Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of DS-CDMA protocols in wireless LANs

The performance of direct-sequence code-division multiple access (DS-CDMA) systems in indoor wireless channels is presented. TCP traffic, channel noise and signal characteristics are measured using commercial products that implement the IEEE 802.11b DS-CDMA standard. The measured throughputs for a low density mobile network are typically around $4.2$ Mbps when one of the two end-systems is on the wired segment. Multipath interference is shown to contribute to significant degradation in TCP performance. Packet loss rates ranging from $0.02$ to $0.2$ percent are observed in line-of-sight conditions. The BER performance of DS-CDMA is estimated using an image source based channel model of the rectangular room to compute the spatial variation of the channel impulse response. These impulse responses provide a quantitative measure of the system performance and provide estimates of signal power levels required for supporting high data rates in channels influenced by multipath interference.
A low-power Rijndael S-Box based on pass transmission gate and composite field arithmetic

Using composite field arithmetic in Galois field can result in the compact Rijndael S-Box. However, the power consumption of this solution is too large to be used in resource-limited embedded systems. A full-custom hardware implementation of composite field S-Box is proposed for these targeted domains in this paper. The minimization of power consumption is implemented by optimizing the architecture of the composite field S-Box and using the pass transmission gate (PTG) to realize the logic functions of S-Box. Power simulations were performed using the netlist extracted from the layout. HSPICE simulation results indicated that the proposed S-Box achieves low power consumption of about 130 μW at 10 MHz using 0.25 μm/2.5 V technology, while the consumptions of the positive polarity reed-muller (PPRM) based S-Box and composite field S-Box based on the conventional CMOS logic style are about 240 μW and 420 μW, respectively. The simulations also showed that the presented S-Box obtains better low-voltage operating property, which is clearly relevant for applications like sensor nodes, smart cards and radio frequency identification (RFID) tags.
Unibertsitateko ikasleen laburpen-gaitasunaren azterketa eta testuaren egitura hierarkikoan oinarrituriko atzeraelikadura automatikoa

This paper aims to present a workshop that develops the summarization skills of future university students. The workshop aims to explicitly and consciously work on the required process to summarize school texts and explain to students which skills could benefit when summarizing. Results show an improvement in the student’s metacognitive process, which develops his capacity for improving summaries output. Besides, automatic feedback has been given to students to evaluate the hierarchy of propositions and results show this automatic feedback is beneficial in the learning process of students
Bridged 'V'-shaped patch antenna for dual-band communication

A bridged ‘V’-shaped patch antenna has been designed and fabricated for dual-band communication. The dual-band operation is obtained by placing a bar connecting the two arms at the top of a ‘V’-shaped microstrip antenna. Comparison of the return loss performance of a single ‘V’-shaped antenna and the bridged ‘V’ antenna is carried out. Free space radiation pattern measurement is carried out for the bridged ‘V’-shaped antenna to ascertain the effect of the bridging on the far field pattern. The measured results of the bridged ‘V’-shaped antenna shows good performance of a dual-band operation with the potential of communicating in two different directions.
Efficient Suppression of Substrate Noise Coupling in Complementary Metal-Oxide-Semiconductor Field-Effect-Transistor Technology

In this work, we investigate the substrate noise coupling using S-parameter measurement. Radio frequency domain analysis shows that the noise isolation is strongly dependent on layout geometry, including the parameters such as P/N junction, physical separation distance, guard ring (GR), and deep N-well (DNW). We found that the noise coupling can be efficiently diminished by incorporating a GR and DNW structures.
PREDICTION OF PROCESSING TOMATO YIELD USING A CROP GROWTH MODEL AND REMOTELY SENSED AERIAL IMAGES

Remote sensing using aerial images is very useful in obtaining large amounts of data in a short period of time. Vegetation indices derived from remotely sensed images often correlate well with plant characteristics. In our previous research, we found a relationship between modified normalized difference vegetation index (NDVI) and leaf area index (LAI). In this study, we have developed a model to predict the processing tomato yield based on soil, crop, and environmental parameters. LAI derived from modified NDVI and photosynthetically active radiation (PAR) are the two major inputs to this yield prediction model. The model was calibrated and validated using yield data gathered from a load cell based tomato yield monitor during the 2000 crop growing season. Although the actual and predicted yield maps did not have a very high correlation, the two maps showed similar yield patterns. The RMS error in yield prediction was about 6%.
Comparing the Performance of Ultrasonic Liver Image Enhancement Techniques: A Preference Study

