Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Search in a Panoramic Image Database for Long-term Visual Localization

In this work, we focus on a localization technique that is based on image retrieval. In this technique, database images are kept with GPS coordinates and the geographic location of the retrieved database image serves as an approximate position of the query image. In our scenario, database consists of panoramic images (e.g. Google Street View) and query images are collected with a standard field-of-view camera in a different time. While searching the match of a perspective query image in a panoramic image database, unlike previous studies, we do not generate a number of perspective images from the panoramic image. Instead, taking advantage of CNNs, we slide a search window in the last convolutional layer belonging to the panoramic image and compute the similarity with the descriptor extracted from the query image. In this way, more locations are visited in less amount of time. We conducted experiments with state-of-the-art descriptors and results reveal that the proposed sliding window approach reaches higher accuracy than generating 4 or 8 perspective images.
Improved Multi-access Coded Caching Schemes From Cross Resolvable Designs

Recently multi-access coded caching schemes with number of users different from the number of caches obtained from a special class of resolvable designs called Cross Resolvable Designs (CRDs) have been reported and a new performance metric called rate-per-user has been introduced by Digvijay et al (“Multi-Access Coded Caching Schemes From Cross Resolvable Designs” in IEEE Transactions on Communications, May 2021). In this paper, we present a generalization of this work resulting in multi-access coded caching schemes with improved rate-per-user.
Second-Order Asymptotically Optimal Outlying Sequence Detection with Reject Option

Motivated by practical machine learning applications, we revisit the outlying sequence detection problem (Li et al., TIT 2014) and derive fundamental limits of optimal detection when the reject option is allowed for outlying sequences. In the considered outlying sequence detection (OSD) problem, one is given multiple observed sequences, where all sequences are generated i.i. d. from a nominal distribution with at most one exception. The task is to discern the outlying sequence that is generated according to an anomalous distribution. In OSD, the nominal and anomalous distributions are unknown. In this paper, we consider the case where there is a reject option for the OSD, i.e., we reject the samples as insufficient for making a reliable decision (cf. Bartlett et al., JMLR 2008). We study the tradeoff among the probabilities of misclassification error, false alarm and false reject for tests that satisfy weak conditions on the rate of decrease of these error probabilities as a function of sequence length. We propose a second-order asymptotically optimal test that provides a finite sample approximation to the error probabilities.
Binaural Reproduction From Microphone Array Signals Incorporating Head-Tracking

Binaural reproduction over headphones is becoming popular with applications in teleconferencing, virtual reality and entertainment. Many binaural reproduction methods are developed and studied extensively for spherical and other specially-designed arrays. However, some applications require simpler arrays, such as wearable or mobile. Recently, a binaural reproduction method was developed for such arrays based on binaural signals matching (BSM). While showing that BSM can produce accurate binaural signals, the study was limited to two array configurations and head-tracking was not incorporated. As a follow-up, this paper studies BSM with a varying number of microphones in a semi-circular configuration and investigates a solution for head-tracking. Simulations show that placing two microphones close to the ear positions of the head related transfer functions significantly improves reproduction with a static head. However, microphones placed away from ear positions are needed for head-tracking. This is then subjectively studied in a listening experiment with a semi-circular array, showing that compensating for head-rotations is important for accurate spatial perception but may introduce timbral artifacts due to the limited number of microphones.
The pull evolution: from Kanban to customised token-based systems

This work formalises a new approach for material flow control, i.e. the so-called customised token-based systems (CTBS). This approach represents a natural evolution from classical pull systems, such as Kanban. The article first describes the flow of materials (orders or tasks) from a management point of view, and discusses exact and approximate approaches to tackle the problem. The origin of Customised Production Control systems is explained, as well as the main advantages: high degree of generalisation, enhancement of the space of solutions and adaptation to the manufacturing environment. Next, we propose a method based on cross-entropy for the optimisation of CTBS. The computational experience carried out in a number of scenarios shows the CTBS to be profitable. Furthermore, the resulting control mechanisms seem to be relatively easy to implement in real-manufacturing environments.
"Paradoxes" of carrier lifetime measurements in high-voltage SiC diodes

For Silicon Carbide (SiC) high-voltage rectifier diodes, contradictions appear when the most important parameter of the diodes, the minority carrier lifetime, is measured by different techniques. A qualitative analysis and a computer simulation have been carried out to clarify the origin of these contradictions. For 4H-SiC p/sup +/n diodes with 6 kV blocking capability, data on residual voltage drop at high current densities, switch-on time, reverse current recovery, and post-injection voltage decay are analyzed. It is shown that the whole set of experimental data can be explained by the existence of a thin (l/spl sim/0.1 /spl mu/m) layer near the metallurgical boundary of the p/sup +/n junction with very small carrier lifetime /spl tau//sub l/ that is essentially smaller than the carrier lifetime /spl tau/ across the remaining part of the 50-/spl mu/m n-base. It is emphasized that the existence of such a layer allows, under certain conditions, the combination of a relatively low residual forward voltage drop and very fast reverse recovery. Approaches to minority carrier lifetime measurements are discussed.
Heavily carbon-doped InGaP/GaAs HBT's with buried polycrystalline GaAs under the base electrode

