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Öğe Automatic determination of digital modulation types with different noises using convolutional neural network based on time-frequency information(Elsevier, 2020) Daldal, Nihat; Cömert, Zafer; Polat, KemalIn this study, a novel digital modulation classification model has been proposed for automatically recognizing six different modulation types including amplitude shift keying (ASK), frequency shift keying (FSK), phase-shift keying (PSK), quadrate amplitude shift keying (QASK), quadrate frequency shift keying (QFSK), and quadrate phase-shift keying (QPSK). The determination of modulation type is significant in military communication, satellite communication systems, and submarine communication. To classify the modulation types, we have proposed a two-stage hybrid method combining short-time Fourier transform (STFT) and convolutional neural network (CNN). In the first stage, as the data source, the time-frequency information from these modulation signals have been extracted with STFT. This information has been obtained as 2D images to feed the input of the CNN deep learning method. In the second stage, the obtained 2D time-frequency information has been given to the input of the CNN algorithm to classify the modulation types. In this work, noises at various SNR values from 0 dB to 25 dB were created and added to the modulated signals. Even in the presence of noise, the proposed hybrid deep learning model achieved excellent results in the noised-modulation signals. (C) 2019 Elsevier B.V. All rights reserved.Öğe Classification of multi-carrier digital modulation signals using NCM clustering based feature-weighting method(Elsevier Science Bv, 2019) Daldal, Nihat; Polat, Kemal; Guo, YanhuiThis work presents a novel digital modulation signal classification model by combining Neutrosophic c-means (NCM) based feature weighting (NCMBFW) and classifier algorithms. As the digital modulation signal, the multi-carrier amplitude shift keying (MC-ASK), frequency shift keying (MC-FSK), and phase shift keying (MC-PSK) modulation types are employed. In the first step, the feature extraction process has been conducted from the raw digital modulation signals and thereby extracted time, frequency, and timefrequency domain features from the multi-carrier ASK, FSK, and PSK signals. After that, these features have been weighted by using NCMBFW. Finally, classifier algorithms including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-nearest neighbor (k-NN), AdaBoostM1, and Random Forest, have been used to determine the types of digital modulation signals automatically. Many metrics are used to evaluate the performance in the experiments. The proposed method in the classification of MC digital modulation signals is the first work with respect to the classification of MC modulation signals in the literature. For worst case (in 5 dB), while the obtained f-measure values are 0.842, 0.848, 0.863, 0.842, and 0.894 using LDA, SVM, k-NN, AdaBoostM1, and Random Forest classifiers without NCMBFW, respectively, while the f-measure values by combining NCMBFW with classifier algorithms are 0.983, 0.976, 0.992, 0.988, and 0.991, respectively. The experimental results show that the proposed NCMBFW can be considered as a promising tool to improve the classification performance of digital multi-carrier modulation signals. (C) 2019 Elsevier B.V. All rights reserved.Öğe Deep long short-term memory networks-based automatic recognition of six different digital modulation types under varying noise conditions(Springer London Ltd, 2019) Daldal, Nihat; Yıldırım, Özal; Polat, KemalIn this paper, a new method based on deep learning has been proposed in order to recognize noise-digital modulation signals at varying noise levels automatically. The 8-bit data from six different modulations have been obtained by adding noise levels from 5 to 25dB. The used digital modulation types are Amplitude Shift Keying, Frequency Shift Keying, Phase Shift Keying, Quadrature Amplitude Shift Keying, Quadrature Frequency Shift Keying, and Quadrature Phase Shift Keying. To recognize the noise-digital modulation signals automatically, a new deep long short-term memory networks (LSTMs) model has been proposed and then applied to these signals successfully. A significant advantage of the proposed system is that deep learning method has been trained and tested with raw digital modulation signals without applying any