We derive an analytical model for 1/f noise in MOSFETs, highlighting a term that is often neglected in literature but becomes important for ultra-thin oxides. Furthermore, we identify an interesting relationship between the thermal noise of the gate impedance and the gate noise due to trapping/detrapping between the free carriers in the channel and the oxide traps, as well as the 1/f noise cross-correlation between drain and gate, showing that a single voltage noise generator is not enough to describe completely the 1/f noise. TCAD simulations are used to verify the model predictive capabilities.

In this paper we present our research on the optimisation of a deep neural network for 3D object detection in a point cloud. Techniques like quantisation and pruning available in the Brevitas and PyTorch tools were used. We performed the experiments for the PointPillars network, which offers a reasonable compromise between detection accuracy and calculation complexity. The aim of this work was to propose a variant of the network which we will ultimately implement in an FPGA device. This will allow for real-time LiDAR data processing with low energy consumption. The obtained results indicate that even a significant quantisation from 32-bit floating point to 2-bit integer in the main part of the algorithm, results in 5%-9% decrease of the detection accuracy, while allowing for almost a 16-fold reduction in size of the model.

Modulational stability of envelope soliton is studied in a quantum dusty plasma in three dimension. The Krylov-Bogoliubov-Mitropolsky method is applied to the three dimension plasma governing equations. A generalised form of Nonlinear Schr¨odinger equation is obtained whose dispersive term has a tensorial character. Stability condition is deduced abintio and the stability zones are plotted as a function of plasma parameters

In this work, we analyze the effect of residual frame timing offset and carrier frequency offset along with the presence of fractional Doppler on the bit error rate performance of OFTS system. We show in this work that channel equalization with appropriate channel estimates can compensate for the combined effect of the time-varying multipath channel (TVMC) as well as residual synchronization errors. We also present a unique time-domain channel estimation method for OTFS, which can be used along with the above-mentioned channel equalization. It is seen from the extensive Monte Carlo simulation results of the LDPC coded OTFS system that the estimation and compensation methods presented here provide necessary resilience properties to OTFS against the effects of TVMC channel and residual synchronization errors. A unified signal processing flow for OTFS and OFDM is also described in this work to motivate studies on coexistence between the two as well as to encourage investigations on a seamless transition from OFDM based system to OTFS based systems for future air interface design. This Work is submiited to an IEEE Journal for possible future publication.

We propose an image warping-based remote rendering technique for volumes that decouples the rendering and display phases. Our work builds on prior work that samples the volume on the client using ray casting and reconstructs a z-value based on some heuristic. The color and depth buffer are then sent to the client that reuses this depth image as a stand-in for subsequent frames by warping it according to the current camera position until new data was received from the server. We augment that method by implementing the client renderer using ray tracing. By representing the pixel contributions as spheres, this allows us to effectively vary their footprint based on the distance to the viewer, which we find to give better results than point-based rasterization when applied to volumetric data sets.

This is a short article, focusing on promoting more study on SEIR modeling by leveraging rich data and machine learning. We believe that this is extremely critical as many regions at the country or state/provincial levels have been struggling with their public health intervention policies on fighting the COVID-19 pandemic. Some recent published papers on mitigation measures show promising SEIR modeling results, which could shred the light for other policymakers at different community levels. We present our perspective on this research direction. Hopefully, we can stimulate more studies and help the world win this “war” against the invisible enemy “coronavirus” sooner rather than later.

The binary32 and binary64 floating-point formats provide good performance on current hardware, but also introduce a rounding error in almost every arithmetic operation. Consequently, the accumulation of rounding errors in large computations can cause accuracy issues. One way to prevent these issues is to use multiple-precision floating-point arithmetic. This preprint, submitted to Russian Supercomputing Days 2020, presents a new library of basic linear algebra operations with multiple precision for graphics processing units. The library is written in CUDA C/C++ and uses the residue number system to represent multiple-precision significands of floating-point numbers. The supported data types, memory layout, and main features of the library are considered. Experimental results are presented showing the performance of the library.

