VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network

Data transmission of electroencephalography (EEG) signals over Wireless Body Area Network (WBAN) is currently a widely used system that comes together with challenges in terms of efficiency and effectivity. In this study, an effective Very-Large-Scale Integration (VLSI) circuit design of lossless EEG compression circuit is proposed to increase both efficiency and effectivity of EEG signal transmission over WBAN. The proposed design was realized based on a novel lossless compression algorithm which consists of an adaptive fuzzy predictor, a voting-based scheme and a tri-stage entropy encoder. The tri-stage entropy encoder is composed of a two-stage Huffman and Golomb-Rice encoders with static coding table using basic comparator and multiplexer components. A pipelining technique was incorporated to enhance the performance of the proposed design. The proposed design was fabricated using a 0.18 μm CMOS technology containing 8405 gates with 2.58 mW simulated power consumption under an operating condition of 100 MHz clock speed. The CHB-MIT Scalp EEG Database was used to test the performance of the proposed technique in terms of compression rate which yielded an average value of 2.35 for 23 channels. Compared with previously proposed hardware-oriented lossless EEG compression designs, this work provided a 14.6% increase in compression rate with a 37.3% reduction in hardware cost while maintaining a low system complexity

A High-Accuracy and Power-Efficient Self-Optimizing Wireless Water Level Monitoring IoT Device for Smart City

In this paper; a novel self-optimizing water level monitoring methodology is proposed for smart city applications. Considering system maintenance; the efficiency of power consumption and accuracy will be important for Internet of Things (IoT) devices and systems. A multi-step measurement mechanism and power self-charging process are proposed in this study for improving the efficiency of a device for water level monitoring applications. The proposed methodology improved accuracy by 0.16–0.39% by moving the sensor to estimate the distance relative to different locations. Additional power is generated by executing a multi-step measurement while the power self-optimizing process used dynamically adjusts the settings to balance the current of charging and discharging. The battery level can efficiently go over 50% in a stable charging simulation. These methodologies were successfully implemented using an embedded control device; an ultrasonic sensor module; a LORA transmission module; and a stepper motor. According to the experimental results; the proposed multi-step methodology has the benefits of high accuracy and efficient power consumption for water level monitoring applications

VLSI Implementation of a Cost-Efficient Loeffler-DCT Algorithm with Recursive CORDIC for DCT-Based Encoder

This paper presents a low-cost and high-quality; hardware-oriented; two-dimensional discrete cosine transform (2-D DCT) signal analyzer for image and video encoders. In order to reduce memory requirement and improve image quality; a novel Loeffler DCT based on a coordinate rotation digital computer (CORDIC) technique is proposed. In addition; the proposed algorithm is realized by a recursive CORDIC architecture instead of an unfolded CORDIC architecture with approximated scale factors. In the proposed design; a fully pipelined architecture is developed to efficiently increase operating frequency and throughput; and scale factors are implemented by using four hardware-sharing machines for complexity reduction. Thus; the computational complexity can be decreased significantly with only 0.01 dB loss deviated from the optimal image quality of the Loeffler DCT. Experimental results show that the proposed 2-D DCT spectral analyzer not only achieved a superior average peak signal–noise ratio (PSNR) compared to the previous CORDIC-DCT algorithms but also designed cost-efficient architecture for very large scale integration (VLSI) implementation. The proposed design was realized using a UMC 0.18-μm CMOS process with a synthesized gate count of 8.04 k and core area of 75,100 μm2. Its operating frequency was 100 MHz and power consumption was 4.17 mW. Moreover; this work had at least a 64.1% gate count reduction and saved at least 22.5% in power consumption compared to previous designs

Efficient and Accurate CORDIC Pipelined Architecture Chip Design Based on Binomial Approximation for Biped Robot