This paper describes a new approach to fabricating InGaP/GaAs heterojunction bipolar transistors (HBT's) with a high cutoff frequency (f/sub T/), high maximum oscillation frequency (f/sub max/), and low external collector capacitance (C/sub bc/). To attain a high f/sub T/ and f/sub max/, a heavy carbon-doping (1.3/spl times/10/sup 20/ cm/sup -3/) technique was used with a thin (30-nm-thick) GaAs base layer, while for low C/sub bc/, low-temperature gas-source molecular-beam epitaxial growth on SiO/sub 2/-patterned substrates was used to bury high-resistance polycrystalline GaAs under the base electrode. An f/sub T/ of 120 GHz and an f/sub max/ of 230 GHz were achieved for three parallel 0.7/spl times/8.5 /spl mu/m HBT's with an undoped-collector structure, and an f/sub T/ of 170 GHz and an f/sub max/ of 160 GHz were obtained for a single 0.9/spl times/10 /spl mu/m HBT with a ballistic-collection-transistor structure. Compared to HBT's without buried poly-GaAs, the maximum stable gain was improved by 1.2 dB in the 0.7/spl times/8.5 /spl mu/m HBT and by 2.3 dB in the 0.9/spl times/10 /spl mu/m HBT due to the reduction in C/sub bc/. These results show the high potential of the proposed HBT's for high-speed digital and broadband-amplifier applications.
A hybrid and fast deep learning framework for Covid-19 detection via 3D Chest CT Images

In this paper, we present a hybrid deep learning framework named CTNet which combines convolutional neural network (CNN) and transformer together for the detection of COVID-19 via 3D chest CT images. It consists of a CNN feature extractor module with SE attention to extract sufficient features from CT scans, together with a transformer model to model the discriminative features of the 3D CT scans. Compared to previous works, CTNet provides an effective and efficient method to perform COVID-19 diagnosis via 3D CT scans with data resampling strategy. Advanced results on a large and public benchmarks, COV19-CT-DB database, was achieved by the proposed CTNet with a macro F1 score of 88.21% on the validation set, which lead ten percentage over the state-of-the-art baseline approach proposed together with the dataset. Notably, the inference speed of the proposed framework is about ten times faster than that of the typical CNN frameworks which make it more promising in actual applications.
Residual Dilated U-net For The Segmentation Of COVID-19 Infection From CT Images

Medical imaging such as computed tomography (CT) plays a critical role in the global fight against COVID-19. Computer-aided platforms have emerged to help radiologists diagnose and track disease prognosis. In this paper, we introduce an automated deep-learning segmentation model, which builds upon the current U-net model, however, leverages the strengths of long and short skip connections. We complemented the long skip connections with a cascaded dilated convolution module that learns multi-scale context information, compensates the reduction in receptive fields, and reduces the disparity between encoded and decoded features. The short connections are considered in utilizing residual blocks as the basic building blocks for our model. They ease the training process, reduce the degradation problem, and propagate the low fine details. This enables the model to perform well in capturing smaller regions of interest. Furthermore, each residual block is followed by a squeeze and excitation unit, which stimulates informative features and suppresses less important ones, thus improving the overall feature representation. After extensive experimentation with a dataset of 1705 COVID-19 axial CT images, we demonstrate that performance gains can be achieved when deep learning modules are integrated with the basic U- net model. Experimental results show that our model outperformed the basic U-net and ResDUnet model by 8.1% and 1.9% in dice similarity, respectively. Our model provided a dice similarity measure of 85.3%, with a slight increase in trainable parameters, thus demonstrating a huge potential for use in the clinical domain.
A transformer-based framework for automatic COVID19 diagnosis in chest CTs

Automated diagnosis of covid19 in chest CTs is becoming a clinically important technique to support precision and efficient diagnosis and treatment planning. A few efforts have been made to automatically diagnose the COVID-19 in CTs using CNNs, and the task still remains a challenge. In this paper, we present a transformer-based framework for COVID19 classification. We attempt to expand the adaption of vision transformer as a robust feature learner to the 3D CTs to diagnose the COVID-19. The framework consists of two main stages: lung segmentation using UNet followed by the classification, in which the features extracted from each CT slice using Swin transformer in a CT scan are aggregated into 3D volume level feature. We also investigated the performance of using the robust CNNs (BiT and EfficientNetV2) as backbones in the framework. The dataset from the ICCV workshop: MIA-COV19D, is used in our experiments. The evaluation results show that the method with the backbone of Swin transformer gain the best F1 score of 0.935 on the validation dataset, while the CNN based backbone of EfficientNetV2 has the competitive classification performance with the best precision of 93.7%. The final prediction model with Swin transformer achieves the F1 score of 0.84 on the test dataset, which doesn’t require an additional post-processing stage.
Common Image Source Search for Multiple Spatial Room Impulse Response Measurements

