Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance analysis of RF Energy Harvesting Cooperative Communication Networks with DF scheme

Wireless energy transfer cooperative communication systems are analyzed in this paper. In these systems, a source node can communicate with a destination node directly or via the selected relay nodes, while relay nodes harvest energy from radio frequency for forwarding the received signal. In addition, the decode and forward (DF) protocol is applied to relay nodes, and selection combination technique is employed at the destination in order to select the best relay node. The system performance is presented by outage probability expressions over independent and identically distributed (i.i.d) Nakagami-m channel model. The theoretical analysis and the close-form expression of outage probability are derived and compared with the Monte Carlo simulation. The perfect match of calculation and simulation results verifies our theoretical analysis.
Design and Implementation of Power Efficient Logic BIST With High Fault Coverage Using Verilog

Due to ever increasing number of transistors on chip and decrease in feature size have posed challenges in manufacturing and have risen defects due to them. Thus, testing have become vital for any Very Large Scale Integrated (VLSI) design. The design engineers concentrate more on design development and test techniques used to test those designs are neglected due to design cycle time. Any design will not be passed unless it is 100% fault free and Design For Testability (DFT) technique facilitates to detect the faults. Multiple standards are developed to test different part of Integrated circuits. In this paper power efficient & high fault coverage Built In Self-Test (BIST) is designed and implemented to test combinational logic. The developed technique is tested on standard combinational circuits and has given promising results. The conventional Linear Feedback Shift Register (LFSR) is modified to generate all the states, hence improving fault coverage. Compared to conventional method, 100% fault coverage & 12.25% reduction in power is achieved by the proposed design. The design is coded in Verilog, verified for functionality using Xilinx ISIM simulator, synthesized by targeting the design to slow_vdd1v0 1.0 library for 45nm technology using Cadence genus tool and validated on FPGA Spartan 6 boards.
A Unifying Framework for Blind Source Separation Based on A Joint Diagonalizability Constraint

We present a unifying framework for dealing with convolutive blind source separation (BSS), which fully models inter-channel, inter-frequency, and inter-frame correlation of sources by latent covariance matrices subject to a joint diagonalizability constraint. The framework is shown to encompass as its specific realizations a variety of standard BSS and dereverberation methods that have been developed independently, including frequency-domain independent component analysis (FDICA), fast full-rank spatial covariance analysis (FastFCA), and weighted prediction error (WPE). This gives a unified view of conventional methods and a systematic way of deriving new BSS methods. A BSS experiment on speech mixtures showed improved separation performance of a proposed method compared to the state-of-the-art independent low-rank matrix analysis.
Imitating Driver Behavior for Fast Overtaking through Bagging Gaussian Process Regression

For tackling with the autonomous driving tasks such as fast overtaking, traditional rule-based methods are inflexible and lack abilities of adapting to rapidly changing environments. However, human drivers performed well and have accumulated a large amount of driving behavior data which can be used as a reliable basis for learning intelligent driving behavior in these environments. The autonomous vehicle can gradually learn to obtain human-like behavior policies for complex driving tasks through imitation learning (IL). In this paper, a Bagging Gaussian Process Regression (Bagging GPR) method is proposed to imitate driver behaviors for dynamic complex driving tasks, which combined the Bagging with the Gaussian Process Regression (GPR). Human driver data are collected through the simulation platform Prescan, and different driver behavior models are trained and verified by collected data. Experiments show that compared with Multi-layer BP, ELM, Regression Tree Ensemble, SVR and GPR, the proposed method can further reduce state-action imitating errors and achieve more robust performance of the driver behavior learning.
FOR SERIOUS INFECTIONS

After a pathogen has been identified and the antibiotic susceptibility deter­ mined, parenteral antibiotic administration can be replaced by the oral route for certain patients with meningitis, brain abscess, endocarditis, and skeletal infections. Antibiotics should be administered with the stomach empty and accompanied by 3 ml/kg of water. Direct instillation into the lumen of the small intestine may be advantageous with selected patients. Documenting adequate antibiotic absorption and ensuring compliance are essential to efficacious therapy.
Prospective Electro-hydraulic Control System for a Collaborative Humanoid Robot