feature extraction from the signals. In this study, the noise modulation signals of 5-25dB have been classified and compared with each other. The innovative aspect of the study is to classify the modulation with the LSTM method without dealing with the extraction of signal characteristics. Without noise, added digital modulation signals had been classified as the success rate of 97.22%, while with all noise-added signals have been classified as the success rate of 94.72% with deep LSTM model. The experimental results show that the proposed deep LSTM model has been achieved remarkable results in recognition of noised six different modulation signals with a fully end-to-end structure.Öğe Design and Implementation of Drive and Control System for Ultrasonic Motor over Power Line Communication(Taylor & Francis Inc, 2024) Daldal, Nihat; Aytar, Oktay; Bekiroglu, Erdal; Bal, GungorIn this study, remote control application of an ultrasonic motor (USM) has been achieved over the power line communication (PLC) system. Fast, practical, affordable and effective operating mode is essential for the USM. This study aimed to develop an original, efficient, effective and economical method. Drive and control of USM control has been succeeded with the developed PLC control system. A two-phase high-frequency inverter, a power line transmitter, and a power line receiver circuits have been designed to drive and control of the ultrasonic motor. Required measurements are acquired from the power line to select the most suitable communication frequency and coupling circuit impedance for the PLC system. For the communication frequency and impedance value measurements the receiver and the transmitter circuits have been designed. The PLC-controlled system has been tested for different operating conditions of the ultrasonic motor. USM control has been accomplished over the existing power line without using extra cables and interfaces for communication. The obtained results show that the PLC-controlled system is practical, reliable, cost-effective, and feasible for the remote control of the USM. This research contributes a new and essential perspective for the PLC-based remote control studies in addition to the USM drive and control strategies.Öğe Estimation of body fat percentage using hybrid machine learning algorithms(Elsevier Sci Ltd, 2021) Uçar, Muhammed Kürşad; Uçar, Zeliha; Köksal, Fatih; Daldal, NihatBefore obesity treatment, body fat percentage (BFP) should be determined. BFP cannot be measured by weighing. The devices developed to produce solutions to this problem are called "Body Analysis Devices". These devices are very costly. Therefore, more practical and cost-effective solutions are needed. This study aims to determine BFP using hybrid machine learning methods with high accuracy rate and minimum parameter. This study uses real data sets, which are 13 anthropometric measurements of individuals. Different feature groups were created with feature selection algorithm. In the next step, 4 different hybrid models were created by using MLFFNN, SVMs, and DT regression models. According to the results, BFP of individuals can be estimated with a correlation value of R = 0.79 with one anthropometric measurement. The results show that the developed system can be used to estimate BFP in practice. Besides, the system can calculate BFP with just one anthropometric measurement without device requirement. (C) 2020 Elsevier Ltd. All rights reserved.Öğe İçme suyu şebeke otomasyonunun tasarımı ve gerçekleştirilmesi(2018) Daldal, NihatGünümüzde içme suyu şebekeleri hemen hemen her yerleşim bölgesinde mevcuttur ve içme suyu şebekesi yönetimi kaçınılmaz bir sorundur. Özellikle bu şebekeler çoğu yerlerde, kişilerin inisiyatifine bağlı olarak kontrolsüz bir şekilde yönetilmektedir. Bu sebeple enerji tüketimi, su sarfiyatı ve insan gücü kaybı oldukça fazladır. İçme suyu şebekelerinde temelde yüksek noktalarda su depoları bulunmakta, alçak noktalarda ise pompa istasyonu ile depolara su gönderilmektedir. Otomasyonsuz sistemlerde pompa istasyonundaki motor depolara görevli kişinin pompayı çalıştırması ile su göndermektedir. Eğer depo ile pompa istasyonu arasında haberleşme yoksa, kontrolsüz olarak zamanlamalı çalıştırılan pompa ile depo taşmaktadır. Bu sebeple su sarfiyatı, pompanın elektrik sarfiyatı ve sistemi çalıştırıp kontrolü sağlayan insan gücü kaybı ile motorun mekanik ömrünün azalması en önemli kayıplardır. Bu çalışmada Ankara ili Kızılcahamam ilçesinin içme suyu şebekesinin, bilgisayar kontrollü otomasyon sistemine çevrilmesi sağlanmıştır. İlçede içme suyu için 7 su deposu ve 8 pompa istasyonu bulunmaktadır. Çalışmanın tamamlanması ile su depolarının taşması veya depoların boş kalması engellenmiştir. Ayrıca pompa arızaları, elektrik sarfiyatı ve sistem kontrolünü sağlayan çalışan sayısı minimuma indirgenmiştir. Uygulanan sistem 2011 yılından günümüzde aktif olarak kullanılmaktadır.