Abstract This is a preliminary study and the objective of this study has been to compare the performance of some of the primitive and fundamentally different post acquisition image enhancement algorithms as applied to ultrasound (US) liver images. Such a comparison would help to decide as to which algorithm could be useful for clinicians, and in evaluating the role of US liver image enhancement in a soft-copy environment. In this study, 10 US liver images were taken, and 5 fundamentally different and widely employed image enhancement techniques were applied on these images. As the principal objective of image enhancement is to obtain an image with a high content of visual detail, a multipoint rank-order method was used to identify small differences or trends in observations. Among the different algorithms, the morphological Altering outperformed other techniques. The images with diffused liver diseases had a preference of 76% and the images with cystic masses had a preference of 67%. Wavelet-based Altering closely followed this with 63% of preference in respect of images with a diffused liver disease and 57% of preference in respect of images with cystic masses.
Research on Impact of Radio Irregularity on Self-Localization of WSN

Localization is a vital foundation work in Wireless Sensor Network (WSN). Almost all of location algorithms at present need the position information of reference nodes to locate the unknown nodes. But most of algorithms assume an idealistic radio propagation model that is far from the reality. This will lead to obvious difference compared with real localization of WSN. In this paper we investigate the impact of radio irregularity on the localization algorithms performance in WSN. We introduce the Radio Irregularity Model (RIM) which is established upon empirical data. With this model, this paper analyzes the impact of radio irregularity on localization algorithms. We compare three typical coarse-grained localization algorithms: APIT, Centroid and DV-HOP in simulated realistic settings. Our experimental results show that radio irregularity has a significant impact on some main evaluation aspects of localization algorithms. Some interesting phenomena is worthy of further study.
A Theoretical Framework for Healthcare Innovation Management Using Wiki Based Digital Ecosystem

Information Technology innovations have strongly affected today’s businesses and the way we work. This effect involves different industries, and the healthcare industry is one of them. Various healthcare information systems have been introduced to manage and share patient records and information. However, based on the reviewed literatures, the healthcare knowledge management system does not have the same focus and attention. It is found that there is no system that is able to manage the tacit healthcare knowledge and innovation. As a result, this paper aims to introduce a theoretical framework that enables healthcare tacit knowledge management and global sharing. Digital Ecosystem is found to be the most suitable technology to achieve this aim; specifically with the wiki environment as it is most suitable for the healthcare industry requirements.
Estimating ocean surface velocity and coherence time using multichannel ATI-SAR systems

Conventional along-track interferometric synthetic aperture radars (ATI-SARs) derive the ocean surface velocity from an estimate of the phase difference between the SAR echoes received from two displaced phase centres with a single time-lag. The authors propose an efficient asymptotic maximum likelihood (ML) algorithm for jointly estimating the ocean surface velocity and ocean coherence time by using multichannel SAR data collected from an array of phase centres with multiple time-lags. The method combines a covariance matching algorithm with the use of the extended invariance principle (EXIP). Simulated results show that the proposed technique provides better estimation accuracy than the conventional two-channel system. Moreover, it provides unambiguous velocity retrieval and flexibility to varying ocean coherence time.
A Supervised Robust Predictive Multi-Controller for Large Operating Conditions of an Open-Channel System

Abstract In this paper, a method for designing and for choosing parameters of a supervised robust predictive multi-controller strategy (SRPMC) is proposed. The SRPMC strategy is based on a multi-model representation taking into account the large operating conditions of the real process. In this application to an open-channel system, multiple discrete models were identified for representing the overall range of discharge. The number of models is given by an algorithm initialized by the choice of only one parameter. The SRPMC strategy is compared with a PID control strategy showing its effectiveness and better performances.
Design and development of smart electricity meter – the path towards sustainable globe

Reducing energy demand in the domestic segment is an important problem worldwide. This study focused on the awareness of residents on energy conservation and on the potential of reducing energy demand through energy-saving activities. The following observations were noted down during the experiment of smart meter in 10 houses of Mumbai city. (1) Its installation led to a reduction in power consumption, (2) a change in energy-saving behaviours of the household members such as the reduction of standby power and a better control of appliance operation, and (3) energy-conservation awareness affected not only the power consumption of the appliances explicitly shown on the display monitor but also other household appliances. (4) Development of sustainable environmental system.
CMOS current reference with supply and temperature compensation

The dependency of CMOS current reference on supply voltage and temperature is compensated by simply subtracting two current outputs with the same dependencies on the supply voltage and temperature. With this compensation scheme, a self-biased CMOS current reference has been implemented in a 0.25 µm standard CMOS technology. The current reference provides 10.45 µA output current, while the supply and temperature dependencies are 1700 ppm/V and 720 ppm/°C, respectively. The current reference occupies only 0.002 mm2 active area and can operate down to 1.1 V.
Infrared Small Target Detection Using Nonoverlapping Patch Spatial–Temporal Tensor Factorization With Capped Nuclear Norm Regularization

