Patient Confidential Data Hiding and Transmission System Using Amplitude Quantization in the Frequency Domain of ECG Signals.

The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients' information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients' information in an electrocardiogram (ECG). In this system, we first consider a non-linear model with a hiding state switch to enhance the quality of the hidden ECG signals. Next, we utilize particle swarm optimization (PSO) to solve the non-linear model so as to have a good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative root mean square error (rRMSE). Accordingly, the distortion of the shape in each ECG signal is tiny, while the hidden information can fulfill the needs of physiological diagnostics. The extraction of hidden information is reversely similar to a hiding procedure without primary ECG signals. Preliminary outcomes confirm the effectiveness of our proposed method, especially an Amplitude Similarity of almost 1, an Interval RMSE of almost 0, and SNRs all above 30.

Realization of Forest Internet of Things Using Wireless Network Communication Technology of Low-Power Wide-Area Network.

This work implements an intelligent forest monitoring system using the Internet of things (IoT) with the wireless network communication technology of a low-power wide-area network (LPWAN), a long range (LoRa), and a narrow-band Internet of things (NB-IoT). A solar micro-weather station with LoRa-based sensors and communications was built to monitor the forest status and information such as the light intensity, air pressure, ultraviolet intensity, CO2, etc. Moreover, a multi-hop algorithm for the LoRa-based sensors and communications is proposed to solve the problem of long-distance communication without 3G/4G. For the forest without electricity, we installed solar panels to supply electricity for the sensors and other equipment. In order to avoid the problem of insufficient solar panels due to insufficient sunlight in the forest, we also connected each solar panel to a battery to store electricity. The experimental results show the implementation of the proposed method and its performance.

Multiple Performance Optimization for Microstrip Patch Antenna Improvement.

As the Internet of Things (IOT) becomes more widely used in our everyday lives, an increasing number of wireless communication devices are required, meaning that an increasing number of signals are transmitted and received through antennas. Thus, the performance of antennas plays an important role in IOT applications, and increasing the efficiency of antenna design has become a crucial topic. Antenna designers have often optimized antennas by using an EM simulation tool. Although this method is feasible, a great deal of time is often spent on designing the antenna. To improve the efficiency of antenna optimization, this paper proposes a design of experiments (DOE) method for antenna optimization. The antenna length and area in each direction were the experimental parameters, and the response variables were antenna gain and return loss. Response surface methodology was used to obtain optimal parameters for the layout of the antenna. Finally, we utilized antenna simulation software to verify the optimal parameters for antenna optimization, showing how the DOE method can increase the efficiency of antenna optimization. The antenna optimized by DOE was implemented, and its measured results show that the antenna gain and return loss were 2.65 dBi and 11.2 dB, respectively.

Intelligent Healthcare System Using Mathematical Model and Simulated Annealing to Hide Patients Data in the Low-Frequency Amplitude of ECG Signals.

Healthcare is an important medical topic in recent years. In this study, the novelty we propose is the intelligent healthcare system using an inequality-type optimization mathematical model with signal-to-noise ratio (SNR) and wavelet-domain low-frequency amplitude adjustment techniques to hide patients' confidential data in their electrocardiogram (ECG) signals. The extraction of the hidden patient information also utilizes the low-frequency amplitude adjustment. The detailed steps of establishing the system are as follows. To integrate confidential patient data into ECG signals, we first propose a nonlinear model to optimize the quality of ECG signals with the embedded patients' confidential data including patient name, patient birthdate, date of medical treatment, and medical history. Then, we apply Simulated Annealing (SA) to solve the nonlinear model such that the ECG signals with embedded patients' confidential data have good SNR, good root mean square error (RMSE), and high similarity. In other words, the distortion of the PQRST complexes and the ECG shape caused by the embedded patients' confidential data is very small, and thus the quality of the embedded ECG signals meets the requirements of physiological diagnostics. In the terminals, one can receive the ECG signals with the embedded patients' confidential data. In addition, the embedded patients' confidential data can be received and extracted without the original ECG signals. The experimental results confirm the efficiency that our method maintains a high quality of each ECG signal with the embedded patient confidential data. Moreover, the embedded confidential data shows a good robustness against common attacks.