Recently, much research has focused on the design of biped robots with stable and smooth walking ability, identical to human beings, and thus, in the coming years, biped robots will accomplish rescue or exploration tasks in challenging environments. To achieve this goal, one of the important problems is to design a chip for real-time calculation of moving length and rotation angle of the biped robot. This paper presents an efficient and accurate coordinate rotation digital computer (CORDIC)-based efficient chip design to calculate the moving length and rotation angle for each step of the biped robot. In a previous work, the hardware cost of the accurate CORDIC-based algorithm of biped robots was primarily limited by the scale-factor architecture. To solve this problem, a binomial approximation was carefully employed for computing the scale-factor. In doing so, the CORDIC-based architecture can achieve similar accuracy but with fewer iterations, thus reducing hardware cost. Hence, incorporating CORDIC-based architecture with binomial approximation, pipelined architecture, and hardware sharing machines, this paper proposes a novel efficient and accurate CORDIC-based chip design by using an iterative pipelining architecture for biped robots. In this design, only low-complexity shift and add operators were used for realizing efficient hardware architecture and achieving the real-time computation of lengths and angles for biped robots. Compared with current designs, this work reduced hardware cost by 7.2%, decreased average errors by 94.5%, and improved average executing performance by 31.5%, when computing ten angles of biped robots

Effect of end-stage renal disease on long-term survival after a first-ever mechanical ventilation: a population-based study

The 30-day, 6-month, and 1-, 2-, 5-, and 10-year survival rate differences in the ESRD Pos and ESRD Neg groups from the beginning. (DOCX 17 kb

The Uses of a Dual-Band Corrugated Circularly Polarized Horn Antenna for 5G Systems

This paper presents the development of a wide-beam width, dual-band, omnidirectional antenna for the mm-wave band used in 5G communication systems for indoor coverage. The 5G indoor environment includes features of wide space and short range. Additionally, it needs to function well under a variety of circumstances in order to carry out its diverse set of network applications. The waveguide antenna has been designed to be small enough to meet the requirements of mm-wave band and utilizes a corrugated horn to produce a wide beam width. Additionally, it is small enough to integrate with 5G communication products and is easy to manufacture. This design is simple enough to have multi-feature antenna performance and is more useful for the femtocell repeater. The corrugated circularly polarized horn antenna has been designed for two frequency bands; namely, 26.5–30 GHz for the low band and 36–40 GHz for high band. The results of this study show that return-loss is better than 18 dB for both low and high band. The peak gain is 6.1 dBi for the low band and 8.7 dBi for the high band. The beam width is 105 degrees and 77 degrees for the low band and the high band, respectively. The axial ratio is less than 5.2 dB for both low and high band. Generally, traditional circularly polarized antennas cannot meet the requirements for broadband. The designs for the antennas proposed here can meet the requirements of FR2 bandwidths. This feature limits axial ratio performance. The measurement error in the current experiment comes from the high precision control on the size of the ridge

Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors

A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering

Graphene on Au-coated SiOx substrate: Its core-level photoelectron micro-spectroscopy study

The core-level electronic structures of the exfoliated graphene sheets on a Au-coated SiOx substrate have been studied by synchrotron radiation photoelectron spectroscopy (SR-PES) on a micron-scale. The graphene was firstly demonstrated its visibility on the Au-coated SiOx substrate by micro-optical characterization, and then conducted into SR-PES study. Because of the elimination of charging effect, precise C 1s core-level characterization clearly shows graphitic and contaminated carbon states of graphene. Different levels of Au-coating-induced p-type doping on single- and double-layer graphene sheets were also examined in the C 1s core-level shift. The Au-coated SiOx substrate can be treated as a simple but high-throughput platform for in situ studying graphene under further hybridization by PES

A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia

Atrial fibrillation (AF) is the most common cardiovascular disease (CVD); and most existing algorithms are usually designed for the diagnosis (i.e.; feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper; we utilized the MIT-BIH AF Database (AFDB); which is composed of data from normal people and patients with AF and onset characteristics; and the AFPDB database (i.e.; PAF Prediction Challenge Database); which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF); and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction; we regarded diagnosis and prediction as two classification problems; adopted the traditional support vector machine (SVM) algorithm; and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process; the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases; the sensitivity; specificity; and accuracy measures were 99.2% and 99.2%; 99.2% and 93.3%; and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases; respectively. Moreover; the sensitivity; specificity; and accuracy were 94.2%; 79.7%; and 87.0%; respectively; when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels