congruence sequences in the prime field; channel rendezvous are guaranteed to occur among all the elements (i.e., licensed channels) in at least one of the columns between any two CH matrices. As a result, the new asynchronous CH sequences always have uniform time-to-rendezvous (TTR) structures and satisfy the desirable design criteria of short periods, full degree-of-rendezvous (DoR), and even channel use (ECU). The studies show that the new constructions have the best balance between the full DoR, ECU, short period, small TTR mean and variance, and shortest maximum-time-to-rendezvous (MTTR) and maximum-first-time-to-rendezvous (MFTTR) in their respective categories of constructions. A short period can save memory in small sensors and mobile devices and are suitable for the Internet-of-Things and device-to-device communications. A short MTTR/MFTTR and a small TTR mean/variance can improve the rendezvous frequency and shorten the latency, thus enhancing and stabilizing the throughput.