Wavelet-Domain Information-Hiding Technology with High-Quality Audio Signals on MEMS Sensors.

Due to the rapid development of sensor technology and the popularity of the Internet, not only has the amount of digital information transmission skyrocketed, but also its acquisition and dissemination has become easier. The study mainly investigates audio security issues with data compression for private data transmission on the Internet or MEMS (micro-electro-mechanical systems) audio sensor digital microphones. Imperceptibility, embedding capacity, and robustness are three main requirements for audio information-hiding techniques. To achieve the three main requirements, this study proposes a high-quality audio information-hiding technology in the wavelet domain. Due to the fact that wavelet domain provides a useful and robust platform for audio information hiding, this study applies multi-coefficients of discrete wavelet transform (DWT) to hide information. By considering a good, imperceptible concealment, we combine signal-to-noise ratio (SNR) with quantization embedding for these coefficients in a mathematical model. Moreover, amplitude-thresholding compression technology is combined in this model. Finally, the matrix-type Lagrange principle plays an essential role in solving the model so as to reduce the carrying capacity of network transmission while protecting personal copyright or private information. Based on the experimental results, we nearly maintained the original quality of the embedded audio by optimization of signal-to-noise ratio (SNR). Moreover, the proposed method has good robustness against common attacks.

Application of Deep Reinforcement Learning to NS-SHAFT Game Signal Control.

Reinforcement learning (RL) with both exploration and exploit abilities is applied to games to demonstrate that it can surpass human performance. This paper mainly applies Deep Q-Network (DQN), which combines reinforcement learning and deep learning to the real-time action response of NS-SHAFT game with Cheat Engine as the API of game information autonomously. Based on a personal computer, we build an experimental learning environment that automatically captures the NS-SHAFT's frame, which is provided to DQN to decide the action of moving left, moving right, or stay in same location, survey different parameters: such as the sample frequency, different reward function, and batch size, etc. The experiment found that the relevant parameter settings have a certain degree of influence on the DQN learning effect. Moreover, we use Cheat Engine as the API of NS-SHAFT game information to locate the relevant values in the NS-SHAFT game, and then read the relevant values to achieve the operation of the overall experimental platform and the calculation of Reward. Accordingly, we successfully establish an instant learning environment and instant game training for the NS-SHAFT game.

Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals.

With the advent of the aging era, healthcare and elderly care have become the focus of medical care, especially the care of the elderly with dementia. Patients' confidential data hiding is a useful technology for healthcare and patient information privacy. In this study, we implement an intelligent healthcare system using the multiple-coefficient quantization technology in transform domain to hide patients' confidential data into electrocardiogram (ECG) signals obtained by ECG sensor module. In embedding patients' confidential data, we first consider a non-linear model for optimizing the quality of the embedded ECG signals. Next, we apply simulated annealing (SA) to solve the non-linear model so as to have good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative RMSE (rRMSE). Accordingly, the distortion of the PQRST complexes and the ECG amplitude is very small so that the embedded confidential data can satisfy the requirements of physiological diagnostics. In end devices, one can receive the ECG signals with the embedded confidential data and without the original ECG signals. Experimental results confirm the effectiveness of our method, which remains high quality for each ECG signal with the embedded confidential data no matter how the quantization size Q is increased.

Application of Machine Learning in Air Hockey Interactive Control System.

In recent years, chip design technology and AI (artificial intelligence) have made significant progress. This forces all of fields to investigate how to increase the competitiveness of products with machine learning technology. In this work, we mainly use deep learning coupled with motor control to realize the real-time interactive system of air hockey, and to verify the feasibility of machine learning in the real-time interactive system. In particular, we use the convolutional neural network YOLO ("you only look once") to capture the hockey current position. At the same time, the law of reflection and neural networking are applied to predict the end position of the puck Based on the predicted location, the system will control the stepping motor to move the linear slide to realize the real-time interactive air hockey system. Finally, we discuss and verify the accuracy of the prediction of the puck end position and improve the system response time to meet the system requirements.