Öğe Manuel jeneratörün otomatik jeneratöre dönüştürülmesi ve uzaktan izleme(2019) Daldal, Nihat; Şeremet, İbrahimEnerji kesintisinde enerji devamlılığının sağlanması bakımından jeneratörler günümüzde çok kullanılan en önemli aygıtlardan biridir. Jeneratörler manuel olarak çalıştırılabildiği gibi, şebeke gerilimi kesildiği anda otomatik olarak devreye giren türleri de mevcuttur. Kesinti durumlarında enerji ihtiyacının acilen karşılanması için jeneratörlerin otomatik olarak devreye girmesi önemlidir ancak manuel çalıştırılan jeneratörlere göre bu tür jeneratör fiyatları oldukça yüksektir. Bu çalışmada manuel olarak çalıştırılan düşük güçlü jeneratörlerin düşük maliyetle otomatik çalışır jeneratöre dönüştürülmesi gerçekleştirilmiş ve jeneratörler ve şebekedeki bilgilerin uzaktan izlenmesi sağlanmıştır. Sistemde öncelikle manuel çalışmayı otomatikleştirecek yapı anlatılmış ve bunun için mikrodenetleyici tabanlı kontrol sistemi geliştirilmiştir. Böylelikle daha ucuz maliyette otomatik jeneratör kurulumu gerçekleştirilmiştir. Çalışmanın diğer aşamasında ise şebeke ve jeneratör üzerindeki gerilim ve frekans değerlerlerini uzaktan izleyebilmek için ENC2860J ethernet modülü mikrodenetleyicili sistem kartı ile haberleştirilerek verilerin internet ortamına gönderimi sağlanmıştır. Uzaktan bu değerleri takip edebilmek için modem üzerinde alınacak olan IP adresi ile yerel ağda bilgilerin izlenmesi gerçekleştirilmiş daha sonra Mathswork’un entegrasyonu olan ThingSpeak adı verilen bir internet sitesinde kullanıcı hesabı oluşturularak jeneratör ve şebeke değerlerini uzaktan izleme gerçekleştirilmiş ve başarılı bir şekilde uygulanmıştır.Öğe Measurement and Evaluation of Solar Panel Data Via DC Power Line(Ieee, 2022) Daldal, Nihat; Uzun, Berat; Bekiroglu, ErdalToday, it is important to monitor the data first of all in order to increase the efficiency of solar panels. In this study, parameters affecting the efficiency of photovoltaic panels, such as ambient temperature, panel temperature, humidity, light ratio, panel current, panel voltage were measured at certain time intervals. The data was then transferred to the computer via the PV panel (Power Line Communication, PLC) using the existing photovoltaic panel DC power line with the FSK modulator-demodulator, which was converted into a serial information package and designed, and the data was recorded. Here the panel data is collected entirely via its own energy cable without the use of any lines or wireless units. With Python-based software, graphs of panel parameters were created, the data obtained were analyzed and the factors affecting energy production were examined.Öğe The methods toward improving communication performance in transparent radio frequency signals(Hindawi Ltd, 2020) Daldal, Nihat; Nour, Majid; Polat, KemalIn wireless digital communications, amplitude-shift keying (ASK) and frequency-shift keying (FSK) modules are often used and radio frequency (RF), communication synchronization, and noise problems affect the performance very much. In particular, the sending of byte-type data called synchronous and preamble before sending data in intermodule communication increases the sent data and decreases the speed. Also, the microcontroller at the output of the RF receiver module continuously listens to the RF noise and analyzes incoming data, but this increases the processing load of the microcontroller. Moreover, it reduces the speed of performing other operations. In this study, a transparent RF transmitter and receiver have been investigated, and methods for increasing the communication performance of the modules have been proposed and performed. Two of the proposed methods prevented the continuous listening of the microprocessor in the RF receiver structure so that the microprocessor can be used with other processes. In other methods, the compression of the data size was achieved because the transmission of a series of data in RF communication systems was limited to a certain extent. In the last section of the study, since the RF modules have failed to transmit the data due to corruption in the extended data dimensions, the bit carrier control security code has been created for the data series and more healthy communication has been performed.