The trend in humanoid robots is increasing as we move into the 4th Industrial revolution. Researchers are in a quest to design the efficient humanoid robots. However, most of the robots that have been developed up to date still suffer from high power usage as the robots are fully electrical. Furthermore, these robots are designed to be autonomous and thus cannot or have limited interaction with humans. This paper proposes an electro-hydraulic Collaborative humanoid robot system. This type of system can help humans and robots work together in achieving a task. Furthermore, an electro-hydraulic approach helps reduce the operational and idle power consumption of the entire robot though the design of an efficient control system.
Assessment of Electronic Drug Information Resource Availability in Alabama Pharmacies

The two primary objectives of this study were to identify available electronic drug information (DI) resources in licensed Alabama pharmacies and to identify common electronic resources to teach in the doctor of pharmacy curriculum at the McWhorter School of Pharmacy. A survey consisting of 10 questions was mailed to all licensed medication-dispensing pharmacy practices in Alabama (N = 1,562), of which 699 (44%) were returned for analysis, primarily by community and hospital practices (78.8% and 10.7%, respectively). The survey identifed type of pharmacy, demographics, and availability and usage of electronic DI resources. Drug Facts and Comparisons, Internet search engine, the Pharmacist’s Letter, and Clinical Pharmacology were the most available electronic DI resources (59.5%, 48.6%, 42.3%, and 31%, respectively) among responding pharmacies. Drug Facts and Comparisons was the most common print-based resource; Lexi-Comp and Epocrates were the most utilized handheld DI resources.
Coverage Analysis of Relay Assisted V2I Communication in Microcellular Urban Networks

Vehicular communications have a wide range of applications and are quickly growing in last years. In our work, we investigate vehicular communications to improve the Vehicle-to-infrastructure (V2I) link and enhance the performance of the entire network. We consider a microcellular urban network and compare the coverage probability of the V2I link to the relay-assisted link. We deploy a Manhattan grid where the streets are deployed according to a Poisson line process (PLP), and microcell base stations (BSs) and vehicular users are placed according to a Poisson point process (PPP). We analyze the relay position that maximizes the coverage improvement depending on the density of streets, BSs and vehicular users. We derive analytical expressions for the coverage probability of both direct and relay-assisted links by exploiting tools from stochastic geometry and perform Monte Carlo system level simulations of our model.
DIPEPTIDYL PEPTIDASE-4 (DPP-4) INHIBITION: A NOVEL APPROACH FOR THE TREATMENT OF TYPE II DIABETES MELLITUS

Diabetes Mellitus is a metabolic disorder characterized by hyperglycemia. The prevalence of Diabetes is on the rise, more alarmingly in the developing nations. It is projected that as many as 40-45% of persons aged 65 or older have either Type II Diabetes or its precursor state, Impaired Glucose Tolerance (IGT). The current therapeutic strategies for Type II Diabetes are relatively limited, and involve insulin therapy for Type I Diabetes and Oral Hypoglycemic Agents (OHAs) such as Sulfonylureas, Metformin and the Thiazolidinediones as the primary line of treatment for Type II Diabetes. Combination therapy with one or more of these agents is also a viable option. A wide variety of therapeutic approaches are now being examined for Type II Diabetes. Dipeptidyl peptidase IV (DPP IV), a key regulator of incretinhormones, glucagon-like peptide (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), remains an attractive target in diabetes as it enhances glucose (nutrient) dependent insulin secretion. The present review will give an insight into the development of several DPP-4 inhibitors and their potential prospects in the treatment of Type II Diabetes Mellitus.
Designing at Millimeter-Wave: Lessons from a Triple Coil Variable Transformer

This paper presents a triple coil variable transformer that can be used to design RF blocks for millimeterwave 5G transceivers. By employing a third coil with a varying capacitive load in a two coil transformer, simultaneous dual band or a wide-band response can be achieved ranging from 20 GHz to 50 GHz. The idea is validated using simulations and fabricated using 45 nm CMOS SOI technology. In addition, reasons for mismatch between the simulated and measured results are studied. Furthermore, a modified switching configuration is proposed to alleviate the problems with the original design.
A Neural Network-Based Nonlinear Acoustic Echo Canceller

