Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer Navigation using Audio and Video Aid for Amputees and Parkinsons Patients

A majority of disadvantaged children in underdeveloped countries cannot attend school due to the dearth of proper infrastructure and inadequate reading materials. Due to the pandemic, schools used various technologies to migrate quickly to online classrooms. This has greatly benefitted People with Disabilities since it made learning more accessible by removing physical impediments such as lack of infrastructure. However, ease of computer navigation still poses an issue for the disabled. This paper presents a novel approach to address this issue. We make use of Face Detection and Speech Recognition to simulate certain mouse and keyboard commands. The user can simply move their face to advance the cursor and voice command to perform the desired operation. For that the system uses the in-built camera and microphone, our solution is extremely cost-effective as compared to other commercial solutions.
Approximate Optimal Tracking Control for a Class of Discrete-time Nonlinear Systems based on GDHP Iterative Algorithm

In this paper, an iterative globalized dual heuristic programming (GDHP) method is applied to optimal tracking control of a class of discrete affine nonlinear systems. In order to further consider the control signal in the varying process, this paper adds a control error difference term to the quadratic cost function. Through system transformation, the optimal tracking problem is transformed into designing the optimal regulator problem for tracking error dynamics. Then, the GDHP algorithm is introduced to deal with the optimal regulation problem. Three neural networks are used as parameter structures to realize the algorithm, which aims at approximating cost function, control law and error dynamics respectively. A simulation example is given to verify the effectiveness of the method.
Gas Ash Microscopic Image Segmentation with SLIC and Mean Shift

For the problems of long processing time, fragmentary clustering regions and poor segmentation accuracy of gas ash microscopic image segmentation with traditional mean shift algorithm, this paper proposes an improved image segmentation method combining simple linear iterative clustering (SLIC) and mean shift. Firstly, SLIC algorithm is employed to generate a certain number of superpixels of gas ash microscopic images. Then, color, texture, and position information of each superpixel are extracted, and mapped into feature space for mean shift clustering. Finally, an adaptive region merging algorithm is used to further merge similar regions. The experimental results indicate that as a sample mode, the generated superpixels are more effective than original pixels in mean shift clustering, that is, the processing time is greatly shorten and clustering regions are more complete. Owing to the addition of texture features, the generated target areas are more accurate, especially for mosaic structure. This study can offer a support for further automatic component recognition, which is the foundation of industrial robot in analysis of gas ash.
Integer Programming Methods for a Vessel Scheduling Problem

In a previous paper, Appelgren (Appelgren, L. 1969. A column generation algorithm for a ship scheduling problem. Trans. Sci. 3 53--68.), a decomposition algorithm for a class of vessel scheduling problems was presented. In some problems, the algorithm gives fractional solutions that cannot be interpreted as feasible schedules. This paper treats two integer programming methods that can be used to resolve these cases. The cutting plane method that was first tested was abandoned because it was not able to solve all the test problems. The second method is a branch-and-bound algorithm, where the branching is performed on one of the “essential” fractional variables and where the bounds are obtained by the decomposition algorithm. All fractional problems that have been found by simulation or in regular use of the algorithm have been solved, mostly with one branching only. There are fundamental difficulties in combining these integer programming methods with the Dantzig-Wolfe decomposition, since the constraints generated in the master program have to be taken into account in the solution of the subprograms. The success in this case is due to the simple structure of the master LP problem.
Food Recognition using Transfer Learning

This paper proposes the application of Transfer Learning in classifying a food dish. Traditional methods involve using Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), which are highly inefficient when the classes in a dataset increase. Therefore, more modern ways of classification become vital to adapt to evolving human tastes. Thus, we have achieved excellent results by leveraging Neural Networks in the form of ResNet, VGG19, EfficientNet, and DenseNet. Additionally, a web crawler has been integrated to provide the recipe for the same dish.
Optimal Placement of Multi-Controller Considering Load Balance and Control Delay in Software Defined Satellite Network

In the space-ground integrated network (SGIN), the deployment of multi-controllers has become a key factor affecting the efficiency of the control and management. In order to adapt to the control delay and resource constraints requirements of SGIN, this paper studies the multi-controller deployment problem considering control delay and load balance. We make the following contributions. First, we evaluate the average network response delay of low-orbit satellite nodes based on the satellite network topology and satellite network resource status. Furthermore, the load balance is considered in the proposed model to satisfy the resource constraints of SGIN. Second, a new heuristic algorithm is proposed to effectively improve the convergence speed of searching feasible solutions based on the improved immune algorithm. Third, the proposed method is numerically demonstrated considering a simulated SGIN architecture based on STK.
A Unified Approach for Managing Heterogeneous Processing Elements on FPGAs