Infrared target detection technology is indispensable in precision guidance and infrared warning systems. It faces grant challenges under complex scenes, such as poor robustness against a variety of scenes, inaccurate separation of the target from similar goals, etc. To solve these problems, we present a novel small target detection method based on spatial–temporal information of infrared imageries. A nonoverlapping patch spatial–temporal tensor (NPSTT) model is established by sliding a window to obtain nonoverlapping patches in adjacent images. Moreover, the tensor capped nuclear norm (TCNN) is introduced, which approximates the tensor rank more accurately for local infrared images. TCNN regularization and NPSTT (TCNN-NPSTT) are adopted to detect the potential targets. Essentially, the procedure of extracting targets from the background is converted to the low rank and sparse tensor factorization. Besides, an efficient optimization scheme utilizing the alternating direction multiplier method (ADMM) is introduced to solve the proposed model. Experiments in various scenes show that NPSTT can obtain better performance against complex backgrounds than state-of-the-art baselines, using the evaluating indicators include the receiver operating characteristic curve (ROC), signal-to-clutter ratio gain (SCRG), and background suppression factor (BSF).
Who Are the Authors

ABSTRACTAn analysis of the authors of Australian library research articles published in The Australian Library Journal and Australian Academic & Research Libraries over the ten-year period 1985–1994 has been made to examine their characteristics: whether they wrote alone, or with others, what sex they were, and what were their institutional affiliations. Some comparisons are made with similar international studies, and some recommendations are made.
An agent-based approach to decluttering the interfaces of multi-UAV command and control systems

Information overload and cluttered user interfaces can lead to decreased situational awareness and lowered performance of human operators. Irrelevant data increases searching times for tasks requiring the identification of threats, causing delayed decisions. Cognitive burden on the user increases as displays become more cluttered, which results in increased operator stress leading to poor decision-making ability. To address this issue, we have designed an intelligent agentbased system for the automatic de-cluttering of a representative net-centric interface designed for controlling multiple unmanned aerial vehicles (UAVs) by a single operator. Our concept is called ARID, for Agent-based Reduction of Information Density. The ARID hypothesis is that intelligent agents can improve operator performance by deemphasizing those aspects of a display that can be inferred as less-important to the mission goals. ARID agents receive information about the world via data feeds provided by various net-centric sources. Each agent has an understanding of the user interface symbols that are used to represent various entities, terrain features, and zones. The agent also is provided with a mission goal which is used for inferring the relevance of a given symbol to the success of the mission goal. First level facts, such as spatial relationships, are calculated by supporting agents and assigned a BDU (belief/disbelief/uncertainty) value. A dynamic set of rules provides an inference mechanism by which an agent can infer new facts from the given assertions. We have developed a Subjective Logic-based Evidential Reasoning Network that explicitly deals with belief and uncertainty in the knowledge base, and is used to derive a relevancy belief for every UI symbol in the map display. Subjective Logic is used to combine values when different sources provide different results for the same symbol. User Interface agents apply the results of the relevancy beliefs and transform the display to minimize the apparent clutter caused by less relevant elements. Two transformations, transparency and grouping, are used in the current implementation.
Safety of Using Rituximab Therapy During COVID-19 Pandemic

Introduction: Our modern world is facing extraordinary circumstances while passing through a serious pandemic caused by the novel coronavirus (COVID-19) which may lead to multi-organ system failure & death. B cell depletion could compromise antiviral immunity, which makes the safety of rituximab use in the COVID 19 era unclear. Methods: All patients who had renal transplant from the 1st of March 2020 till 30th of July 2020 and registered in the OPTN were retrospectively reviewed. Patients were followed up to September 2020. We included patients who received rituximab induction therapy during this period. Exclusion criteria were patients who did not receive rituximab induction therapy, and patients with missing data about rituximab induction therapy. Measured outcome centered on patient survival until the end of the follow-up period. Results: During the first wave of COVID-19 pandemic from the 1st of March 2020 till 31st of July 2020, 9,095 patients had renal transplant and were registered in OPTN database. 8,770 patients had single organ transplant (kidney transplant) and 325 patients had dual organ transplant (kidney and pancreas). Out of this cohort, 114 patients received rituximab induction therapy. The total number of deaths was 127 patients (1.39%). Among those who received rituximab induction agent, only one patient died during the follow-up period. The cause of death for this patient was unknown. Conclusion: Our study showed that it is safe to use Rituximab as an important therapeutic agent during COVID-19 pandemic. However, special precautions and shielding are needed.
Boosting Transformers using Background Knowledge, or how to detect Drug Mentions in Social Media using Limited Data

