Combinatorial MAB-Based Joint Channel and Spreading Factor Selection for LoRa Devices.

Long-Range (LoRa) devices have been deployed in many Internet of Things (IoT) applications due to their ability to communicate over long distances with low power consumption. The scalability and communication performance of the LoRa systems are highly dependent on the spreading factor (SF) and channel allocations. In particular, it is important to set the SF appropriately according to the distance between the LoRa device and the gateway since the signal reception sensitivity and bit rate depend on the used SF, which are in a trade-off relationship. In addition, considering the surge in the number of LoRa devices recently, the scalability of LoRa systems is also greatly affected by the channels that the LoRa devices use for communications. It was demonstrated that the lightweight decentralized learning-based joint channel and SF-selection methods can make appropriate decisions with low computational complexity and power consumption in our previous study. However, the effect of the location situation of the LoRa devices on the communication performance in a practical larger-scale LoRa system has not been studied. Hence, to clarify the effect of the location situation of the LoRa devices on the communication performance in LoRa systems, in this paper, we implemented and evaluated the learning-based joint channel and SF-selection methods in a practical LoRa system. In the learning-based methods, the channel and SF are decided only based on the ACKnowledge information. The learning methods evaluated in this paper were the Tug of War dynamics, Upper Confidence Bound 1, and ϵ-greedy algorithms. Moreover, to consider the relevance of the channel and SF, we propose a combinational multi-armed bandit-based joint channel and SF-selection method. Compared with the independent methods, the combinations of the channel and SF are set as arms. Conversely, the SF and channel are set as independent arms in the independent methods that are evaluated in our previous work. From the experimental results, we can see the following points. First, the combinatorial methods can achieve a higher frame success rate and fairness than the independent methods. In addition, the FSR can be improved by joint channel and SF selection compared to SF selection only. Moreover, the channel and SF selection dependents on the location situation to a great extent.

An implementation and evaluation of MPTCP-based IoT router.

The evolving Internet of Things (IoT) promisingly improves the quality of life and transforms many industries. However, the IoT application challenges the wireless networks since the resource-constrained IoT devices typically need to send data to the cloud or edge server. Therefore, it is necessary to introduce an intermediate device between IoT devices and the servers, for example, to reduce the cost of direct communication between them. In another case, the device may move and collect the data from IoT devices before transmitting it to the server. The intermediate device should be designed to have resilient Internet connections and sufficient bandwidth in such a context. This work implements and evaluates a Multipath TCP (MPTCP) IoT router, which uses multiple radios to connect a server to address the demanding design. The router leverages MPTCP, an extension of TCP for simultaneous transmission over several paths on top of Wi-Fi interfaces. MPTCP has also supported several working modes for throughput and (or) resilience enhancements. First, we implement the MPTCP kernels, which can run on the popular IoT devices Raspberry Pi 3B+ and 4. Second, we extensively evaluate the performance of IoT routers in a static and mobility scenario. The static scenario's evaluation results show that the MPTCP-based router can achieve seamless handover and bandwidth aggregation. In the mobility scenario, the MPTCP router with one backup path performs better than the single-path TCP. Besides, the MPTCP routers are more energy-efficient than TCP on the same hardware.

Pairing Optimization via Statistics: Algebraic Structure in Pairing Problems and Its Application to Performance Enhancement.

Fully pairing all elements of a set while attempting to maximize the total benefit is a combinatorically difficult problem. Such pairing problems naturally appear in various situations in science, technology, economics, and other fields. In our previous study, we proposed an efficient method to infer the underlying compatibilities among the entities, under the constraint that only the total compatibility is observable. Furthermore, by transforming the pairing problem into a traveling salesman problem with a multi-layer architecture, a pairing optimization algorithm was successfully demonstrated to derive a high-total-compatibility pairing. However, there is substantial room for further performance enhancement by further exploiting the underlying mathematical properties. In this study, we prove the existence of algebraic structures in the pairing problem. We transform the initially estimated compatibility information into an equivalent form where the variance of the individual compatibilities is minimized. We then demonstrate that the total compatibility obtained when using the heuristic pairing algorithm on the transformed problem is significantly higher compared to the previous method. With this improved perspective on the pairing problem using fundamental mathematical properties, we can contribute to practical applications such as wireless communications beyond 5G, where efficient pairing is of critical importance. As the pairing problem is a special case of the maximum weighted matching problem, our findings may also have implications for other algorithms on fully connected graphs.