The discovery of ink mismatch provides important clues to pre-writing examiners to indicate whether a particular manuscript is written by a specific pen, or if a particular part of a note (e.g., signature) is written on a different ink compared to any other ink. In this paper, we show that the hyperspectral image (HSI) of the handwritten notes differs between visually similar inks. For this purpose, we have created the first hyperspectral data domain for a handwritten image in various blue and black inks, containing 33 visual reference bands. In unsupervised clustering technique, visual responses of inks fall into different groups to allow the separation of two different inks from the text of the questions. The same method when used in the RGB scan of these outputs fails to accurately distinguish the ink because it is very difficult to separate the ink from the optical range. HSI overcomes RGB deficiencies and allows better discrimination between inks.

It is required to calculate the stored reactive energy of an antenna in order to evaluate its Q factor. Although it has been investigated for a long time, some issues still need further clarification. The main difficulty involved is that the reactive energy of an antenna tends to become infinitely large when integrating the conventionally defined energy density in the whole space outside a small sphere containing the antenna. The reactive energy is usually made to be bounded by subtracting an additional term associated with the radiation fields. However, there exists no well-accepted accurate definition for this additional term that is valid for all cases. By re-checking the definition of reactive energies, a new formulation is proposed in this paper which can separate the reactive energy and the radiation energy explicitly based on source-potentials. The clearly defined reactive energy is bounded without necessity to subtract that additional term, and the resultant formulae are easy to implement.

An ad-hoc network using unmanned aerial vehicle (UAV) as relay, has been gaining significant attention especially for communication between far apart ground terminals during emergency conditions due to agility and resilience requirements. However, UAV hovering localization (HL) and power allocation (PA) are the key issues in such relay based flying ad hoc networks (FANETs). In this work, we propose a framework to jointly optimize the placement of rotary-wing UAV hovering and PA for maximization of network throughput in a three node decode-and-forward (DF) FANET. Specifically, we develop three different optimization schemes, (i) individual UAV HL optimization with a fixed allocated power, (ii) individual PA optimization for a fixed UAV HL, (iii) jointly optimal UAV HL and PA. For every optimization problem, the underlying convexity is proved and the global optimal solutions have been obtained. Further, we provide novel analysis by utilizing the characteristics of sigmoidal function thereby obtaining the closed-form and semi-closed-form expressions respectively for the globally-optimal solutions for individual and joint optimization schemes. The analytical results are numerically validated and various optimal design insights are discussed. It has been found that the proposed joint optimal scheme shows an average performance enhancement of 52% over benchmark scheme.

We report about the application of state-of-the-art deep learning techniques to the automatic and interpretable assignment of ICD-O3 topography and morphology codes to free-text cancer reports. We present results on a large dataset (more than 80 000 labeled and 1 500 000 unlabeled anonymized reports written in Italian and collected from hospitals in Tuscany over more than a decade) and with a large number of classes (134 morphological classes and 61 topographical classes) for which we obtained the approval from the institutional ethics committee (CEAV 14081 oss 27/11/2018). We compare alternative architectures in terms of prediction accuracy and interpretability and show that our best model achieves a multiclass accuracy of 90.3% on topography site assignment and 84.8% on morphology type assignment. We found that in this context hierarchical models are not better than flat models and that an element-wise maximum aggregator is slightly better than attentive models on site classification. Moreover, the maximum aggregator offers a way to interpret the classification process.

Forgery investigation and detection has been a relevant topic of interest for human beings since ages. Important messages written and transported by kings in old ages were sealed with signatures and stamps to achieve this purpose. But with the advent of digital technology, forgery detection has become even more important since tools for forgery have become vast as well. In this paper a technique based on pixel clustering has been introduced for detection of modification, alteration or forgery done with a different ink color pen. Hyperspectral images are used for ink mismatch detection in a handwritten note. We propose ink classification based on pixel intensities values present in all the bands of hyperspectral images of the handwritten note. Our proposed technique is quite simple yet effective in detecting ink mismatch with relatively high accuracy.

