Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fully Integrated Li-Ion-Compatible Hybrid Four-Level DC–DC Converter in 28-nm FDSOI

This paper presents a Li-ion-compatible fully integrated hybrid dc–dc converter implemented in 28-nm FDSOI. A modified four-level converter is proposed to achieve high efficiency while operating from the 2.8–4.2-V Li-ion battery range by using stacked 1.5-V transistors. In this paper, the proposed driver architecture exploits the internal nodes of the power stage as power/GND rails for drivers, eliminating the need for dedicated power rails, level shifters, or stacked drivers. The proposed converter operates in the discontinuous conduction mode (DCM) and regulates the output with constant ON-time pulse-frequency modulation (PFM), to achieve high efficiency across low load range suitable for wearable/Internet of Things (IoT) applications. The fabricated chip regulates a 0.6–1.2-V output from a 2.8–4.2-V input voltage over an output power range from <inline-formula> <tex-math notation="LaTeX">$10~\mu \textrm {W}$ </tex-math></inline-formula> to 40 mW with 78% peak efficiency, and switching frequency up to 200 MHz. The converter occupies <inline-formula> <tex-math notation="LaTeX">$1.5~\textrm {mm}^{2}$ </tex-math></inline-formula> of on-die area including a pair of 5-nF flying capacitors and a 3-nH inductor.
Compact Real-Time Inter-Frame Histogram Builder for 15-Bits High-Speed ToF-Imagers Based on Single-Photon Detection

Time-of-flight (ToF) image sensors based on single-photon detection, i.e., SPADs, require some filtering of pixel readings. Accurate depth measurements are only possible if the jitter of the detector is mitigated. Moreover, the time stamp needs to be effectively separated from uncorrelated noise, such as dark counts and background illumination. A powerful tool for this is building a histogram of a number of pixel readings. Future generation of ToF imagers are seeking to increase spatial and temporal resolution along with the dynamic range and frame rate. Under these circumstances, storing the complete histogram for every pixel becomes practically impossible. Considering that most of the information contained by the histogram represents noise, we propose a highly efficient method to store just the relevant data required for the ToF computation. This method makes use of the shifted inter-frame histogram. It requires a memory as low as 128 times smaller than storing the complete histogram if the pixel values are coded on up to 15 bits. Moreover, a fixed 28 words memory is enough to process histograms containing up to 215 bins. In exchange, the overall frame rate only decreases to one half. The hardware implementation of this algorithm is presented. Its remarkable robustness for a low SNR of the ToF estimation is demonstrated by Matlab simulations and FPGA implementation using input data from a SPAD camera prototype.
3D Indoor Localization Mechanism Based on Multiple Sensors

In unfamiliar complicated high-rise building, users not only have to understand the 2D plane location information of floor, but also need to obtain the Z axis information of floor. According to this requirement, we proposed a 3D indoor localization mechanism based on multi-sensor, which integrates the air pressure examination and geomagnetic examination information to assist inertial navigation for calibration, and conduct integrated processing of examined information. The mechanism is based on the dynamic air pressure floor discrimination model, and according to the actual current floor, average time spent by passing each floor and the height of each floor, determine the floor of user in a real-time manner; according to the standard geomagnetic information data of floor, compute the geomagnetic coordinates of current location based on the geomagnetic realtime localization model, and obtain the floor of personnel and the plane location on the floor in a real-time manner. During the localization process of mechanism, it does not require additional hardware equipment or network signal, but only needs the air pressure gauge sensor and geomagnetic sensor to complete the indoor 3D real-time localization of building, which is convenient in practical application with broad adaptability.
Identification of Distributed Malware

Smartphones have evolved over the years from simple devices to communicate with each other to fully functional portable computers although with comparatively less computational power but inholding multiple applications within. With the smartphone revolution, the value of personal data has increased. As technological complexities increase, so do the vulnerabilities in the system. Smartphones are the latest target for attacks. Android being an open source platform and also the most widely used smartphone OS draws the attention of many malware writers to exploit the vulnerabilities of it. Attackers try to take advantage of these vulnerabilities and fool the user and misuse their data. Malwares have come a long way from simple worms to sophisticated DDOS using Botnets, the latest trends in computer malware tend to go in the distributed direction, to evade the multiple anti-virus apps developed to counter generic viruses and Trojans. However, the recent trend in android system is to have a combination of applications which acts as malware. The applications are benign individually but when grouped, these may result into a malicious activity. This paper proposes a new category of distributed malware in android system, how it can be used to evade the current security, and how it can be detected with the help of graph matching algorithm.
A Novel Approach to Joint User Selection and Precoding for Multiuser MISO Downlink Channels