In this letter, we introduce a novel approach for nonlinear acoustic echo cancellation. The proposed approach uses the principle of transfer learning to train a neural network that approximates the nonlinear function responsible for the nonlinear distortions and generalizes this network to different acoustic conditions. The topology of the proposed network is inspired by the conventional adaptive filtering approaches for nonlinear acoustic echo cancellation. The network is trained to model the nonlinear distortions using the conventional error backpropagation algorithm. In deployment, and in order to account for any variation or discrepancy between training and deployment conditions, only a subset of the network's parameters is adapted using the significance-aware elitist resampling particle filter. The proposed approach is evaluated and verified using synthesized nonlinear distortions and real nonlinear distortions recorded by a commercial mobile phone.
Crease Detection and Repair Based on Minutia Density Distribution

The existence of crease fatally destroys fingerprint structure and texture, concretely, creases will dissever ridgelines and produce a series of pseudo minutiae. Thus, creases will reduce the accuracy of the fingerprint recognition algorithm obviously, especially in the leading algorithm based on minutiae-matching. In this work, we propose a novel approach for detecting and repairing the creases. First, from the minutia density distribution, we sift out large minutia density areas as the crease candidates. We select the candidates meeting some constraints as true crease areas, then we divide them into Large Orientation Difference Crease Areas (LODCAs) and Small Orientation Difference Crease Areas (SODCAs). Secondly, we reconnect the broken ridgelines using stepwise approximation approach in SODCA, while in LODCA, triangular constraint approach is more applicable. The experimental results indicate that the algorithm can detect and repair creases effectively and the recognition accuracy improves greatly with wee additional calculation.
The 2D Materials Used for Nanodevice Applications: Utilizing Aggressively Scaled Transistors

Current nanodevices face many difficulties, one of which is the well-known issue of short-channel effects (SCEs). SCEs cause significant degradation in device characteristics. To overcome this issue, 2D materials have been studied since 2000. This article reveals the motivation for research on 2D materials for device application.
Performance Delay Comparison in Random Access Procedure for NB-IoT, LoRa, and SigFox IoT Protocols

The Narrowband Internet of Things (NB-IoT), Long Range (LoRa), and SigFox protocols are promising technologies in massive connectivity of Internet of Things (IoT) devices for long-range transmissions, but have different characteristics among them. These differences can influence the performance of IoT solutions, namely, devices connectivity, information latency, and even device battery life. Some applications related to metering or telemedicine bring great demands on low latency. This paper presents the characteristics that differentiate these protocols regarding the delays caused by the random access procedures of each one. As a result, it is possible to analyze the protocols and possible choices and adjustments to perform in their configuration to benefit the applications that are using them.
A 28nm bulk-CMOS 50MHz 18 dBm-IIP3 Active-RC Analog Filter based on 7 GHz UGB OTA

This paper presents the design and the experimental validation of a 6th-order continuous-time low-pass filter in 28 nm bulk-CMOS, based on the cascade of 3 Rauch biquadratic cells. Each cell exploits a broad-bandwidth Operational Transconductance Amplifier (OTA), without Miller compensation scheme for differential Loop Gain stability. This maximizes the OTA unity gain bandwidth, with no power increase w.r. t classical compensation schemes, and improves both frequency response accuracy and linearity over the filter pass-band. This aggressive design choice is sustained by the higher 28 nm CMOS transistor’s transition frequency and by the intrinsic feature of the Rauch cell, whose R-C feedback/direct path nets introduce two poles and two zeros that self-compensate the differential loop-gain. On the other hand, the proposed OTA only exploits a compensation scheme for the common-mode signal stability, which does not affect the differential signal. The prototype synthesizes 50 MHz low-pass frequency response at 3.3 mA current consumption from a single 1.1 V supply and performs 18 dBm and 16.5 dBm Input IP3 for 10&11 MHz and 40&41 MHz input tones, equalizing the linearity performance over the filter pass-band, just thanks to the OTA wider bandwidth. This finally allows 153 dBJ-1 and 158 dBJ-1 Figure-of-Merit at 10&11 MHz and 40&41 MHz input tones.
Performance Evaluation of a C-RAN Supporting Quasi-Random Traffic