Öğe A new generation communication system based on deep learning methods for the process of modulation and demodulation from the modulated images(Hindawi Ltd, 2022) Daldal, Nihat; Sezer, Zeynel Abidin; Nour, Majid; Alhudhaif, Adi; Polat, KemalDemodulating the modulated signals used in digital communication on the receiver side is necessary in terms of communication. The currently used systems are systems with a variety of hardware. These systems are used separately for each type of communication signal. A single algorithm facilitates the classification and subsequent demodulation of signals without needing hardware instead of extra hardware cost and complex systems. This study, which aims to make modulation classification by using images of signals, provides this convenience. In this study, a classification and demodulation process is done by using images of digital modulation signals. Convolutional neural network (CNN), a deep learning algorithm, has been used for classification and recognition. Images of the signals of quadrate amplitude shift keying (QASK), quadrate frequency shift keying (QFSK), and quadrate phase shift keying (QPSK) digital modulation types at noise levels of 0 dB, 5 dB, 10 dB, and 15 dB were used. Thanks to this algorithm, which works without hardware, the success achieved is around 98%. Python programming language and libraries have been used in training and testing the algorithm. Demodulation processes of these signals have been performed for demodulation using the nonlinear autoregressive network with exogenous inputs (NARX) algorithm, an artificial neural network. As a result of using MATLAB, the NARX algorithm achieved approximately 94% success in obtaining the information signal. Thanks to the work done, it will be possible to classify and demodulate other communication signals without extra hardware.Öğe A novel demodulation method for quadrate type modulations using a hybrid signal processing method(Elsevier, 2020) Daldal, NihatIn this study, the estimation of modulation type and demodulation data from Quadrate type QASK, QFSK and QPSK modulation signals, which are widely used in digital modulation types, has been performed. For this purpose, modulation signals with 1-byte length ranging from 0 to 255 have been obtained for QASK, QFSK and QPSK modulations from multi-level modulation methods. An automatic 3-stage software layer has been developed for the estimation of data from QASK, QFSK and QPSK modulated signals. Quadrate type modulation signals have 4 carrier frequencies. In the software layer, firstly, the modulation signals have been divided into 4 equal parts, and each piece's period has been determined. Moreover, by applying Fourier analysis to each part, amplitude and phase information from the signal has been extracted for the first 10 harmonics. With Fourier analysis, reference signals for QASK, QFSK, and QPSK modulation were generated according to the amplitude, frequency and phase information of each part. Error matrices have been created by comparing each part of the data to the reference signals to be demodulated. According to the least faulty matrices, the binary data value of the signal piece has been found, and then the type of modulation has been determined by comparing the errors totals. Finally, the baseband demodulation data obtained in 4 separate logic 2 bits have been converted to decimal. In this way, the type of any modulation signal could be automatically determined and so that the base band data could be obtained successfully by the developed method. The proposed hybrid signal processing can be easily adapted to microcontroller systems in the real world. (C) 2019 Elsevier B.V. All rights reserved.