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Multi-Agent Deep Q-Learning for Antenna Tilt Optimization in Wireless Networks
(2023-01-01)
Wongphatcharatham, Tanutsorn
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Phakphisut, Watid
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Puttarak, Nattakan
The configuration of an antenna installed at a base station involves the quality of communication in wireless networks. For example, at each transmitter, the antenna tilt must be optimized such that the desired and undesired receivers obtain the highest and lowest signal strength, respectively. In this work, we propose to use multi-agent deep Q-learning to optimize the antenna tilt. Our channel model includes the three-dimensional antenna gain, the Ericsson path loss model, and the digital elevation model (DEM). Our simulation indicates that multiagant deep Q-learning provides good signal quality.
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Reference Signal Received Power Prediction Using Convolutional Neural Network with Residual Loss
(2023-01-01)
Ngenjaroendee, Thearrawit
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Phakphisut, Watid
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Wijitpornchai, Thongchai
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Areeprayoonkij, Poonlarp
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Jaruvitayakovit, Tanun
In this paper, LTE measurement reports collected from user equipments are used to generate the residual loss, which can represent the loss value of each grid. The residual loss and geospatial data are used in the learning process of convolutional neural network (CNN). We also use the site configuration and three-dimensional antenna pattern. Thus, the neural network and convolutional neural network are proposed to construct deep learning to predict the reference signal received power (RSRP) in Bangkok, Thailand. The results show that residual loss can improve the efficiency of prediction.
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Non-invasive Optical Blood Glucose Measuring System using Regression Models
(2023-01-01)
Limpiti, Tulaya
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Seemavijai, Mayravee
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Pongmee, Narutchai
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Kanayat, Pannita
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Koseeyaporn, Jeerasuda
Diabetes is one of the chronic diseases with an increasing number of patients every year. Therefore, blood glucose level (BGL) is an essential health information for diagnosis and management of diabetes. Commonly, the BGL is usually measured invasively by taking blood samples, which can cause pain or discomfort to the patients. Our paper aims to design and develop a low-cost, low-computational non-invasive blood glucose measuring system using optical technique. The proposed system consists of a hardware for measuring photoplethysmogram (PPG). The PPG signal is subsequently analyzed to estimate the corresponding BGL, which is sent wirelessly to be stored in a database and displayed on an Android application. The application recommends the appropriate dosage of insulin injection based on the type of users and their diets. The report of historical blood glucose levels in the database can also be generated for further medical examination.Three PPG features, namely peak-to-peak amplitude, maximum amplitude, and standard deviation of signal amplitude, have the strongest correlations to the BGL. We investigate three regression models for estimating the BGL from these PPG parameters. The robustness of the system is assessed using cross validations. The average root mean squares error (RMSE) for three-fold cross-validations are 17.38, 16.76, and 16.39 mg/dL for simple linear regression, principal component regression, and partial least squares regression, respectively. The RMSEs from leave-one-out cross validation are approximately 12 mg/dL, with partial least squares regression model having the best accuracy. Furthermore, the results from Clarke Error Grid Analysis indicate that the system can be implemented with any of the three models for practical usage.
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Cardiac Arrhythmia Teletriage using Electrocardiogram
(2021-01-01)
Limpiti, Tulaya
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Chokchaichumnankit, Thunchanok
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Sanguanchom, Jirayu
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Soyphan, Natchanon
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Puttarak, Nattakan
Telehealth has become a favorable method for receiving medical care during the COVID-19 pandemic. It reduces physical contact and also benefits those who live a distance from hospitals. In this paper we present a cardiac arrhythmia teletriage using electrocardiogram (ECG). The system consists of a diagnostic algorithm for arrhythmias and an Android application. The diagnostic algorithm can detect five types of cardiac problems-arrhythmia, bradycardia, tachycardia, bradyarrhythmia, and tachyarrhythmia. The Android application is the main communication channel between patients and healthcare providers. The user uploads their ECG and receives a preliminary diagnosis of their heart health via the application. The system notifies the user if it detects any abnormalities. The user can then make an appointment online for further examination at the hospital. The capability of the proposed system is evaluated using four databases from PhysioNet - MITBIH Normal Sinus Rhythm Database, MIT-BIH Arrhythmia Database, MIT-BIH Atrial Fibrillation Database, and CU Ventricular Tachyarrhythmia Database. It is found that the algorithm is able to detect abnormal ECG signals with an average accuracy of 82.1%.