—To process natural language and to extract information from text, transformers are currently the model of choice for many different tasks. Conversely, if the number of training examples is very limited, fine-tuning might not achieve the expected results, similarly as for other machine learning methods. In the past, a large range of different techniques have been presented to overcome this challenge, such as data augmentation or using distantly labelled data. In this work, we present our contribution to the drug mention detection of the BioCreative VII Challenge (Track 3), which includes a large number of negative, but only a small proportion of positive documents. In course of this, we explore different techniques to boost performance of a pre-trained transformer model. The combination of our transformer model and usage of background knowledge achieved the best results for our use case.
Millimeter-wave imaging for concealed weapon detection

Full body, real-time, millimeter-wave imaging systems have been developed at the Pacific Northwest National Laboratory for the detection of body-worn, concealed weapons and contraband at security checkpoints. These security systems employ methods derived from microwave holography techniques that utilize phase and amplitude information recorded over a two-dimensional aperture to reconstruct a focused image of the target. Millimeter-wave imaging is well suited for the detection of concealed weapons or other contraband carried on personnel, since millimeter waves are non-ionizing, readily penetrate common clothing material, and are reflected from the human body and any concealed items. In this paper, wide-bandwidth, three-dimensional, holographic microwave imaging techniques and a full-body, planar, millimeter-wave imaging system are described.
Matrix Based Feature Measurement and Extraction for Face Recognition

In this paper, a matrix based feature extraction and measurement method, i.e.: multi-column principle component analysis (MCPCA) is used to directly and effectively extract features from the matrix. We analyze the advantages of MCPCA over the conventional principal component analysis (PCA) and two-dimensional PCA (2DPCA), and we have successfully applied it into face image recognition. Extensive face recognition experiments illustrate that the proposed method obtains high accuracy, and it is more robust than previous conventional face recognition methods.
Optoelectronic oscillator with improved phase noise and frequency stability

In this paper we report on recent improvements in phase noise and frequency stability of a 10 GHz opto-electronic oscillator. In our OEO loop, the high Q elements (the optical fiber and the narrow bandpass microwave filter) are thermally stabilized using resistive heaters and temperature controllers, keeping their temperature above ambient. The thermally stabilized free running OEO demonstrates a short-term frequency stability of 0.02 ppm (over several hours) and frequency vs. temperature slope of −0.1 ppm/°C (compared to -8.3 ppm/°C for non thermally stabilized OEO). We obtained an exceptional spectral purity with phase noise level of -143 dBc/Hz at 10 kHz of offset frequency. We also describe the multi-loop configuration that reduces dramatically the spurious level at offset frequencies related to the loop round trip harmonic frequency. The multi-loop configuration has stronger mode selectivity due to interference between signals having different cavity lengths. A drop of the spurious level below −90 dBc was demonstrated. The effect of the oscillator aging on the frequency stability was studied as well by recording the oscillator frequency (in a chamber) over several weeks. We observed reversal in aging direction with logarithmic behavior of A ln(B t+1)-C ln(D t+1), where t is the time and A, B, C, D are constants. Initially, in the first several days, the positive aging dominates. However, later the negative aging mechanism dominates. We have concluded that the long-term aging behavioral model is consistent with the experimental results.
A parallel parking system for a car-like robot with sensor guidance

Abstract An automated parallel parking strategy for a car-like mobile robot is presented. The study considers general cases of parallel parking for a rectangular robot within a rectangular space. The system works in three phases. In scanning phase the parking environment is detected by ultrasonic sensors mounted on the robot and a parking position and manoeuvring path is produced if the space is sufficient. Then in the positioning phase the robot reverses to the edge of the parking space, avoiding potential collisions. Finally, in manoeuvring phase the robot moves to the parking position in the parking space in a unified pattern, which may require backward and forward manoeuvres depending on the dimensions of the parking space. Motion characteristics of this kind of robot are modelled, taking into account the non-holonomic constraints acting on the car-like robot. On the basis of the characteristics, a collision-free path is planned in reference to the surroundings. The strategy has been integrated into an automated parking system and implemented in a modified B12 mobile robot capable of safe parking in tight situations. The system is developed for an automated parking device to help vehicle drivers. It also shows the potential to be integrated into automobiles.
Design optimization of a high-sensitive absolute micro-pressure sensor

Purpose – The purpose of this study is to develop a piezoresistive absolute micro-pressure sensor for altimetry. For this application, both high sensitivity and high overload resistance are required. To develop a piezoresistive absolute micro-pressure sensor for altimetry, both high sensitivity and high-overload resistance are required. The structure design and optimization are critical for achieving the purpose. Besides, the study of dynamic performances is important for providing a solution to improve the accuracy under vibration environments. Design/methodology/approach – An improved structure is studied through incorporating sensitive beams into the twin-island-diaphragm structure. Equations about surface stress and deflection of the sensor are established by multivariate fittings based on the ANSYS simulation results. Structure dimensions are determined by MATLAB optimization. The silicon bulk micromachining technology is utilized to fabricate the sensor prototype. The performances under both static .