Image source reversion algorithms estimate room geometry from measured spatial room impulse responses by locating image sources. However, most of the methods have been limited to a single loudspeaker position and to convex rooms. Earlier, we have proposed a method that combines image sources from multiple receiver locations to find more image sources accurately even in concave rooms. Here, we extend the method to cope with multiple sound sources, thus the image source search can utilize measurements from multiple source and receiver positions simultaneously. The search method is tested with two measurement datasets and is found to improve the result compared to the previous algorithm. For the future applications, we also propose methods for generating a concave room model from the found reflection planes, for estimating material filters for each surface, and for interpolating between measured source locations.
Rate-Distortion Problems of the Poisson Process: a Group-Theoretic Approach

We study rate-distortion problems of a Poisson process using a group theoretic approach. By describing a realization of a Poisson point process with either point timings or inter-point intervals and by choosing appropriate distortion measures, we establish rate-distortion problems of a homogeneous Poisson process as ball-or sphere-covering problems for realizations of the hyperoctahedral group in $\mathbb{R}^{n}$. Specifically, the realizations we investigate are a hypercube and a hyperoctahedron. Thereby we unify three known rate-distortion problems of a Poisson process (with different distortion measures, but resulting in the same rate-distortion function) with the Laplacian-$\ell_{1}$ rate-distortion problem.
Evaluating volumetric and slice-based approaches for COVID-19 detection in chest CTs

The paper presents a comparative analysis of several distinct approaches based on deep learning for identifying COVID-19 cases in chest CTs. A first approach is a volumetric one, involving 3D convolutions, while other two approaches perform at first slice-wise classification and then aggregate the results at the volume level. The experiments are carried on the COV19-CT-DB dataset, with the aim of addressing the challenge raised by the MIA-COV19D Competition within ICCV 2021. Our best results reach a macro F1 score of 92.34% on the validation subset and 90.06% on the test set, obtained with the volumetric approach which was ranked second in the competition. Its performance can be further improved by a simple trick, using semi-supervised training in the form of self-training, technique which proved to bring a consistent increase over the reported F1-score on the validation subset.
On the Design Criteria for Suppression of Secondary Flows in Centrifugal and Mixed Flow Impellers

In this paper, for the first lime, a set of guide-lines are presented for the systematic design of mixed flow and centrifugal compressors and pumps with suppressed secondary flows and a uniform exit flow field. The paper describes the shape of the optimum pressure distribution for the suppression of secondary flows in the impeller with reference to classical secondary flow theory. The feasibility of achieving this pressure distribution is then demonstrated by deriving guide-lines for the design specifications of a 3D inverse design method, in which the blades are designed subject to a specified circulation distribution or Display Formula2πrV¯θ. The guide-lines will define the optimum choice of the blade loading or Display Formula∂rV¯θ/∂m and the stacking condition for the blades. These guide-lines are then used in the design of three different low specific speed centrifugal pump impellers and a high specific speed industrial centrifugal compressor impeller. The flow through all the designed impellers are computed numerically by a 3D viscous code and the resulting flow field is compared to that obtained in the corresponding conventional impeller. The results show consistent suppression of secondary flows in all cases. The design guide-lines are validated experimentally by comparing the performance of the inverse designed centrifugal compressor impeller with the corresponding conventional impeller. The overall performance of the stage with the inverse designed impeller with suppressed secondary flows was found to be 5% higher than the conventional impeller at the peak efficiency point. Exit flow traverse results at the impeller exit indicate a more uniform exit flow than that measured at the exit from the conventional impeller.Copyright © 1997 by ASME
Electronic Data Collection for Rockfall Analysis

Rockfall analysis traditionally has used conventional stationery tools, that is, pencil and paper, for data collection. Traditional methodologies are being revisited with the advent of personal digital assistants (PDAs) or pen-based computers that enable field data to be collected electronically. The advantages over data collection with pencil and paper include automatic error and data integrity checks during data input and the elimination of manual data entry. PDAs also allow automatic branching to solicit data input on the basis of previous data entered and support for code or scripting, which can be used to create unique files names from the data entered. These advantages are illustrated in an electronic data collection methodology as implemented within a rockfall hazard rating system for the Tennessee Department of Transportation.
Experimental Analysis of Overall Thermal Properties of Parabolic Trough Receivers