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Transfer Learning for Classifying Motor Imagery EEG: A Comparative Study
(2021-01-01)
Limpiti, Tulaya
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Seetanathum, Kornthum
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Sricom, Natchaya
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Puttarak, Nattakan
Motor imagery (MI) is the cognitive process when a person imagines performing a specific movement of their body. The corresponding electroencephalographic (EEG) signals can be measured externally to the head using scalp electrodes. Such signals have been applied in healthcare brain-computer-interface (BCI) systems, for example, motor rehabilitation and prosthetics control. These systems convert different MI EEG input signals to directives, so their performances depend on the efficiency of the embedded signal classification algorithm. In this paper we investigate the effectiveness of transfer learning in classifying the MI EEG data. The Continuous Wavelet Transform (CWT) is used to construct the scalograms, which serve as the inputs to the deep learning structure. The efficacies of five pre-trained networksAlexNet, ResNet18, ResNet50, InceptionV3 and ShuffleNet-are evaluated on the BCI competition IV data set 2a. Binary (left hand vs. right hand) and four-class (left hand, right hand, both feet, and tongue) classifications are trained and tested using fivefold cross validation. The result indicates that using the CWT with transfer learning models provides very high classification accuracies. The ResNet18 network achieves the best accuracies in both cases at 95.03±2.95% and 91.86±2.90%, respectively. In addition, we examine the effect of different time-frequency features on the classification performance by comparing the scalogram of the CWT and the spectogram of the Short-Time Fourier Transform (STFT) as the inputs. It is found that the CWT is the preferred choice as it is superior to the STFT.
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A smart dialogue box for the hearing impaired
(2019-07-01)
Limpiti, Tulaya
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Khamyan, Sudarat
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Chumsaeng, Setthawut
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Puttarak, Nattakan
Conversations between a person with severe hearing impairment and a hearing person is difficult because people who know sign language are rare. Therefore, to facilitate the conversation, we design and develop a portable dialogue box that can be used without an interpreter. The system detects sign language of a deaf person by capturing hand images with a camera. The images are pre-processed and the histograms of oriented gradients (HOG) are extracted as image features. A multi-class support vector machine uses the HOGs as inputs to classify the alphabets. The vocal reply from a hearing person is received through a microphone and interpreted via Google cloud speech API. Both parts are processed by a microcomputer to produce messages of the conversation. The dialogue is displayed on the graphical user interface. The system can also record the conversation logs into the database.
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An implementation of maximum likelihood decoder for error-file recovery on cloud storage system
(2018-07-02)
Sangprasittichok, Pakpoom
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Puttarak, Nattakan
Cloud storage system is a distributed storage system which stores data in many disks on the Internet. In order to achieve more reliable data and improve performance of the storage system, an erasure code is invented and applied to cloud storage system wherein the user's data will be stored online through the Internet. This paper applies the complete graph of ring (CGR) codes [7], which is a maximum distance separable (MDS) code, and implements in cloud storage system. Moreover, the maximum likelihood decoder (MLD) is also applied and implemented on the receiver to detect and correct errors due to noisy channel. In this paper, the grey-scale images/files are randomly stored on distributed cloud storage, then sent via communications channel, and finally read by receivers. The result shows that the receiver operated with the MLD outperforms by giving the lower BER.
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An implementation of low-density MDS array codes for data protection in distributed network data storage system
(2016-09-07)
Puttarak, Nattakan
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Kaewprapha, Phisan
We present an implementation of disk arrays(RAIDs) in distributed networked data storage environment by applying an error-correcting to accommodate fault tolerance feature. Any of the current state-of-the-art distributed file systems, such as MooseFS [1], can be used as an underlying data space where low density-MDS (Maximum distance separable-Low density parity check) array codes [3] is used as a logical protection layer implemented through FUSE interface (File System In User Space). The redundancy/parity scheme is based on graph structures leading to an MDS code, then can be simply implemented in a parity matrix (H matrix) of an LDPC code. The ability of error correction and data recovery is shown as the bit-error rate (BER) has been investigated. It achieves the same trend compared to the simulation results in [3]. The low-density MDS array code can mitigate hardware failure at higher disk space efficiency comparing to the repetition code.
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Latency-reduced retransmission in multicast network using systematic network code
(2016-09-07)
Muangboonma, Nakhon
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Puttarak, Nattakan
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Kaewprapha, Phisan
The reliability of multicast network where source delivers many frames of data to multiple receivers over a large scale network is degraded by delay of retransmissions after some data frame has been lost. This paper proposes a coding technique applying on network layer to combat this problem by using systematic network code with overheard retransmission technique. Latency of retransmission and throughput will be measured to represent the performance of this algorithm. The results show that this technique can improve reliability and performance of multicast network by decreasing a delay of retransmission for 40-70 % and yet increasing throughput for more than 8-19% compared with a conventional method.
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Network localization using tree search algorithm
(2016-09-06)
Kaewprapha, Phisan
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Tansarn, Thaewa
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Puttarak, Nattakan
we consider a network localization problem by modeling this as a unit disk graph where nodes are randomly placed with uniform distribution in an area where connectivity between nodes are defined when the distances fall within a unit range. Under a condition that a number of nodes know their locations (anchor nodes), this paper proposes a heuristic approach to find a realization for the rest of the network by applying a tree search algorithm in a depth-first-search manner utilizing graph properties to speed up the search, aka pruning the search tree by modeling an evaluation function from those properties. The evaluation function is used to select the order of the unknown nodes to iterate to. In [1,2], it is demonstrated that number of connections to previously localized nodes reduces the average feasible iterations within reasonable time. This paper also extends the idea further by accommodating various other properties of graph into the evaluation function. The results show that node degrees, node distances and shortest paths to anchor nodes drastically improve the number of iterations required for realizing feasible localization instance by 40%.

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