The downlink capacity of a unicast network with a large number of users than the base station transmit antennas depends on user selection and interference among the selected users. Various suboptimal selection schemes in combination with suboptimal or optimal precoding have been proposed in the literature, and some of these techniques asymptotically achieve the sum capacity of DPC, as the number of users goes to infinity. In the previous works, the joint design problem is addressed as a decoupled problem of selection and precoding either at the design level or the solution level. In this work, we focus on the design of a joint solution to the joint design problem, which has the potential to achieve better sum capacity than the aforementioned designs. First, we develop a framework to formulate the joint design problem, with the objective of maximizing Weighted sum rate (WSR). Further, we reformulate the resulting NP-hard WSR maximization problem as a difference-of-concave (DC) functions with novel rate reformulations. Finally, the DC maximization problem is solved using the convex-concave procedure, which is guaranteed to converge to a stationary point. Through simulations, we show the efficacy of proposed solution over the state-of-the-art designs.
Low-Power Fast-Transient Capacitor-Less LDO Regulator With High Slew-Rate Class-AB Amplifier

This brief presents a low-power fast-transient capacitor-less low-dropout regulator (CL-LDO) for system-on-a-chip applications. A low-quiescent-current class-AB amplifier with embedded slew-rate enhancement (SRE) circuit is proposed to improve both current efficiency and load transient performance. As the SRE circuit is directly controlled by the amplifier, only a minimum hardware overhead is required. The proposed CL-LDO is fabricated in a 0.18-<inline-formula> <tex-math notation="LaTeX">${\mu }\text{m}$ </tex-math></inline-formula> standard CMOS process. It occupies an active area of 0.031 mm<sup>2</sup> and consumes a quiescent current of <inline-formula> <tex-math notation="LaTeX">$10.2~\mu \text{A}$ </tex-math></inline-formula>. It is capable of delivering a maximum load current of 100 mA at 1.0-V output from a 1.2-V power supply. The measured results show that a settling time of <inline-formula> <tex-math notation="LaTeX">$0.22~\mu \text{s}$ </tex-math></inline-formula> is achieved for load steps from 1 mA to 100 mA (and vice versa) with an edge time of <inline-formula> <tex-math notation="LaTeX">$0.1~\mu \text{s}$ </tex-math></inline-formula>.
FAST PHASE-DIFFERENCE-BASED DOA ESTIMATION USING RANDOM FERNS

Direction of arrival (DOA) information of a signal is important in communications, localization, object tracking and so on. Frequency-domain-based time-delay estimation is capable of achieving DOA in subsample accuracy; however, it suffers from the phase wrapping problem. In this paper, a frequency-diversity based method is proposed to overcome the phase wrapping problem. Inspired by the machine learning technique of random ferns, an algorithm is proposed to speed up the search procedure. The performance of the algorithm is evaluated based on three different signal models using both simulations and experimental tests. The results show that using random ferns can reduce search time to 1/6 of the search time of the exhaustive method while maintaining the same accuracy. The proposed search approach outperforms a benchmark frequency-diversity based algorithm by offering lower DOA estimation error.
A Real-time and Memory-saving Link Recovery Mechanism for Green Software-Defined Networking

It is crucial to achieve high reliability and low latency concurrently for networked applications such as smart grid, data center, and intelligent factory. Furthermore, maintaining low complexity and energy efficiency for network devices incurs another dimension of challenges. Based on Software Defined Networking (SDN), this paper presents a link recovery mechanism which enhances the reliability of the network, maintains low communication latency, and reduces memory utilizations of the switching devices. To be specific, for achieving low latency, the protection-based link recovery approach is employed where the backup paths are pre-installed in the switch. Furthermore, an improved Segment Routing (SR) approach is utilized where the path information is encoded in the packet header for reducing the flow states stored in the switch. The experimental results show that, achieving real-time link recovery, the proposed mechanism leads to a 25% saving in memory utilizations and thus greatly reduces the complexity of the switch. The energy efficiency of the switch, and the entire network, can be enhanced accordingly.
Research on Vehicle Taillight Detection and Semantic Recognition Based on Internet of Vehicle

Internet of Things(IOT) technology provides sufficient information technology and necessary decision-making basis for intelligent transportation. Internet of vehicles(IOV) is based on the architecture of the perception layer, network layer, and application layer. This article focuses on the tail light status in the perception layer of automotive active safety systems and proposes a method of vehicle taillamp detection and recognition based on active contour extraction and color feature analysis to solve the problem of information perception against the front vehicle. First, the front vehicles are detected and extracted by convolutional neural network and active contour models. Second, it analyzes the characteristics of taillamp pair and then segment the correct taillamp pair by color space conversion and location correlation principle. On this basis, the histograms distribution of taillamp are analyzed in different states and complete the recognition of taillamp. The experimental results show that the method can accurately identify the taillamp signal of the front vehicle in the daytime and effectively reduce the rate of false judgement caused by light interference.
High Voltage Gain Ratio Isolated Resonant Switched-Capacitor Converter for Sustainable Energy