Online machine learning algorithms to optimize performances of complex wireless communication systems.

Data-driven and feedback cycle-based approaches are necessary to optimize the performance of modern complex wireless communication systems. Machine learning technologies can provide solutions for these requirements. This study shows a comprehensive framework of optimizing wireless communication systems and proposes two optimal decision schemes that have not been well-investigated in existing research. The first one is supervised learning modeling and optimal decision making by optimization, and the second is a simple and implementable reinforcement learning algorithm. The proposed schemes were verified through real-world experiments and computer simulations, which revealed the necessity and validity of this research.

Arm order recognition in multi-armed bandit problem with laser chaos time series.

By exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing the time-division multiplexing of laser chaos time series. Although the algorithm detects the arm with the highest reward expectation, the correct recognition of the order of arms in terms of reward expectations is not achievable. Here, we present an algorithm where the degree of exploration is adaptively controlled based on confidence intervals that represent the estimation accuracy of reward expectations. We have demonstrated numerically that our approach did improve arm order recognition accuracy significantly, along with reduced dependence on reward environments, and the total reward is almost maintained compared with conventional MAB methods. This study applies to sectors where the order information is critical, such as efficient allocation of resources in information and communications technology.

Dynamic channel selection in wireless communications via a multi-armed bandit algorithm using laser chaos time series.

Dynamic channel selection is among the most important wireless communication elements in dynamically changing electromagnetic environments wherein, a user can experience improved communication quality by choosing a better channel. Multi-armed bandit (MAB) algorithms are a promising approach that resolve the trade-off between channel exploration and exploitation of enhanced communication quality. Ultrafast solution of MAB problems has been demonstrated by utilizing chaotically oscillating time series generated by semiconductor lasers. In this study, we experimentally demonstrate a MAB algorithm incorporating laser chaos time series in a wireless local area network (WLAN). Autonomous and adaptive dynamic channel selection is successfully demonstrated in an IEEE802.11a-based, four-channel WLAN. Although the laser chaos time series is arranged prior to the WLAN experiments, the results confirm the usefulness of ultrafast chaotic sequences for real wireless applications. In addition, we numerically examine the underlying adaptation mechanism of the significantly simplified MAB algorithm implemented in the present study compared with the previously reported chaos-based decision makers. This study provides a first step toward the application of ultrafast chaotic lasers for future high-performance wireless communication networks.

Scalable photonic reinforcement learning by time-division multiplexing of laser chaos.

Reinforcement learning involves decision-making in dynamic and uncertain environments and constitutes a crucial element of artificial intelligence. In our previous work, we experimentally demonstrated that the ultrafast chaotic oscillatory dynamics of lasers can be used to efficiently solve the two-armed bandit problem, which requires decision-making concerning a class of difficult trade-offs called the exploration-exploitation dilemma. However, only two selections were employed in that research; hence, the scalability of the laser-chaos-based reinforcement learning should be clarified. In this study, we demonstrated a scalable, pipelined principle of resolving the multi-armed bandit problem by introducing time-division multiplexing of chaotically oscillated ultrafast time series. The experimental demonstrations in which bandit problems with up to 64 arms were successfully solved are presented where laser chaos time series significantly outperforms quasiperiodic signals, computer-generated pseudorandom numbers, and coloured noise. Detailed analyses are also provided that include performance comparisons among laser chaos signals generated in different physical conditions, which coincide with the diffusivity inherent in the time series. This study paves the way for ultrafast reinforcement learning by taking advantage of the ultrahigh bandwidths of light wave and practical enabling technologies.