FPGA designs do not typically include all available processing elements, e.g., LUTs, DSPs and embedded cores. Additional work is required to manage their different implementations and behaviour, which can unbalance parallel pipelines and complicate development. In this paper we introduce a novel management architecture to unify heterogeneous processing elements into compute pools. A pool formed of E processing elements, each implementing the same function, serves D parallel function calls. A call-and-response approach to computation allows for different processing element implementations, connections, latencies and non-deterministic behaviour. Our rotating scheduler automatically arbitrates access to processing elements, uses greatly simplified routing, and scales linearly with D parallel accesses to the compute pool. Processing elements can easily be added to improve performance, or removed to reduce resource use and routing, facilitating higher operating frequencies. Migrating to larger or smaller FPGAs thus comes at a known performance cost. We assess our framework with a range of neural network activation functions (ReLU, LReLU, ELU, GELU, sigmoid, swish, softplus and tanh) on the Xilinx Alveo U280.
Bearing fault diagnosis under different operating conditions based on source domain multi sample joint distribution adaptation

In order to solve the problem of unsatisfactory Bearing fault diagnosis under different operating conditions. In this paper, Source domain Multi sample Joint Distribution Adaptation (SM-JDA) method is applied to the fault diagnosis of variable operating conditions bearing. The method first uses stratified random sampling to sample of source domain and obtain multiple source domain samples. Secondly, the kernel method is used to project the source domain and the target domain into the Reproducing Kernel Hilbert Space (RKHS) to reduce the distribution difference between the source domain and the target domain. The final classification result is determined by the majority consent criterion through the Multiclass Relevance Vector Machine (M-RVM) output target domain classification result. Compared with other methods, the method in this paper is proved to be feasible and effective by performing eighteen tasks on the public dataset.
A neighborhood improvement algorithm for rectilinear Steiner trees

A heuristic algorithm for constructing a rectilinear Steiner tree (RST) from a rectilinear minimum-spanning tree of a given point set is described. The heuristic algorithm is based on replacing neighborhood structures of an independent set of rectilinear minimum-spanning tree points by their optimal RSTs. The time complexity of each iteration of the algorithm is O(n log n), where n is the cardinality of the input point set. <<ETX>>
Whodunnit? A case-study of an investigation to find a multi-core scalability bottleneck

Programming autograding at scale is a requirement for many online learning applications. This paper is a case study report of the hunt for the bottleneck that was hindering the multicore scalability of one such autograder - EvalPro. EvalPro is the autograding module of our in-house online learning application Bodhitree, and is used regularly for program autograding in our introductory programming course. A baseline experiment found that the maximum autograding throughput provided by EvalPro did not increase much beyond that provided with 8 cores. We however had larger servers (e.g. 64 core machines) for use for a scaled up deployment of the application. To address this issue, we followed an empirical approach first, hypothesizing various bottlenecks and checking whether removing them increased scalability. We followed this with profiling experiments on microbenchmarks that finally helped us identify the bottleneck: an off-the-shelf program not a part of our application, but was simply used by our application. Our main conclusions from the study are that bottleneck identification studies remain complex and time consuming and must be done systematically, and that stable well-known, off the shelf programs may also not display multi-core scalability, as they may not have been built for such a purpose.
Tactile Signal Reconstruction Assisted by Video Processing

Because of prior knowledge, a person will deduce the strength of the contact force using only visual input. This research proposes a method for cross-modal signal processing to recover tactile information by using visual input. According to our method, visual information supplements tactile information as an auxiliary source. Our paper starts with a group of photos (GOPs) and uses them as input. Second, to identify regions of in-terest(ROI), we employ the low-rank foreground based attention mechanism (LAM). In order to determine contact force in video frames, we prefer to suggest a linear regression convolutional neural network (LRCNN). Results from experiments support our ability to rebuild sentience. Additionally, simulation results as compared to alternative methods of work, our method can save network costs while also increasing the accuracy of material identification.
Impact of Environment on Navigation Performance for Autonomous Mobile Robots in Crowds

We have been studying methods to improve both the safety and efficiency of navigation for autonomous mobile robots in crowds by switching between multiple control policies, including deep reinforcement learning. Compared to existing methods, we have shown that the method improves the collision rate (safety metrics) even in narrow corridors, but more detailed analysis is needed to address deviations in results due to environments. In this paper, we clarify the impact of different environments on navigation performance by conducting experiments under different policy-switching conditions in multiple environments. Moreover, we examine the ratio of policies used in each environment and the specific switching procedures. The results indicate what policies should be used in the target environment.
Finite element calculation of three‐dimensional hot forging