In this paper we consider a cloud radio access network (C-RAN) where the remote radio heads (RRHs) are separated from the baseband signal processing servers, named baseband units (BBUs). The latter forms a centralized pool of high-performance data center resources. To benefit from network function virtualization, we consider virtualized BBU (V-BBU) resources where the BBU functionality and services have been virtualized in the form of virtual network functions. We consider all RRHs in the C-RAN as a single cluster. Each RRH of such a cluster accommodates quasi-random traffic. This means that new calls in a RRH are generated by a finite number of mobile users. An arriving call requires a radio resource unit from the serving RRH and a computational resource unit from the V-BBU. If these resource units are available, then the call is accepted and remains in the system for a generally distributed service time. Otherwise, the call is blocked and lost. In order to analyze this C-RAN we model it as a loss system, show that the steady probabilities have a product form solution and propose a convolution algorithm for the accurate determination of the main teletraffic performance measure which is congestion probabilities. The accuracy of this algorithm is verified via simulation.
Personal Identification by Capturing Ear Geometry through Convex hull: A Novel Ear Biometric System

We present a novel method developed for identification of persons. The case in point is ear biometrics. The novel features first of their kind heralding the geometry of the ear were extracted. The geometry of image was meticulously traced using convex hull an optimized geometrical polygon which will encase the edge of the image in its entity. Around 600 images were captured in almost similar exposure conditions. Thirteen parameters of convex hull were correlated with ear geometry. A deployable personal identification system was developed and evaluated by performing identification tests. It is found that the system performed efficiently with 96% of recognition accuracy.
Optimizing Hierarchical, Concurrent State Machines in Umple for Model Checking

This paper presents our work on the optimization of hierarchical, concurrent state machines for the purpose of model checking software systems. We propose an encoding strategy that reduces the explosion of the state space during model checking. Our method removes non-concurrent composite states of a state machine but retains its concurrent and basic states counterparts. Transitions into and those originating from the removed states are redirected in a manner that is semantics-preserving. The resulting state machine has a state-space lesser than (or equal to) its unoptimized version. This in-turn yields improvements in resource utilization as attested by means of case studies. While cone of influence (COI) reduction remains a potent method for managing state space explosion during model checking, it was discovered that our approach outperforms COI on some parameters. It even facilitates further reduction in resource utilization when combined with COI.
Low-Complexity optimization for Direction-of-Arrival Estimation via Approximate Message Passing

Sparsity-inducing techniques have been introduced into direction of arrival (DOA) estimation and achieved a great success in performance. However the computational complexity of the conventional sparsity-inducing techniques is prohibitively high and thus prevents such methods from application. In this paper, we propose a low-complexity DOA estimation algorithm based on approximate message passing (AMP). Derived from the loopy belief propagation, AMP is a fast algorithm to obtain the posterior distribution of the signal. The proposed algorithm combines the AMP with expectation maximization (EM) technique to adaptively learn the hyper-parameters in the Gaussian priori of the signal. Closed-form update rule of signal prior variance is derived using fix-point method, an estimator of sources number and an empirical update rule for noise variance are also derived. Compared with the state-of-the-art algorithms, the proposed algorithm reduces the computational complexity by several orders of magnitude, while obtaining comparable performance of DOA estimation. Numerical simulation demonstrates the advantages of the proposed algorithm.
A 19-GHz Pulsed-Coherent ToF Receiver With 40-μm Precision for Laser Ranging Systems

