Miracle-dz/UniFAR-FTUs huggingface dataset:
A 0.8 V, 5 nA PTAT current reference circuit with improved supply voltage sensitivity

This paper presents a low voltage, low power Proportional to Absolute Temperature (PTAT) current reference circuit with improved supply voltage sensitivity. The proposed circuit is designed and laid out in UMC 65 nm CMOS technology and simulated using Cadence Virtuoso. It generates a reference current (Iref) of 5 nA at 0.8 V supply voltage (Vdd) at room temperature (27°C). Composite transistors are used to improve the supply voltage sensitivity when compared to a traditional beta-multiplier circuit. The current reference circuit consumes a power of 8 nW and follows PTAT characteristics in the temperature range of 0°C to 80°C. The supply voltage sensitivity of Iref is 2.6 %/V, which shows that the proposed circuit is less sensitive to supply voltage variations.
Dietary fiber, phytoestrogens, and breast cancer.

: The hypothesis that diet exerts an influence on breast cancer risk has emphasized a role for fat, and current dietary intervention trials to reduce that risk are designed specifically to decrease fat consumption to 15-20% of total calories. There is, however, mounting evidence that dietary fiber has a protective effect and may favorably modify the enhanced breast cancer risk associated with the typical American high-fat low-fiber diet. These data come largely from epidemiological studies, but a few experiments with animal models have also been performed. The mechanisms concerned probably involve estrogen metabolism and bioactivity, both by effects on the enterohepatic circulation of estrogens and the actions of fiber-associated phytoestrogens. More studies are essential to determine the specific types of dietary fiber that are likely to affect the risk of breast cancer, and an appropriate modification in fiber intake should then be added to dietary fat reduction in any future clinical intervention trials designed to demonstrate a favorable influence on breast cancer incidence.
Surgical Tools Detection Based on Modulated Anchoring Network in Laparoscopic Videos

Minimally invasive surgery like laparoscopic surgery is an active research area of clinical practice for less pain and a faster recovery rate. Detection of surgical tools with more accurate spatial locations in surgical videos not only helps to ensure patient safety by reducing the incidence of complications but also makes a difference to assess the surgeon performance. In this paper, we propose a novel Modulated Anchoring Network for detection of laparoscopic surgery tools based on Faster R-CNN, which inherits the merits of two-stage approaches while also maintains high efficiency of comparable speed as state-of-the-art one-stage methods. Since objects like surgical instruments with a wide aspect ratio are difficult to recognize, we develop a novel training scheme named as modulated anchoring to explicitly predict arbitrary anchor shapes of objects of interest. For taking the relationship of different tools into consideration, it is useful to embed the relation module in our network. We evaluate our method using an existing dataset (m2cai16-tool-locations) and a new private dataset (AJU-Set), both collected from cholecystectomy surgical videos in hospital, covering information of seven surgical tools with spatial bounds. We show that our detector yields excellent detection accuracy of 69.6% and 76.5% over the introduced datasets superior to other recently used architectures. We further verify the efficiency of our method by analyzing the usage patterns of tools, the economy of the movement, and the dexterity of operations to assess surgical quality.
Face Spoofing Detection Based on Combining Different Color Space Models

At present, with the widespread application of face recognition for identity authentication technology, spoofing attack has become the biggest security threat. For in the RGB color space model, the real face and the spoofing attack image are difficult to distinguish, a method based on the combination of different color space models is proposed. The main idea is to transform RGB image into YCbCr and Luv color space model to extract LBP (Local Binary Patterns) features, then transform RGB image into HSV color space model to extract CM (Color Moment) features, and finally cascade the extracted features into SVM (Support Vector Machine) for decision classification. Compared with the existing methods on two public datasets of Replay-Attack and CASIA-FASD, the experimental results show that the proposed method is superior to single color space and single feature face spoofing detection method, and the calculation process is very simple, and it has higher discrimination ability.
Current and future trends in photovoltaic technology

The work presented analyzes the current technology trends in solar cell research and photovoltaic (PV) industry. All presented trends like passivated emitter rear contact (PERC) Integrated back contact (IBC) and silicon heterojunction (SHJ) technology currently lead to higher solar module efficiencies in mass production with current values around 20% under standard testing conditions (STC). A promising trend is the current development of perovskite solar cells which, according to the study presented, will lead to solar modules with 30% conversion efficiency in mass production in about 10 years from now. The paper also analyzes additional trends in PV technology like bifacial modules or PV roof tiles. Latter products also need to be analyzed under oblique light conditions. Incidence angle modifier (IAM) measurements of several optically uncoupled solar glasses are presented.
Circuit Analysis of Pass Transistors and Inverter based on Negative Capacitance Silicon-Germanium Double Gate FET (NCSiGeDGFET)