A three-dimensional finite, element model for the simulation of isothermal hot forging is presented. The material behaviour is assumed to be incompressible visco-plastic (Norton–Hoff law), with the associated friction law. The velocity field is calculated with a finite element approximation using eight node hexahedral or six node prismatic elements. An explicit Euler scheme is used for time integration. Simulations of compression of a cubic block and horizontal cylinder are performed. The computed results are compared with experimental measurements made on special Plasticine.
Audio-Visual Class Association Based on Two-stage Self-supervised Contrastive Learning towards Robust Scene Analysis

This paper proposes a novel audio and visual class association method based on contrastive learning that can obtain not only one-to-one but also one-to-many, many-to-many, and even no correspondence between audio and visual classes. The proposed method consists of two training stages. In the first stage, for “correspondence” training, using self-supervised contrastive learning, one-to-one, one-to-many, and many-to-many correspondences are trained under a criterion that corresponding AV pairs become close and non-corresponding pairs are far. In the second stage, for “non-correspondence” training, those relationships are acquired through contrastive learning using a dataset consisting of pairs of visual and audio classes that have no correspondence. To build such a dataset, we utilize the trends of change in the class embeddings and split a set of all the classes into two subsets, with AV correspondence and without AV correspondence. The trained model with the proposed method was evaluated by the F1-score for the class embedding, and indoor experiment on mapping of two sound sources. As a result, the F1-score was 74.7% after the first stage, and it improved by 1.86 points to 76.6% after the second stage, confirming, the proposed method is effective for mapping between general audio and visual classes, including one-to-many, many-to-many, and non-corresponding classes. Indoor experiment revealed that our model could predict correct correspondence even in real environment.
Efficient algorithms for sequence analysis with concave and convex gap costs

We describe algorithms for two problems in sequence analysis: sequence alignment with gaps (multiple consecutive insertions and deletions treated as a unit) and RNA secondary structure with single loops only. We make the assumption that the gap cost or loop cost is a convex or concave function of the length of the gap or loop, and show how this assumption may be used to develop efficient algorithms for these problems. We show how the restriction to convex or concave functions may be relaxed, and give algorithms for solving the problems when the cost functions are neither convex nor concave, but can be split into a small number of convex or concave functions. Finally we point out some sparsity in the structure of our sequence analysis problems, and describe how we may take advantage of that sparsity to further speed up our algorithms.
Aerial Reconfigurable Intelligent Surface Assisted Maritime Wireless Communications

Millimeter wave (mmWave) technology is capable of enhancing the capacity of maritime wireless systems. However, mountains and high-rise buildings impose high-frequency signal blockage, which limits its practical implementation in these environments. In order to mitigate the blockage problems of signal propagations caused by onshore mountains and mountainous near-shore islands, we first propose an aerial reconfigurable intelligent surface (ARIS) assisted maritime wireless communications system, where an ARIS is employed to provide wireless services for maritime users (MUs) that are within the blocked offshore areas. Then, a channel estimation protocol is proposed for our ARIS-assisted maritime wireless communications system, where an offline channel estimation scheme is conceived for estimating the channel between the onShore base station (S-BS) and ARIS, whilst an online channel estimation scheme is designed for obtaining estimates of the ARIS-MUs channel. Based on the channel estimates obtained, an iterative algorithm is provided for obtaining the optimized reflection elements of the ARIS for maximizing the achievable rate of the proposed system. Our simulation results validate the effectiveness of the proposed ARIS-assisted maritime wireless system.
Bibliometric Analysis of Teaching and Learning in Engineering and Computing Education

In the contemporary world, the techniques and trends in the teaching-learning of engineering and computing education are undergoing constant changes, where it is necessary to update constantly, so this article describes the results of a bibliometric analysis oriented to teaching-learning of education in engineering and computing, taking all the documents indexed in the history of the core collection of the Web of Sciences database, from 1985 to June 2022, where 621 papers were analyzed, showing a growing trend in scientific production in the last 20 years inconstantly. Regarding the productive leadership, it was possible to see that the USA maintains the most significant number of documents, obtaining an effective leadership of its 132 papers, in 81.06%, followed by England and China. According to the type of collaboration, it was observed that the institutional cooperation is the one that obtained the highest production, showing that the authors tend to make most of the documents on the selected topic with other authors from the same institution. The Computer and Education journal was the one that made the most significant number of records (231), followed by the International Journal of Engineering Education (124), and through Bradford‘s law, the first journal was the only one found in the core of specialization by topic addressed; in addition, 2,031 keywords (author keywords and keywords Plus) were found in the 621 documents analyzed, which were developed by 1,675 authors.
An Early Wake-up and Access Barring Scheme for Improving the Probability of NDP in BLE Networks