This letter presents a pulsed-coherent time-of-flight (ToF) receiver for a laser ranging system implemented in 28-nm CMOS process. The prototype is designed at 19-GHz carrier frequency with 6.8-ns pulsed modulation. A segmented coarse-fine ToF measurement is adopted to achieve high resolution and fast acquisition. The coarse ToF is recorded by detecting the pulse’s edge whereas the fine measurement is measured by coherent detection. The proposed post-edge detection with automatic gain control loop provides better suppression of walk error from 600 to 26 ps as compared to the rising-edge detection. Also, phase-invariant variable-gain amplifiers provide a low phase error of <± 1° across gain settings with 60-dB dynamic range. This letter can, based on the timing accuracy, achieve 40-µm precision with 1-MHz sampling rate.
Monolithic High Contrast Grating Nitride-Based VECSEL

This paper proposes a new design for a nitride-based vertical-external-cavity surface-emitting laser (VECSEL) emitting at 394 nm. The structure contains a monolithic high contrast grating (MHCG) instead of a conventional distributed Bragg reflector (DBR), makes the vertical design of the laser more compact. In a numerical analysis, we consider several designs for MHCG VECSELs and show that replacing the DBR with an MHCG improves laser performance by 50%, in terms of output energy and slope efficiency. Further optimization of the MHCG VECSEL design achieves an almost threefold improvement in both values.
TV-CAR speech analysis based on Regularized LP

Linear Prediction (LP) analysis is speech analysis to estimate AR (Auto-Regressive) coefficients to represent the all-pole spectrum that is applied in speech synthesis recently besides speech coding. We have proposed $l_{2} -$ norm optimization-based TV-CAR (Time-Varying Complex AR) speech analysis for an analytic signal, MMSE (Minimizing Mean Square Error) or ELS (Extended Least Square) method, and we have applied them into the speech processing such as robust ASR or F0 estimation of speech. On the other hand, B.Kleijn et al. have proposed Regularized Linear Prediction (RLP) method to suppress pitch related bias that is an overestimation of the first formant. In the RLP, $l_{2} -$ norm regularized term that is the norm of spectral changes in the frequencies is introduced to suppress the rapid spectral changes. The RLP estimates the parameter so as to minimize $l_{2} -$ norm criterion added by the $l_{2} -$ norm regularized penalty term. In this paper, the RLP-based TV-CAR speech analysis is proposed and evaluated with the F0 estimation of speech using IRAPT (Instantaneous RAPT) with Keele Pitch Database under noisy conditions.
Wireless Powered Machine-Type Communication: Energy Minimization via Compressed Transmission

We consider a machine-type communication (MTC) node that is served by a hybrid access point (HAP) which provides RF power transfer to the node and receives data transmission from the node. Due to the lossy wireless medium and limited efficiency of RF energy transducer, the energy cost at the HAP is substantial. To minimize the energy cost while still satisfying the system requirement, the harvested energy at the MTC node must be used efficiently. To this end, we consider that the MTC node employs data compression in order to reduce the energy cost of data transmission. Data compression itself consumes time and energy, which needs to be carefully controlled. Thus, we propose to jointly optimize the harvesting-time, compression and transmission design, to minimize the energy cost of the system under given delay constraint. The proposed scheme achieves up to 19% performance gain, under given system constraints, as compared to optimizing harvesting-time ratio and transmission rate without employing compression.
Dual-Stage Phase Unwrapping

Energy minimization on large scale problems can be expensive to compute. This paper presents an extension of a primal-dual algorithm to scale to large scale convex energy minimization problems, while guaranteeing the global optimum on convergence. This is done through the use of a so-called “Dual-Stage” approach that exposes a natural level of parallelism. Benchmarks on the Phase Unwrapping problem in InSAR show that using this approach can give significant improvements to computation-time and memory-time used.
Thermoelectric Properties of Bismuth Telluride and Antimony Telluride Synthesized by Electrochemical Deposition Method

The paper presents the study of thermoelectric properties of bismuth telluride and antimony telluride obtained by electrochemical deposition method. Measurements of electrical conductivity and the Seebeck coefficient were carried out on a NETZCH SBA 458 Nemesis® measuring setup. The coefficient value of bismuth telluride accounted for –47 µV/K, and the coefficient value of bismuth telluride made up 20 µV/K.