Öğe A novel demodulation structure for quadrate modulation signals using the segmentary neural network modelling(Elsevier Sci Ltd, 2020) Daldal, Nihat; Nour, Majid; Polat, KemalIn digital communication, the baseband information signal is modulated by the high-frequency carrier to produce a passband signal and applied to the transmission line. QASK (quadrature amplitude-shift keying), QFSK (quadrature frequency-shift keying), and QPSK (quadrature phase-shift keying) modulations from Quadrate type digital modulations are used for high-speed communication in passband digital modulations. They are a widespread modulation for fast and easy data transmission, especially in wireless communication and modem devices. In these modulations, four separate carriers are used, and since each carrier represents 2 bits, the transmission rate doubles compared to conventional digital modulation. This study aims to obtain the baseband signal from quadrate type modulation signals. For this purpose, all 8-bit data between Decimal 0-255 were obtained in QASK, QFSK, and QPSK modulations and signal matrices were formed. The various SNR (signal to noise ratio) values of 5 dB-10 dB-15 dB-20 dB noise were added to these signals to examine the system performance during the modulated signal transmission. The generated signals were given to the demodulation system developed using different methods and tested. The best results were obtained in developed a segmentary NN (Neural Network). In this study, it was observed that modulation signal matrices were given directly to an ANN (Artificial Neural Network) and that the results could not be predicted with the application of noise matrices for testing. Then the modulation signals with the proposed method are divided into four parts. Each segment represents a two-bit piece of data. In the case of a column matrix, four parts were applied as input to the neural network model. In the output of ANN, the result matrix to be predicted is created. Each modulation signal applied to the network input was classified between 0 and 3 at the output. Modulation data-carrying 8 bits are applied to the network in 4 steps and classified. 4 separate classification data from the ANN output is converted back to 2-bit logic. Therefore, signals carrying 8 bits of data are obtained in 4 steps. After the formation of the ANN network, baseband digital signal estimation was performed quickly in 4 steps across each byte modulation signals under different noises coming into the network, and demodulation data was successfully achieved. (C) 2020 Elsevier Ltd. All rights reserved.Öğe A novel demodulation system for base band digital modulation signals based on the deep long short-term memory model(Elsevier Sci Ltd, 2020) Daldal, Nihat; Şengür, Abdulkadir; Polat, Kemal; Cömert, ZaferFor high-frequency digital signals to be transmitted over long distances, the basic digital signal needs to be modulated with a high-frequency carrier. In this case, the baseband digital signal is called the pass-band signal. In particular, in wireless communication systems or ultraviolet or infrared communications, transitional band digital modulations are used. The most commonly used transition band modulations are ASK (Amplitude Shift Keying), FSK (Frequency Shift Keying) and PSK (Phase Shift Keying) modulations. In this study, 8 bits of all digital baseband data were obtained from the transition band modulations in ASK, FSK and PSK modulations in MATLAB. Also, the noises ranging from 5 dB to 25 dB were added to these ASK, FSK, and PSK modulations. The originality of this paper is to a single deep learning model to demodulate the ASK, FSK, and PSK modulations by using a data-driven approach. The main aim is to demodulate the baseband numerical data from the transition band noised modulation signals instead of the hardware demodulator circuits. For this aim, the noised modulated signals were applied to deep LSTM (Long short-term memory) model without feature extraction. The performance measures to evaluate the proposed deep learning-based demodulator method have been used, and they are MAPE, MSE, R-2, RMSE, and NRMSE. The obtained MAPE demodulation results for the worst case of ASK, FSK, and PSK (added 5 dB to these modulations) are 4.392, 5.60, and 3.166, respectively. The experimental results have demonstrated that the proposed LSTM demodulator model could be used safely in the demodulation of ASK, FSK, and PSK modulations in the real world. (C) 2020 Elsevier Ltd. All rights reserved.Öğe A novel tilt and acceleration measurement system based on hall-effect sensors using neural networks(Hindawi Ltd, 2022) Nour, Majid; Daldal, Nihat; Kahraman, Mehmet Fatih; Sindi, Hatem; Alhudhaif, Adi; Polat, KemalA tilt sensor is a device used to measure the tilt on many axes of a reference point. Tilt sensors measure the bending position according to gravity and are used in many applications. Slope sensors allow easy detection of direction or slope in the air. These tilt gauges have become increasingly popular and are being adapted for a growing number of high-end applications. As an example of practical application, the tilt sensor provides valuable information about an aircraft's vertical and horizontal tilt. This information also helps the pilot understand how to deal with obstacles during flight. In this paper, Hall-effect effective inclination and acceleration sensor design, which makes a real-time measurement, have been realized. 