This work presents the performance of Negative Capacitance Silicon-Germanium Double Gate FET (NCSiGeDGFET) based pass transistors and inverter. The ferroelectric layer has been introduced to reduce the power dissipation while SiGe has been incorporated to improve the current drivability. The static and transient characteristics of NCpMOS-Pass Transistor (NCpMOS-PT), NCnMOS-Pass Transistor (NCnMOS-PT), and NCCMOS inverter have been obtained by solving Landau-Khalatnikov equation with TCAD 2D electrostatic. It has been shown that NCpMOS-PT, NCnMOS-PT, and NCCMOS exhibit superior performance in terms of steeper characteristics and reduced static and switching power dissipation as compared to conventional pass transistors and inverter.
Saliency Prediction Network for $360^\circ$ Videos

Panoramic videos are becoming more and more easily obtained for common users. Although these videos have <inline-formula><tex-math notation="LaTeX">$360^\circ$</tex-math></inline-formula> field of view, they are usually displayed with perspective views, which needs the saliency informations for viewing angle selection. In this paper, we propose a saliency prediction network for <inline-formula><tex-math notation="LaTeX">$360^\circ$</tex-math></inline-formula> videos. Our network takes video frames and optical flows in cube map format as input, thus it does not suffer from image distorations of panoramic frames. The network is composed of feature encoding module and saliency prediction module. The feature encoding module extracts spatial and temporal features. Then these features are processed by a decoder and bidirectional convolutional LSTM for saliency prediction. To more thoroughly mine the motion information, the temporal stream of feature encoding module accepts optical flows before and after current frame. We also incorporate the global feature of video frames, residual attention and Gaussian priors into the network by considering the viewing behavior of <inline-formula><tex-math notation="LaTeX">$360^\circ$</tex-math></inline-formula> videos, which is useful for performance improvement. To evaluate the performance of our method, we compare it with three state-of-the-art saliency prediction algorithms on two publicly available datasets. The experimental result has shown the effectiveness of our method, which gets the best performance.
OIDC-Net: Omnidirectional Image Distortion Correction via Coarse-to-Fine Region Attention

Omnidirectional cameras have recently received significant attention in panoramic imaging systems such as virtual reality (VR) technology; however, the strong geometric distortion in omnidirectional images severely affects the object recognition and semantic understanding. In this paper, we propose an automatic omnidirectional image distortion correction approach powered by a unified learning model (OIDC-Net). This approach is applicable for almost all types of omnidirectional cameras, requiring nothing more than a distorted image. A crucial and challenging ingredient for reconstructing the real physical scene is to estimate the heterogeneous distortion coefficients in an appropriate camera model. To address this issue, we present a novel coarse-to-fine region attention mechanism to alleviate the difficulty of predicting all coefficients simultaneously. With the proposed cascade structure and deep fusion strategy, the ambiguous relationship among these heterogeneous distortion coefficients has been incrementally perceived. Our experimental results show significant improvement over the state-of-the-art methods in terms of visual appearance, while maintaining a promising quantitative performance.
Under Voltage Lockout (UVLO) System for Elapsed Time Counter using UMC180 process technology

This paper presents the design of Operational amplifier based comparator used to detect the power supply in Elapsed Time Counter. The basic parameters of comparator are offset, delay, slew rate, high gain, switching point. The parameter of interest is switching point required for the application (toggling the power good signal at specific supply voltage). The proposed comparator works on 1.8V supply is biased with a current of 1uA, consumes around 0.5mW of power and occupies of silicon area. The process technology used is UMC180. This paper also presents the novel designs of Bandgap reference Voltage and Current used to bias the comparator, which are independent of variations in temperature, process corners and supply voltage.
An Efficient Cell Segmentation Algorithm Based on Unsupervised Clustering and Morphology

In traditional Chinese medicine processing domain, the ‘firepower’ and ‘proper roasting’ are vital for medicinal efficacy of Chinese herbs. We take phellodendron microscopic images to determine if the roasting is appropriate according to some features of crystal fiber and stone cell, these features include color, geometrical appearance, and so on. The first step of feature analysis is to segment objects accurately. In the paper, we propose an efficient unsupervised algorithm to segment stone cell and crystal fiber in phellodendron microscopic images automatically without any human's intervene. Firstly, the superpixels is adopted, then the method extracts features such as RGB, Sobel, Harris and so on, clustering superpixels patches according to the characteristic of phellodendron, lastly morphological operations are implemented to produce segmented stone cell and crystal fiber. The experimental evaluation shows that the proposed algorithm can segment objects automatically with about 87% precision and 93% recall.
Comparison of Internal Fault Classification of Wind Turbine Benchmark Model