Technical Note: Gray tracking in medical color displays-A report of Task Group 196.

PURPOSE: The authors discuss measurement methods and instrumentation useful for the characterization of the gray tracking performance of medical color monitors for diagnostic applications. The authors define gray tracking as the variability in the chromaticity of the gray levels in a color monitor. METHODS: The authors present data regarding the capability of color measurement instruments with respect to their abilities to measure a target white point corresponding to the CIE Standard Illuminant D65 at different luminance values within the grayscale palette of a medical display. The authors then discuss evidence of significant differences in performance among color measurement instruments currently available for medical physicists to perform calibrations and image quality checks for the consistent representation of color in medical displays. In addition, the authors introduce two metrics for quantifying grayscale chromaticity consistency of gray tracking. RESULTS: The authors' findings show that there is an order of magnitude difference in the accuracy of field and reference instruments. The gray tracking metrics quantify how close the grayscale chromaticity is to the chromaticity of the full white point (equal amounts of red, green, and blue at maximum level) or to consecutive levels (equal values for red, green, and blue), with a lower value representing an improved grayscale tracking performance. An illustrative example of how to calculate and report the gray tracking performance according to the Task Group definitions is provided. CONCLUSIONS: The authors' proposed methodology for characterizing the grayscale degradation in chromaticity for color monitors that can be used to establish standards and procedures aiding in the quality control testing of color displays and color measurement instrumentation.

Preliminary investigation of the clinical usefulness of super-high-resolution LCDs with 9 and 15 mega-sub-pixels: observation studies with phantoms.

Our purpose in this study was to evaluate the preliminary clinical efficacy of soft-copy reading of digital mammography, for a 15-mega-sub-pixel (MsP) and a 9-MsP super-high-resolution liquid-crystal display (SHR-LCD) by use of an independent sub-pixel driving technology. We performed three kinds of phantom observation studies by six radiological technologists. Detectability of a contrast-detail phantom and simulated small objects (SSOs) resembling microcalcifications (MCLs), and shape discrimination ability of SSOs with round and square shapes, were examined and compared with a 5-MP conventional LCD (5-MP LCD). In each study, four types of display magnification ratio were used. The detectability and the shape discrimination ability of the 15-MsP SHR-LCD were highest among the three LCDs of most of the display magnification ratios. The 9-MsP SHR-LCD indicated a higher or equal performance as compared with the 5-MP LCD in the SSO detection and shape studies. The results of our study demonstrated that the SHR-LCDs had good potential to detect MCLs and to evaluate the shape in high-resolution digital mammography.

Analysis method of noise power spectrum for medical monochrome liquid crystal displays.

We investigated methods of analyzing the noise power spectrum (NPS) measurement for medical liquid crystal displays (LCDs). Uniform images displayed on the LCDs were imaged with a high-performance digital camera equipped with a close-up lens, and then the NPSs were calculated from the image data by means of several analysis methods. In a method using the 2D fast Fourier transform (FFT) with a 256 x 256 pixels data segment (basic method), we examined the efficacy of a background trend correction (BTC) and a Hanning windowing process used for reducing the spectral estimation errors in the Fourier analysis. To improve the frequency resolution of the basic method, we examined two 2D FFT methods by using 512 x 512 and 1024 x 1024 pixel segments. In addition, we studied a 1D FFT method with 1024-point 1D noise profiles (1D method). In these three methods, the BTC by a second-order polynomial fit and Hanning windowing were commonly applied. A 3-mega-pixel (MP) and a 5-MP monochrome LCD were employed for evaluating the respective methods. Also, a prototype 5-MP LCD equipped with a new anti-reflection surface-coated panel was compared with the conventional 5-MP LCD. The Hanning windowing process was indispensable for avoiding the spectral leakage errors caused by the pixel structures of the LCD. Sufficient frequency resolution was obtained by the 2D FFT method with the 1024 x 1024 pixels segments and the 1D method. The method which provided the most reliable NPSs was the 1D method, with which the BTC was achieved successfully.

Preparation and certification of the new reference materials; plastics (disk form, JSAC 0621-0625) for determination of mercury using x-ray fluorescent analysis.

The Japan Society for Analytical Chemistry has developed some new plastic certified reference materials (CRMs) for the analysis of mercury in polyester disks using XRF analysis. These CRMs (named as JSAC 0621-0625) were prepared by casting polyesters including a toluene solution of organometallic compounds as a standard. Concentrations of the five levels of mercury ranged from 0 to 250 mg/kg. Homogeneity tests of prepared disks had shown excellent results. Interlaboratory comparison study for the certification was performed by 15 laboratory participants. The z-scores in robust statistical method was applied for the evaluation of outliers. The certified values were assigned after discarding outliers. The uncertainties of certified values were determined as the confidence levels of 95%.

Assessment of flat panel LCD primary class display performance based on AAPM TG 18 acceptance protocol.

The image display is an important component of the Picture Archiving and Communication System (PACS) and of digital imaging in general. In this paper, we assess the display performance of 32 different flat panel LCD devices, in terms of their reflection, luminance response, luminance uniformity, resolution, noise, veiling glare and color uniformity included in the tentative guidelines of the AAPM TG18 document version 8.1. We also report on the angular dependencies of luminance and contrast, which constitute one of the miscellaneous tests. The tools used included a telescopic photometer, which was also used as a colorimeter, an illuminance meter, light sources for the reflection assessment, light-blocking devices, and digital TG18 test patterns. The luminance ratio (LR), maximum luminance difference (ALmax) and deviation of contrast response with respect to that of DICOM GSDF were 379.2+/-61.0, 1.6+/-1.1%, and 4.84+/-0.58%, respectively. The maximum luminance nonuniformity was 9.2+/-3.9% for the 10% luminance of the TG18-UNL10 test pattern. In the luminance-based resolution test, the percent luminance difference (deltaL) at the center was 0.78+/-0.42%. In all cases of noise testing, the rectangular target in each square in the three quadrants was visible, as were all 15 targets, except for the smallest one, in each corner pattern and the center pattern. The glare ratio (GR) was 2350+/-1460. The average color uniformity parameter, delta(u',v'), across the display area of each display device was 0.002+/-0.001. Nevertheless, not all of the color uniformity parameters of the display devices associated with a workstation met the acceptance criteria. For 7 selected flat panel displays, the mean specular and diffuse reflection coefficients were 0.0061+/-0.0010 and 0.0017+/-0.0005 cd/m2 per lux, respectively. All of the test results conformed to the criteria recommended by AAPM TG18, indicating that the displays were fully acceptable for diagnostic image interpretation. The maximum viewing angle conforming to the DICOM 3.14 standard luminance responses with a 10% tolerance was found to be approximately 50 degrees in both directions along the vertical axis, 10 degrees in the upper direction and 20 degrees in the lower direction along the horizontal axis, and 20 degrees in the upper direction and 10 degrees in the lower direction along the diagonal axis. Therefore, a radiologist should interpret a displayed image by considering the physical characteristics of the narrow viewing angle of the AMLCD displays. The acceptance testing protocol described herein demonstrates the successful clinical implementation of the guidelines for the viewing conditions of medical displays, and if implemented with a QC program, can be used to determine when LCD devices used for diagnostic interpretation need to be upgraded.

Solving large scale traveling salesman problems by chaotic neurodynamics.

We propose a novel approach for solving large scale traveling salesman problems (TSPs) by chaotic dynamics. First, we realize the tabu search on a neural network, by utilizing the refractory effects as the tabu effects. Then, we extend it to a chaotic neural network version. We propose two types of chaotic searching methods, which are based on two different tabu searches. While the first one requires neurons of the order of n2 for an n-city TSP, the second one requires only n neurons. Moreover, an automatic parameter tuning method of our chaotic neural network is presented for easy application to various problems. Last, we show that our method with n neurons is applicable to large TSPs such as an 85,900-city problem and exhibits better performance than the conventional stochastic searches and the tabu searches.