A novel high-gain isolated two-switch boosting resonant switched-capacitor (ITBRSC) converter for sustainable energy is presented in this paper. It concludes a two-switch boosting resonant switched-capacitor (TBRSC) converter unit, an isolated transformer unit, and a load voltage regulation unit. There are two small value inductors in the proposed topology. They are, respectively, injected into the charge loop and the discharge loop. As a result, the pulsating input current is highly decreased. In this way, it could achieve the continuous input current in the primary side of ITBRSC converter. The TBRSC converter unit boosts the input voltage with the high gain ratio. In combination with the transformer, the ITBRSC converter could provide a good isolation performance by adopting the transformer. The proposed converter in this paper could be used in the variable input voltage and wide operation area. It could be acted as the high-gain isolated power supply for sustainable energy systems, such as electric vehicles and wind energy. The SC converter-based high-gain isolated converter could achieve the high power density and simple topology. A 500-W power-rating ITBRSC converter is constructed. The function of the ITBRSC converter is demonstrated and tested with the simulation and experiments. The high gain ratio and robust performance of the proposed converter are totally verified. It could facilitate the application of SC converters for sustainable energy.
The Four-Cluster Spectrum of Mind-Body Interrelationships: An Integrative Model

Despite the shift toward a biopsychosocial paradigm of medicine, many physicians and mental health professionals (MHPs) find it difficult to treat patients with psycho-somatic disorders. This situation is particularly troublesome due to the high prevalence of these conditions. Although progress has been made over the last few decades in understanding mechanisms underlying the mind-body relationship, disparities remain between research and its clinical implementation. One possible reason for this is the lack of a comprehensive, agreed-upon model that incorporates a biopsychosocial framework and is rooted in an understanding of the various psychobiological pathways. Such a model would enable better communication between physicians and MHPs, allowing them to provide coordinated, stratified treatment. In this paper, four archetypal case studies, together with standard care options are presented to illustrate the current state of affairs. A four-tiered conceptual model of mind-body interrelationships based on pathophysiological and psychopathological mechanisms is suggested to help optimize the treatment of somatic complaints. This Four-Cluster model consists of: (1) Organic Conditions: Structural, or degenerative processes that can affect mood and psychological responses but are not clearly exacerbated by stress. (2) Stress Exacerbated Diseases: Biological disorders with a distinct pathophysiology, such as inflammatory or autoimmune diseases, whose progression is clearly exacerbated by stress. (3) Functional Somatic Syndromes: Conditions wherein heightened sensitivity to stimuli together with hyper-reactivity of the autonomic system form a “vicious cycle” of mutually enhancing learning processes. These processes involve biological mechanisms, such as central sensitization and psychological mechanisms such as catastrophization and selective attention. (4) Conversion Disorder: Physical manifestations of psychological distress, expressed somatically. Symptoms are solely an expression of problems in patients' psychic functioning and are not caused by biological pathology. Finally, suggested management of the aforementioned case studies is presented through the lens of the Four-Cluster model and a proposed integration of our model with existing theories is discussed. As it is rooted in an understanding of psychobiological pathways of illness, the proposed model enables a new way to discern which form of mind-body interaction is manifesting in different diseases and proposes a way to coordinate treatment plans accordingly, to enhance the accuracy and efficacy of care.
Spatial Diversity to Support URLLC through Unlicensed Spectrum in Industrial Wireless Network Systems

This paper proposes a method to enable efficient communication service for the fifth generation (5G) wireless communication and it addresses performance requirements for better as well as faster broadband in unlicensed spectrum. The coexistence of the unlicensed spectrum with the licensed spectrum is the major importance to prevent the lack of communication means for coordination and negotiation, offering capacity boost to the licensed spectrum. Subsequently, the spatial diversity technique was carried out to enable ultra-reliable and achieve minimum transmission delay. A reliability is observed in terms of throughput simulation results conducted under different modulation coding schemes (MCSs). Accordingly, the increased number of spatial degrees of freedom presented in both base station (network) and terminals (transmitter and receiver) automatically improves the system performance. Hence, it is a contributor to ultra-reliable and low latency communication (URLLC). This conveys to boost the performance to meet latency-reliability requirements of the system.
Predicting Market Performance with Hybrid Model