This work attributes to classify four types of internal faults present in the wind turbine benchmark model (WTBM). In this model, different types of possible faults in the WTBM such as in sensor, actuator, and system are modelled. Support Vector Machines (SVM) and Artificial Neural Network (ANN) are used for the fault classification in a comparative study for fault in pitch position due to an error in sensor measurement; generator torque offset due to actuator fault and changed dynamics effect on angular speed. The SVM classifies more accurately than existing results and ANN for the faults under study.
Classification Methods to Improve Performance in Breast Cancer Screening

Breast cancer is a very aggressive type of cancer with a very low median survival. Today the deaths of women in the age group 15-55 are increasing because of malignant cells are increasing in breast. For the death of women it is the main cause. So, the possibility of improvement is only the early diagnosis of patients. Machine Learning (ML) techniques can assist the physicians by expanding tools for detection at initial stage and analysis of breast cancer thus increasing the probability of patient’s survival [1]. At present, mammography is the best imaging strategy utilized by radiologist for screening breast tumours. In this paper, author proposes a system using different classification method like Support Vector Machine (SVM), Naive Bayes, Decision tree and MLP (Multi-Layer Perceptron) for early detection of cancer. Propose system extracts the texture based features and shape based features using LBP, GLCM, Otsu, Compactness, Fourier Transform. The main focus of the presented work is on application of MLP for breast cancer classification. In addition medical images data has been used to improve accuracy. Proposed system will do the comparative study between both datasets by extracting the feature with and without removing pectoral muscles.
Design of Automated Voltage Protection Circuit for Digital DC-DC Switching Power Supply Digital-analog Hybrid Integrated System Design

Based on the research of digital DC-DC switching power management system, this paper uses arbitration scheduling circuit to perform segmental periodic overvoltage detection and undervoltage detection on the output voltage. When DPWM is ‘1’, overvoltage detection is turned on. When DPWM is ‘0’, undervoltage detection is turned on. This can reduce power consumption effectively. At the same time, on the basis of this, the detection signal is reported to the CPU for diagnosis. When the output voltage signal is wrong, the CPU will shut off the power supply of the entire system to achieve the purpose of automatic voltage protection. The new circuit structure proposed in this paper has the characteristics of low power consumption compared with the traditional analog circuit structure, and can realize the information interaction with the CPU to achieve precise logic control. Finally, the simulation was carried out using the 0.18um BCD process, and the ideal results were obtained.
Defect Segmentation in Surfaces using Deep Learning

Surface inspection is one of the most challenging tasks in the manufacturing industry. Defect classification and segmentation are the two main tasks associated with surface inspection. The major challenge lies in the collection of the dataset as it is a very costly procedure and the occurrences of defected samples are very less as compared to non defective samples. Therefore, it becomes important to devise a method that can leverage the limited data available and can also handle the class imbalance between the defected and non defected samples. In this paper, a deep learning approach is proposed that uses pertained networks to perform defect segmentation on industrial surfaces. The deep learning approach consists of an encoder and decoder architecture where on the encoder side, VGG is used with pertained imagenet weights for faster training of the model and on the decoder side, the UNet decoder model is used. The evaluation of the approach shows that the proposed method can be used for surface inspection in various industrial applications.
Urban Public Transport Digital Planning based on an Intelligent Transportation System

there is presented an original concept of intelligent transportation system for public urban passenger transport advanced management, analysis and control. The proposed approach uses intelligent information technologies based on neural networks and methods of Big Data processing, allowing real time adaptation to dynamically changing conditions. Considering the up-to-date research results and technologies related to the transportation problem domain there is introduced a formal model that describes the public transport helping to reveal the main issues. Using the model there is presented a generalized route network planning algorithm. Description of the software implementation includes the specification of solution modules (components). The proposed solution is illustrated by an example of a transport model covering the territory of the Samara city district, Russia. The results of its probation and practical use prove the efficiency of the intelligent transportation system application for public urban passenger transport management.
High Intensity Focused Ultrasound for Noninvasive Medical Applications

Even though ultrasound has been exploited for medical applications for a long time, High Intensity Focused Ultrasound (HIFU) is getting more and more attention not only from the medical community but also from electronic circuit designers thanks to the still growing progress in circuit fabrication technologies as well as in imaging techniques and systems. Also owing to the non-invasive property of HIFU treatment, not only some undesired side-effects can be avoided for patients but also the healing periods can be considerably shortened compared with other medical treatments. However, the remarkable progress has been observed only over the past 10 years. A brief discussion of the HIFU systems with a focus on the ultrasound transducer driver circuits is given in this paper.
HIV-1 in Haitian women 1982-1988.