Hyperspectral imaging allows for analysis of images in several hundred of spectral bands depending on the spectral resolution of the imaging sensor. Hyperspectral document image is the one which has been captured by a hyperspectral camera so that the document can be observed in the different bands on the basis of their unique spectral signatures. To detect the forgery in a document various Ink mismatch detection techniques based on hyperspectral imaging have presented vast potential in differentiating visually similar inks. Inks of different materials exhibit different spectral signature even if they have the same color. Hyperspectral analysis of document images allows identification and discrimination of visually similar inks. Based on this analysis forensic experts can identify the authenticity of the document. In this paper an extensive ink mismatch detection technique is presented which uses KMean Clustering to identify different inks on the basis of their unique spectral response and separates them into different clusters.

Software Testing is the process of evaluating a software program to ensure that it performs its intended purpose. Software testing verifies the safety, reliability, and correct working of software. The growing need for quality software makes software testing a crucial stage in Software Development Lifecycle. There are many methods of testing software, however, the choice of method to test a given software remains a major problem in software testing. Although, it is often impossible to find all errors in software, employing the right combination of methods will make software testing efficient and successful. Knowing these software testing methods is the key to making the right selection. This paper presents a comprehensive study of software testing methods. An explanation of Testing Categories was presented first, followed by Testing Levels (and their comparison), then Testing Techniques (and their comparison). For each Testing Levels and Testing Techniques, examples of some testing types and their pros and cons were given with a brief explanation of some of the important testing types. Furthermore, a clear and distinguishable explanation of two confused and contradictory terms (Verification and Validation) and how they relate to Software Quality was provided.

This paper carries out a comprehensive review on the existing radial distribution test systems available for the power system distribution research area. The information available from this review can be used to establish a database of cases suitable for distribution operation and planning studies. The importance of distribution system, the structure of the system in the grid and the various configurations of the distribution network are provided.

A document by Himanshu Buckchash . Click on the document to view its contents.

This paper reviews some real-world problems modeling as Probabilistic Traveling Salesman Problem (PTSP), by presenting the important results found in the literature. It illustrates the usefulness of the inclusion of probabilistic elements in deterministic models. We propose a new modeling of the PTSP by the deviations of the routing of a robot in order to avoid obstacles which are not foreseen in its path. The Probabilistic Traveling Salesman Problem(PTSP) is a variation of the classic Traveling Salesman Problem (TSP) where each node i is present with probability pi. The solution for the PTSP consists in finding an a priori tour that visits all the cities that minimizes the expected length of the tour. From the litterateur the PTSP is NP-Complete, therefore the execution time is a prime factor in its resolution. In the last of his paper we present a new parallel Tabu search heuristic for solving PTSP by using the Open MPI environment.

This paper proposes a novel tariff regime for peerto-peer energy trading, with an aim to increase transmission efficiency and grid stability by penalising long distance power transactions. In this scheme a portion of the transacted energy is withheld based on the electrical distance between buying and selling parties, calculated here according to the Klein Resistance Distance. This tariff regime is simulated using a dataset of producers and consumers over a 24-hour period. First, a notional marketplace equilibrium simulation is performed, in which consumers can optimally activate demand response resources to exploit local availability of energy. Consumers are observed to move some demand away from peak times to make use of local generation availability. These simulated market out-turns are then used as inputs to a time series power flow analysis, in order to evaluate the network's electrical performance. The regime is found to decrease grid losses and the magnitude of global voltage angle separation. However, the metric whereby taxes are calculated is found to be too skewed in the utility's favour and may discourage adoption of the peer-to-peer system. The method also attempts to encourage regulatory adoption by existing grid operators and utilities. Some counter-intuitive allocations of tokenised energy occur, owing to specific consumers' demand profiles and proximity to generators.