High-Efficiency Automatic Recharging Mechanism for Cleaning Robot Using Multi-Sensor.

Cleaning robot has the highest penetration rate among the service robots. This paper proposes a high-efficiency mechanism for an intelligent cleaning robot automatically returns to charging in a short time when the power is insufficient. The proposed mechanism initially combines the robot's own motor encoder with neural network linear regression to calculate the moving distance and rotation angle for the location estimation of the robot itself. At the same time, a self-rotating camera is applied to scan the number of infrared spots on the docking station to find the location of the docking station so that the cleaning robot returns to charging properly in two stages, existing infrared range and extended infrared range. In addition, six-axis acceleration and ultrasound are both applied to deal with the angle error that is caused by collision. Experimental results show that the proposed recharging mechanism significantly improves the efficiency of recharging.

Implementation of a Big Data Accessing and Processing Platform for Medical Records in Cloud.

Big Data analysis has become a key factor of being innovative and competitive. Along with population growth worldwide and the trend aging of population in developed countries, the rate of the national medical care usage has been increasing. Due to the fact that individual medical data are usually scattered in different institutions and their data formats are varied, to integrate those data that continue increasing is challenging. In order to have scalable load capacity for these data platforms, we must build them in good platform architecture. Some issues must be considered in order to use the cloud computing to quickly integrate big medical data into database for easy analyzing, searching, and filtering big data to obtain valuable information.This work builds a cloud storage system with HBase of Hadoop for storing and analyzing big data of medical records and improves the performance of importing data into database. The data of medical records are stored in HBase database platform for big data analysis. This system performs distributed computing on medical records data processing through Hadoop MapReduce programming, and to provide functions, including keyword search, data filtering, and basic statistics for HBase database. This system uses the Put with the single-threaded method and the CompleteBulkload mechanism to import medical data. From the experimental results, we find that when the file size is less than 300MB, the Put with single-threaded method is used and when the file size is larger than 300MB, the CompleteBulkload mechanism is used to improve the performance of data import into database. This system provides a web interface that allows users to search data, filter out meaningful information through the web, and analyze and convert data in suitable forms that will be helpful for medical staff and institutions.

Obstructive hydrocephalus as a result of giant cell tumor of the thoracic spine: A case report.

Giant cell tumors (GCTs) are rare bone tumors that account for ~5% of all primary bone tumors. When GCTs occur in the spine, patients usually present with localized pain and neurological symptoms, such as radiating pain or hyperesthesia. In the current report, an unusual case of a GCT of the thoracic spine associated with hydrocephalus is described. A 48-year-old male presented with urinary retention, loss of sensation in the lower limbs and inability to walk. The patient eventually developed hydrocephalus combined with altered consciousness, indicated by an inability to follow simple commands. Magnetic resonance (MR) imaging demonstrated the presence of a soft tissue mass at the T2 level, and biopsy examination of the tissue confirmed that it was a GCT. The patient experienced a sudden loss of consciousness due to an acute episode of obstructive hydrocephalus. A ventriculoperitoneal shunting procedure was performed to treat the hydrocephalus, and the patient regained normal consciousness, although the paraplegia persisted. An MR examination performed 30 months following surgery demonstrated that the tumor size was stable, consistent with the slow growth that is characteristic of GCTs. Diagnosis of GCTs may be challenging, and relies on radiographic and histopathologic findings. Although rare, acute hydrocephalus as a result of GCTs should not be excluded from a differential diagnosis.

Quantitative assessment of brain shifting in the late phase postevacuation in unilateral and bilateral chronic subdural hematomas.