In this research, a stock market prediction model was proposed to predict performance of Karachi Stock Exchange (KSE), now merged into Pakistan Stock Exchange (PSX). The model was comprised of four sub-models that were all based on different machine learning technique. Each sub-model used 6 input attributes including fuel price, commodity, foreign exchange, interest rate, general public sentiment and related NEWS. The historical data of the market was also used for predicting the market performance using statistical techniques like Auto-Regressive Integrated Moving Average (ARIMA) and Simple Moving Average (SMA). Support Vector Machine, Radial Basis Function (RBF), Artificial Neural Network’s two variants including Single Layer Perceptron and Multi-layer Perceptron were used to design four different sub-models. The results predicted by all the sub-models were merged in the Hybrid Model. The Hybrid model predicts the market performance on basis of output of each four prediction technique following the majority rule. There were two variants of Hybrid model proposed. Variant I gave about 72.8% accuracy while Variant II gave 95.7% accuracy on training data set. The Hybrid model could not predict better than the results achieved by the MLP based sub-model alone, on test data set. The results suggested that behavior of market can be predicted by using more complex model implementing different machine learning techniques.
Hybrid Wireless Localization via Complex-domain Isometric Embedding

We revisit the super multidimensional scaling (SMDS) wireless localization algorithm first proposed a decade ago, recasting it onto the complex-domain1. Under this new formulation, the edge kernel which carries both angle and distance information simultaneously and plays a central role in the SMDS algorithm, becomes a complex-valued rank-one matrix, resulting in a new complex-domain SMDS framework which yields several advantages over the original, including the elimination of redundancy and the enhancement of conditions to handle information erasure.
SLAT: Sub-Trajectory Linkage Attack Tolerance Framework for Privacy-Preserving Trajectory Publishing

The pervasiveness of location-aware devices offers terrific opportunities for analyzing and mining human mobility. Yet, sharing of trajectory data poses personal privacy issues. Consider a dataset of trajectories, containing detailed movement information about individuals, published with sensitive attributes such as disease, income, etc. One can use partial trajectory knowledge, comprised of non-sensitive location information, for identity, sensitive locations and sensitive values of target individuals. We present SLAT, a Sub-trajectory Linkage Attack Tolerance framework based on anonymization techniques of trajectory splitting, location suppression and sensitive value generalization, to prevent privacy disclosure while preserving accurate trajectory data for query and mining. Experiments show that SLAT is effective to improve data utility of anonymized trajactory data when compared with previous work.
Information Processing and Retrieval from CSV File by Natural Language

Comma Separated Value (CSV) files are widely used as a fundamental data format. Due to its simple structure and ease of creation, many of the data files that are published in open source and used by organizations are usually stored in CSV files. However, searching for or retrieving expected data from CSV files is quite limited by the traditional keyword-matching technique which can't specify the conditions for searching or processing any data on the search. This paper presents a new model that will allow users to easily retrieve information from CSV files by natural language, a language that users are familiar with and use in everyday life. Users can specify conditions for data retrieval and processing to create the information they need. This will help non-technician users easily retrieve information without the need to learn any additional computer languages or programs. The research data includes natural language messages collected from various sources, both online and offline, to cover on both formal and semi-formal language level. By using natural language processing and techniques such as semantic patterns, ontology, and interactive conversation system, this model can analyze the completeness and meaning of natural language statements as well as allows users to edit the incomplete or faulty statements, and improve the model by adding new words, sentence syntaxes and semantic patterns for more accurate results. Evaluation of the model is performed by 98 testers. By inputting 1,137 natural language statements to the model, the results showed that the models were effective in retrieving and processing data accurately with very high values of precision, recall, and F-score which were all higher than 0.9. There are only 18 statements or 3.2% of all statements that produce errors in the outputs which were caused by the typo in 3 cases: missing of some alphabets which change the word's meaning, using of the ambiguous words, and wrong position of words in the natural language statement.
Analysis of All- Optical AND Logic Gate Based on the Cascade of Electro Absorption Modulator and Highly Nonlinear Fibe

The proposed scheme was successful to design and verify optical AND gate based on the cascade of Electro Absorption Modulator and Four Wave Mixing effects in a nonlinear fiber. The logical bit sequences were carried by 1550 nm pump laser at 0.3 mW. The gate was designed with 0.01km optical fiber. The scheme was able to analyze the optical logic states for different combinations in terms of narrow and wideband pulses respectively logic ‘0’ and logic ‘1’. Also the generated logic states represented it in terms of power which exceeds or below the threshold power level.
Effect of Ion-Implantation Temperature on Contact Resistance of Metal/n-Type 4H-SiC With Ar Plasma Treatment

It has been reported that Ar plasma treatment on the n-type 4H-SiC surface before metal deposition can reduce the contact resistance. In that work, the n+ layer was formed by ion implantation at room temperature (RT). In this paper, it is found that Ar plasma treatment on the high-temperature (HT) implanted n+ layer degrades but not improves the contact resistance. A composition of RT/low-energy and HT/high-energy ion implantation can achieve low sheet resistance of the n+ layer and low contact resistance simultaneously. A twofold mechanism is proposed that the amorphous interfacial layer produced by Ar plasma treatment reduces the Schottky barrier height and the defects generated by RT ion implantation enhance current conduction by trap-assisted tunneling.