IoT (Internet of Things) has become attractive and ubiquitous in recent years. BLE (Bluetooth Low Energy) is a protocol widely used in IoT. For BLE, it has low cost, low energy consumption, and is supported on most types of mobile phones. BLE Beacon which is widely applied in many situations, such as shopping malls and indoor navigation, is an application based on the BLE network. This paper is aimed at proposing a feasible scheme to enhance the performance of the BLE Beacon application. communications in BLE networks involve the neighbor discovery process (NDP) in the first phase to create a connection or exchange information with neighbors. When more people with mobile devices simultaneously send requests to the BLE Beacon, the success probability of the NDP decreases and then the latency of the NDP increases. It leads to poor customer experience. Therefore, we propose an Early Wake-Up and Access Barring (EWAB) scheme to improve the NDP performance among an advertiser and multiple scanners. The scheme makes good use of the information that can be contained in the advertising packet to adjust the scan interval and the backoff window dynamically. The results show that EWAB provides better performance in terms of the probability of successful discovery, the mean discovery latency, and power consumption in the BLE networks.
NONLINEAR TIME SERIES MODELS IN ECONOMICS

Abstract. In recent years there has been great interest in developing nonlinear extensions to the basic Autoregressive Integrated Moving Average model popularised by Box and Jenkins. Many of these have been in response to observed nonlinear behaviour in scientific areas such as electronic engineering, geology and oceanography and, as a consequence, have found little application in economics. Economic time series have features peculiar to themselves, and thus often require models to be developed in response to their own special nonlinear character. This paper therefore surveys those nonlinear time series models that have been developed in other disciplines and which have found to be useful for analysing economic time series, such as power transformations, fractional integration and deterministic chaos, and those that have been developed directly in response to nonlinear economic behaviour: for example, logistic transformations, asymmetric models, Markov models for business cycles and time deformation models. Also discussed are various tests for the presence of nonlinearity in time series and the evidence concerning the prevalence of such nonlinearity in economic time series is surveyed.
DCNN Based Human Activity Recognition Using Micro-Doppler Signatures

In recent years, Deep Convolutional Neural Networks (DCNNs) have demonstrated some promising results in classification of micro-Doppler (m-D) radar data in human activity recognition. Compared with camera-based, radar-based human activity recognition is robust to low light conditions, adverse weather conditions, long-range operations, through wall imaging etc. An indigenously developed “DIAT-J.1RADHAR” human activity recognition dataset comprising micro-Doppler signature images of six different activites like (i) person fight punching (boxing) during the one-to-one attack, (ii) person intruding for pre-attack surveillance (army marching), (iii) person training (army jogging), (iv) person shooting (or escaping) with a rifle (jumping with holding a gun), (v) stone/hand-grenade throwing for damage/blasting (stone-pelting/grenades-throwing), and (vi) person hidden translation for attack execution or escape (army crawling and compared performance of this data on various DCNN models. To reduce variations in data, we have cleaned data and make it suitable for DCNN model by using preprocessing methods such as re-scaling, rotation, width shift range, height shift range, sheer range, zoom range and horizontal flip etc. We used different DCNN pre-trained models such as VGG-16, VGG-19, and Inception V3. These models are fine-tuned and the resultant models are performing efficiently for human activity recognition in DIAT-μRadHAR human activity dataset.
Adaptive mm Wave Beam Tracking in 3D Scenes via Multi - Agent Deep Q Learning

Beamforming is indispensable for millimeter wave massive multi-input multi-output systems, meanwhile, the over-head cost of channel state information acquisition can be consid-erable especially in dynamic environments. To reduce beam training overhead, we propose an adaptive distributed beam tracking algorithm using multi-agent deep Q learning for dynamic channel sensing. By online learning of users' movement trajectories as prior, the proposed algorithm learn to scan a beam subspace to maximize the average effective achievable sum-rate, without prior knowledges of dynamic channels. Considering practical implement, we develop a simplified training scheme of deep Q network, to reduce the training complexity. Simulation results demonstrate the effectiveness and superiority of the designed method.
Affective Brain-Computer Interface using Frontal Entropy Asymmetry for Human- Machine Interaction

This paper aims to explore the frontal EEG correlates for an affective brain-computer interaction system in a social-life scenario. We first perform a multi-band decomposition on frontal EEG signals using complete ensemble empirical mode decomposition with adaptive noise. Then frontal asymmetric entropy features, including asymmetry of sample entropy and fuzzy entropy, as well as frontal transfer entropy, are proposed for emotion recognition. Those features are further selected by mutual information and ANOVA test, and classified by random forest and gradient boosting classifier. Average accuracies on the DEAP dataset are 71.09% for arousal, 77.34% for valence, on the human-machine affective interaction system are 84.51%, validating the reliability and effectiveness of the proposed features for emotion recognition.