AIM OF THE STUDY: Recurrence is more common in bilateral chronic subdural hematomas (CSDHs) than in unilateral. Our aim was to quantitatively compare the late phase of brain shifting postevacuation in unilateral and bilateral CSDHs. MATERIALS AND METHODS: We reviewed computed tomography (CT) scans and medical records of consecutive patients with CSDHs who underwent burr hole drainage. CT scan images (preoperative and postoperative days [PODs] 30 and 60) were imported to Adobe Photoshop, and temporal and spatial changes in brain shifting between PODs 30 and 60, and also the subdural space on POD 60, were analyzed. RESULTS: The bilateral group exhibited a significantly greater late phase of brain shifting than the unilateral group between PODs 30 and 60 (P < 0.001). The median late phase of brain shifting of the bilateral group was 8.9 mm (interquartile range [IQR]: 8.3-9.0 mm) between PODs 30 and 60, while that of the unilateral group was 1.8 mm (IQR: 1.3-2.5 mm). CONCLUSIONS: The postevacuation late phase of brain shifting is statistically greater in bilateral CSDHs than in unilateral CSDHs, which might facilitate bridging vein tearing and consequent rebleeding. This may be one factor accounting for the higher recurrence rate of bilateral CSDHs.

Simultaneous cranioplasty and subdural-peritoneal shunting for contralateral symptomatic subdural hygroma following decompressive craniectomy.

BACKGROUND: Contralateral subdural hygroma caused by decompressive craniectomy tends to combine with external cerebral herniation, causing neurological deficits. MATERIAL AND METHODS: Nine patients who underwent one-stage, simultaneous cranioplasty and contralateral subdural-peritoneal shunting were included in this study. Clinical outcome was assessed by Glasgow Outcome Scale as well as Glasgow Coma Scale, muscle power scoring system, and complications. RESULTS: Postoperative computed tomography scans demonstrated completely resolved subdural hygroma and reversed midline shifts, indicating excellent outcome. Among these 9 patients, 4 patients (44%) had improved GOS following the proposed surgery. Four out of 4 patients with lethargy became alert and orientated following surgical intervention. Muscle strength improved significantly 5 months after surgery in 7 out of 7 patients with weakness. Two out of 9 patients presented with drowsiness due to hydrocephalus at an average time of 65 days after surgery. Double gradient shunting is useful to eliminate the respective hydrocephalus and contralateral subdural hygroma. CONCLUSION: The described surgical technique is effective in treating symptomatic contralateral subdural hygroma following decompressive craniectomy and is associated with an excellent structural and functional outcome. However, subdural-peritoneal shunting plus cranioplasty thoroughly resolves the subdural hygroma collection, which might deteriorate the cerebrospinal fluid circulation, leading to hydrocephalus.

Coronary arteries segmentation based on the 3D discrete wavelet transform and 3D neutrosophic transform.

PURPOSE: Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies. METHODS: The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries. RESULTS: Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed. CONCLUSION: Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

3-D titanium mesh reconstruction of defective skull after frontal craniectomy in traumatic brain injury.

INTRODUCTION: Decompressive craniectomy (DC) is a treatment strategy used to reduce intracranial pressure in patients with traumatic brain injuries. However, this procedure has a number of shortcomings, such as excessive sinking of the skin flap, which can lead to cerebral compromise and negatively affect the appearance of the patient. The reconstruction of skull defects has been proposed as a means to overcome these disadvantages. Few previous studies have reported the reconstruction of frontal skull defects using titanium mesh. The aim of this study was to provide a comprehensive review of aesthetic and surgical outcomes associated with this procedure and to list the complications encountered during the repair of frontal skull defects using three-dimensional (3-D) titanium mesh. METHODS: A retrospective review was conducted using records from seven adult patients (32-60 years of age) who received titanium mesh implants at a university hospital in Taiwan between January 2011 and June 2012. Aesthetic outcomes, the function of cranial nerves V and VII, and complications (hardware extrusions, meningitis, osteomyelitis, brain abscess, and pneumocephalus) were evaluated. RESULTS: An algorithm capable of accounting for bifrontal skull defects and median bone ridges was developed to improve computer-assisted design/manufacturing (CAD/CAM) of one-piece 3-D titanium mesh implants, thereby making it possible to repair bifrontal skull defects in a single operation. Following this procedure, aesthetic and functional outcomes were excellent and the implants in all patients appeared stable. However, extended healing times in two of the patients resulted in subclinical infections, which were resolved by administering antibiotics over a period of 2 weeks. No patients suffered trigeminal or facial dysfunction. CONCLUSIONS: Our findings support the use of 3-D titanium mesh in frontal skull reconstruction. Few complications were encountered, the contours of the forehead were faithfully rendered, and the cosmetic appearance of patients was excellent. For patients with bifrontal skull defects, the use of one-piece implants in a single operation provides numerous advantages over conventional staged surgeries. This application helps to reduce operating time, which is particularly beneficial for elderly patients and those requiring bifrontal cranioplasties.