The heat loss of a receiver in a parabolic trough collector plays an important role in collector performance. A number of methods have been used to measure the thermal loss of a receiver tube depending on its operating temperature. This paper presents methods for measuring receiver heat losses including field measurements and laboratory set-ups both based on energy balances from the hot inside of the receiver tube to the ambient. Further approaches are presented to measure and analyze the temperature of the glass envelope of evacuated receivers and to model overall heat losses and emissivity coefficients of the receiver. Good agreement can be found between very different approaches and independent installations. For solar parabolic trough plants operating in the usual 390°C temperature range, the thermal loss is around 300W/m receiver length.
Testability-Enhancing Resynthesis of Reconfigurable Scan Networks

Reconfigurable Scan Networks (RSNs) have to be tested before they can be used for post-silicon validation, diagnosis or online reliability management. Even a single stuck-at fault in the switch logic of an RSN can corrupt the scan paths and make instruments inaccessible. Testing the switch logic of an RSN is a complex sequential test problem. The existing test schemes for RSNs rely on the assumption that a fault in the switch logic will be detected by the altered length of the erroneously activated scan path. However, often this assumption does not hold and faults in the switch logic remain undetected.In this paper, an automated testability-enhancing resynthesis is presented. First, the testability of the initial RSN is accurately analyzed. If any single fault in the switch logic is undetectable by the altered path length, a small number of scan cells is inserted into the RSN. The presented scheme is applicable to arbitrary RSN designs and is compliant with state-of-the-art test methods and the applicable standards. The experimental results show the efficacy, the efficiency and the scalability of the approach.
Evaluating Risk in Construction–Schedule Model (ERIC–S): Construction Schedule Risk Model

Research was undertaken to develop a method to assist in the determination of the lower and upper activity duration values for schedule risk analysis by program evaluation and review technique analysis or Monte Carlo simulation. A belief network was the modeling environment used for this purpose, and the resulting model was named Evaluating Risk in Construction–Schedule Model. The development of the belief network model consisted of four steps. First, construction schedule risks were identified through a literature review, an expert review, and a group review by a team of experts. Second, cause effect relationships among these risks were identified through an expert survey. This led to the development of the structure of belief network model. Third, probabilities for various combinations of parents for each risk variable were obtained through an expert interview survey and incorporated into the model. Finally, sensitivity analysis was performed. The model was tested using 17 case studies with very good re.
Technique for the calibration of thermal voltage converters using a Josephson waveform synthesizer and a transconductance amplifier

This paper describes a simple and effective technique for calibrating thermal converters for an ac–dc voltage transfer difference using a Josephson waveform synthesizer and a transconductance amplifier. Preliminary measurements and uncertainty analysis confirm the possibility of achieving systematic uncertainties below 0.1 µV V−1 at frequencies up to 1 kHz.
Information Leakage in Index Coding

We study the information leakage to a guessing adversary in index coding with a general message distribution. Under both vanishing-error and zero-error decoding assumptions, we develop lower and upper bounds on the optimal leakage rate, which are based on the broadcast rate of the subproblem induced by the set of messages the adversary tries to guess. When the messages are independent and uniformly distributed, the lower and upper bounds match, establishing an equivalence between the two rates.
MEASURING THE CONTEXT OF INFORMATION SYSTEMS USE

An important research stream in information systems (IS) is predicting and explaining technology acceptance among users. This has been and remains of clear importance to system developers and IS managers, and is therefore of interest to the IS research community. It is known that the context in which a system is used affects the determinants of the user’s acceptance of it. Two new multi-item constructs are developed and validated to measure the extent to which a user is using a system within each of two such contexts: utilitarian, where the system has some useful application, and recreational, where the system is being used for enjoyment. Context definitions were used to create 14 candidate items which were initially refined by a series of pre-study interviews, refined further in a study of four systems, and validated in one applied testing situation. The resulting scales exhibit good psychometric properties - reliability and convergent, discriminant and factorial validity, and show a high correlation with self reported context of use. The scales will be of use to researchers working in the area of technology adoption.
Energy Efficiency and Capacity Modeling for Cooperative Cognitive Networks

Cooperative relaying has recently appeared as one of the widely recognized features for future wireless communication systems. The great potential of cooperative communication in increasing system capacity and enhancing power efficiency has attracted large efforts over the last few years. In this paper, we propose a Cooperation Loop as a reference model for all algorithms in relay based cooperative wireless networks. Using this model, we discuss cooperative relay based protocols in IEEE 802.11 standards and limits posed to cognitive approaches. We show the potential location area of relay nodes as well as the performance bounds of capacity gain, delay and power efficiency achieved in relay based scenarios for any cooperative cognitive algorithms.