6 Hall-effect sensors with analog output (UGN-3503) have been used in the sensor structure. These sensors are placed in a machine, and the hall sensor outputs are continuously read according to the movement speed and direction of the sphere magnet placed in the assembly. Hall sensor outputs produce 0-5 Volt analog voltage according to the position of the magnet sphere to the sensor. It is clear that the sphere magnet moves according to the inclination of the mechanism when the mechanism is moved angularly, and the speed of movement from one point to the other changes according to the movement speed. Here, the sphere magnet moves between the hall sensors in the setup according to the ambient inclination and motion acceleration. Each sensor produces analog output values in the range of 0-5 V instantaneous according to the position of the spheroid. Generally defined, according to the sphere magnet position and movement speed, the data received from the hall sensors by the microcontroller have been sent to the computer or microcomputer unit as UART. In the next stage, the actual sensor has been removed. The angle and acceleration values have been continuously produced according to the mechanism's movement and output as UART. Thanks to the fact that the magnet is not left idle and is fixed with springs, problems such as vibration noises and wrong movements and the magnet leaning to the very edge and being out of position even at a slight inclination are prevented. In addition, the Hall-effect sensor outputs are given to an artificial neural network (ANN), and the slope and acceleration information is estimated in the ANN by training with the data obtained from the real-time slope and accelerometer sensor.Öğe Piecewise demodulation based on combined artificial neural network for quadrate frequency shift keying communication signals(Springer, 2020) Daldal, Nihat; Polat, KemalThe Quadrate Frequency-shift Keying (QFSK) modulation is one of the most widely used modulation methods for transmitting base band signal in the transition band in digital communication. It is a very common modulation type for fast and easy transmission of data, especially in wireless communication. In QFSK modulation, 4 separate carriers are used, and since each carrier represents 2 bits, the sending speed doubles according to classical digital modulation. In this study, firstly, with QFSK modulation, 8-bit information-bearing module signals have been obtained. Moreover, the theory has been developed on the demodulation of the QFSK module signal. The aim is to get the base band signal again. For this purpose, all data from 0–255 to 8-bit decimal length has been obtained as QFSK modulated. SNR = 5 dB–10 db–15 dB–20 dB noise has been added to the QFSK signal in order to examine the system performance during the transmission of the modulated signal. The generated signals are given to the developed demodulation system using different foldings. The matrix consisting of QFSK modulation data to be trained in the network of ANN in demodulation is divided into 4 parts. Each piece represents two bits of data. In the case of a column matrix, 4 parts have been applied as an introduction to the neural network model. In the output of ANN, the result matrix to be estimated has been formed. Trained with 5 dB noisy QFSK data of 10-layer network, application of other QFSK signal data to the network for testing, base band data of the signal has been obtained and 100% performance has been achieved. Each piece of modulation signal applied to the ANN network input is classified between 0–3 at the output. The modulation data that carries 8 bits are applied to the network in 4 steps and classified. 4 separate classification data from ANN output are converted to 2 bits of logic. 8-bit demodulation data is obtained from 4 steps. After the formation of the ANN network, the base band digital signal estimation is performed quickly in 4 steps through each byte QFSK modulation signal under different noise coming to the network.