DWT-based segmentation method for coronary arteries.

This work presents a new method for segmenting coronary arteries automatically in computed tomography angiography (CTA) data sets. The method automatically isolates heart and coronary arteries from surrounding structures and search for the probable location of coronary arteries by 3D region growing. Based on the dilation of the probable location, discrete wavelet transformation (DWT) and λ - mean operation complete accurate detection of coronary arties. Finally, the proposed method is tested on clinical CTA data-sets. The results on clinical datasets show that the proposed method is able to extract each branch of arteries when comparing to commercial software GE Healthcare and delineated ground truth.

Hiding patients confidential datainthe ECG signal viaa transform-domain quantization scheme.

Watermarking is the most widely used technology in the field of copyright and biological information protection. In this paper, we use quantization based digital watermark encryption technology on the Electrocardiogram (ECG) to protect patient rights and information. Three transform domains, DWT, DCT, and DFT are adopted to implement the quantization based watermarking technique. Although the watermark embedding process is not invertible, the change of the PQRST complexes and amplitude of the ECG signal is very small and so the watermarked data can meet the requirements of physiological diagnostics. In addition, the hidden information can be extracted without knowledge of the original ECG data. In other words, the proposed watermarking scheme is blind. Experimental results verify the efficiency of the proposed scheme.

Wavelet-based watermarking and compression for ECG signals with verification evaluation.

In the current open society and with the growth of human rights, people are more and more concerned about the privacy of their information and other important data. This study makes use of electrocardiography (ECG) data in order to protect individual information. An ECG signal can not only be used to analyze disease, but also to provide crucial biometric information for identification and authentication. In this study, we propose a new idea of integrating electrocardiogram watermarking and compression approach, which has never been researched before. ECG watermarking can ensure the confidentiality and reliability of a user's data while reducing the amount of data. In the evaluation, we apply the embedding capacity, bit error rate (BER), signal-to-noise ratio (SNR), compression ratio (CR), and compressed-signal to noise ratio (CNR) methods to assess the proposed algorithm. After comprehensive evaluation the final results show that our algorithm is robust and feasible.

Verifying three-dimensional skull model reconstruction using cranial index of symmetry.

BACKGROUND: Difficulty exists in scalp adaptation for cranioplasty with customized computer-assisted design/manufacturing (CAD/CAM) implant in situations of excessive wound tension and sub-cranioplasty dead space. To solve this clinical problem, the CAD/CAM technique should include algorithms to reconstruct a depressed contour to cover the skull defect. Satisfactory CAM-derived alloplastic implants are based on highly accurate three-dimensional (3-D) CAD modeling. Thus, it is quite important to establish a symmetrically regular CAD/CAM reconstruction prior to depressing the contour. The purpose of this study is to verify the aesthetic outcomes of CAD models with regular contours using cranial index of symmetry (CIS). MATERIALS AND METHODS: From January 2011 to June 2012, decompressive craniectomy (DC) was performed for 15 consecutive patients in our institute. 3-D CAD models of skull defects were reconstructed using commercial software. These models were checked in terms of symmetry by CIS scores. RESULTS: CIS scores of CAD reconstructions were 99.24±0.004% (range 98.47-99.84). CIS scores of these CAD models were statistically significantly greater than 95%, identical to 99.5%, but lower than 99.6% (p<0.001, p = 0.064, p = 0.021 respectively, Wilcoxon matched pairs signed rank test). These data evidenced the highly accurate symmetry of these CAD models with regular contours. CONCLUSIONS: CIS calculation is beneficial to assess aesthetic outcomes of CAD-reconstructed skulls in terms of cranial symmetry. This enables further accurate CAD models and CAM cranial implants with depressed contours, which are essential in patients with difficult scalp adaptation.