Pregnant Haitian women (n=4474) residing in a periurban slum were identified to identify risk factors for sexually transmitted diseases and sera were tested to identify antibodies to HIV-1 and syphilis. The seroprevalence rates for antibodies to HIV-1 increased from 8.98% in 1986 to 9.9% in 1987 and 10.3% in 1988. Sera obtained in 1982 from 533 mothers of young infants in the same community revealed that 7.8% were HIV-1 seropositive. Of women pregnant for the 1st time in 1986-88 6.6% were HIV-1 seropositive and 6.0% had a positive VDRL. The highest seropositivity rates (>15%) were noted in women 20-29 years of age with a history of 2 or more sexual partners in the year prior to pregnancy. Factors independently associated with HIV-1 seropositivity in pregnant women by logistic regression analysis included being unmarried ages 20- 29 years having had more than 1 sex partner in the year to pregnancy a positive serologic test for syphilis and smoking. A dose-response effect was noted in the association between HIV-1 seropositivity and smoking. The association between smoking and HIV-1 infections could be confounded by unrecognized behavioral factors or due to a biologic effect of smoking. The continuing high HIV-1 seropositivity rates in pregnant women indicate that current preventive measures are insufficient and increased control efforts are urgently needed. (authors)
Enabling Universal-Input Operation in Electrolytic Capacitor-less LED Drivers Based on Harmonics Injection

LED lighting has been introduced as an energy efficient and reliable lighting source since several decades. Not only energy efficiency; the long lifetime of LEDs is another significant feature compared to the conventional florescent lamps. Unfortunately, employing electrolytic capacitors (E-Cap) in the AC-DC drivers deteriorates the actual lifetime of the whole LED lamp since an E-Cap has a shorter lifetime compared to the lifetime of the LED chip itself. Thus, a new family of capacitor-less drivers has been evolved by allowing pulsating output current which helps in replacing the E-Cap by long lifetime capacitors such as film or ceramic. In order to revoke the LEDs failure due to the successive current peak stress, techniques such as harmonics injection have been adopted in order to alleviate the current peaks by injecting selected values of harmonics to reduce it. In this paper, a universal-input electrolytic capacitor-less LED driver based on harmonic injection technique is presented. The proposed driver, employing a flyback topology, operates at wide input voltage range ($\mathrm{85V}_{\mathrm{AC}}$ to $\mathrm{265V}_{\mathrm{AC}}$) while maintaining high power factor (PF) and low peak-to-average-ratio (PTAR) values. The design procedures of the proposed driver are discussed, simulated, and verified experimentally by means of 4.2 W flyback prototype. The control simplicity has been taken into consideration which guarantees the solution is compatible with Integrated Circuits (ICs) technology.
Using Algebra of Hyper-Dimensional Vectors for Heuristic Representation of Data While Training Wide Neural Networks

The method of heuristic data representation in broad artificial neural networks training using the algebra of binary vectors of large length (hyper-size vectors) is offered in the article. The idea of data decentralization in a binary vector is the basis for a data hyper-size representation. The usage of a traditional data structure presupposes that a binary vector can be divided into two parts where each of them stores some specific value of a data structure field. Such vector division is impossible in a decentralized representation. Each bit of a hyper-size vector stores to some extent structure fields all at once and at the same time all bits of a hyper-size vector are necessary for getting a specific value of a field. Such a representation of data is similar to a traditional one for brain natural neural networks. According to the authors' view point its application will allow to improve the recognition quality and in the future it can lead to the creation of crucially new methodology of machine training and artificial intelligence.
P4 to FPGA-A Fast Approach for Generating Efficient Network Processors

This paper presents a framework for converting P4 programs to VHDL and then implementing them on Field-Programmable Gate Array (FPGA) platforms. In this framework, a match-action-based hardware architecture is introduced with clearly designed components, which correspond to the described functionalities in the P4 programs. A pre-built template library is used for the compilation that includes optimized VHDL templates corresponding to specific clearly designed components. From the output of a standard frontend P4 compiler, the proposed compiler extracts parameters and relationships within the functions being employed, maps them to corresponding templates by calling, configuring, optimizing and instantiating them, and finally generates the appropriate FPGA code. A pre-built evaluation library is also proposed that helps the compiler to optimize the implementation during the mapping phase. A prototype of this framework is also implemented and evaluated; in this process, it is found that the generated processors use few resources and have high throughput and low latency. Compared with a state-of-the-art solution, the packet processing time is halved. In addition, the generated processors are able to operate at a line rate of nearly 100 Gigabits per second for a basic layer-3 forwarding application.