Öğe Remote control of an ultrasonic motor by using a GSM mobile phone(Elsevier Science Sa, 2005) Bekiroğlu, Erdal; Daldal, NihatIn this study, remote control of an ultrasonic motor has been implemented by using a standard GSM mobile phone. To drive the ultrasonic motor a digitally controlled drive system has been designed. Then a tone decoder circuit and microcontroller have been added between output of a mobile phone and the drive system of the motor. This system is flexible to be controlled with both GSM and DTMF based phones. With the developed drive and control system the overall control of the ultrasonic motor has been achieved. The system has been tested for different speed, position and direction conditions successfully. The experimental results verify that the GSM controlled drive system is highly effective, reliable, proper and applicable to achieve remote control of the ultrasonic motor. This study gets novel and important point of view for GSM based remote control applications addition to the control of ultrasonic motors.Öğe Stable measurement system for platinum resistance temperature detector(Maik Nauka/Interperiodica/Springer, 2023) Altınkaya, Serdar; Bayrak, Alper; Daldal, Nihat; Özdil, Osman Eren-Stable and precise temperature measurement is crucial for thermodynamic applications. The stability and precision of the measurement depend on the sensor type and measurement method. In industrial applications, platinum resistance temperature detectors (RTD) are widely preferred. In this study, stable and precise temperature measurement methods by using the Platinum RTD PT1000 sensor are investigated. Measurement methods are considered as measurement circuits and digital filter applications, separately. Experimental studies were evaluated on a household-type oven and comparative results are presented.Öğe Three-channel physiological parameter measurement and its wireless monitoring(Ieee, 2017) Baykoca, Halil İbrahim; Daldal, Nihat; Koç, Kaan Onur; Polat, KemalToday, wireless technologies are being used in almost every area of our lives. Especially in the field of health, the use of wireless technologies is increasing every day. The most important advantage of using wireless communication in biomedical area is that health services can be provided outside health facilities. In addition to this, long and troublesome follow-up of the patient; The patient's home, the doctor's office even without leaving the necessary conditions and conditions to be realized in this area provides serious convenience. With the help of an electronic hardware that can be designed, intensive workload can be reduced to lower levels, the trouble to the patient can be removed, and also the cost can be reduced to a considerable extent. In this study, pulse, temperature and respiration signals received from the body with sensors were transferred wirelessly to Android based phone environment via Arduino and Bluetooth, instantly displayed and monitored from a web site. In this way, it will be possible to measure the instantaneous physiological values of elderly or persons in need of care who are away or have problems in coming to the hospital and transfer them to the doctor or hospital environment.Öğe Toplu Konutlarda Reaktif Güç Kompanzasyonunun Uygulanabilirliğinin Analizi(2020) Sezer, Özkan; Daldal, Nihat; Yücedağ, IbrahimBu çalışmada, teknolojinin hızla gelişmesi sonucu ortaya çıkan elektrik enerjisi ihtiyacının karşılanması içinyapılabilecek iyileştirmeler ele alınmıştır. Bu ihtiyacın karşılanması için enerji üretiminin artırılmasının yanı sıraüretilen enerjinin verimli bir şekilde kullanılması büyük önem arz etmektedir. Enerjinin üretimi ve planlanması,enerji verimliliği açısından son derece önemlidir. Reaktif enerji tüketimini minimuma indirmek yanikompanzasyon yapmak, verimlilik artırır. Ancak günümüzde kompanzasyon sadece belirli gücün üstündekiişletmelerde uygulanmaktadır. Günümüzde elektrik üretimi zor ve pahalı hâle gelmiştir. Bu konuda yapılacak hertürlü tasarruf ve iyileştirme; ülke ekonomisine ve enerji verimliliğine büyük katkı sağlayacaktır. Bu çalışmada,meskenlerde (kompanzasyonun zorunlu kabul edilmediği mekânlarda) kullanılan elektriğin kompanzasyonaihtiyaç duyulup duyulmadığı incelenmiştir. Yapılan bu çalışmada, dört kişilik bir ailenin kaldığı meskene, enerjianalizör sistemi kurularak evin elektrik tüketimi iki aylık süreyle izlenmiş ve elde edilen verilere göredeğerlendirmeler yapılmıştır.
