Search results for: myoelectric signal processing

Authors: George Oliveira, Luana D. Fronza, Luiza Medeiros, Patricia D. M. Plentz

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The research presented in this article is part of a wide project that proposes a scheduling system for multi-robots in intelligent warehouses employing multi-robot path-planning (MPP) and multi-robot task allocation (MRTA) to reconcile multiple restrictions (task delivery time, task priorities, charging capacity, and robots battery capacity). We present the software component capable of interconnecting an open streaming processing architecture and robot operating system (ROS), ensuring communication and message exchange between robots and the environment in which they are inserted. Simulation results show the good performance of our proposed technique for connecting ROS and streaming platforms.

Keywords: complex distributed systems, mobile robots, smart warehouses, streaming platforms

Procedia PDF Downloads 181

Authors: Aysun Sezer

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Magnetic Resonance Imaging (MRI) is one of the advanced diagnostic tools for evaluating shoulder pathologies. Proton Density (PD)-weighted MRI sequences prove highly effective in detecting edema. However, they are deficient in the anatomical identification of bones due to a trauma-induced decrease in signal-to-noise ratio and blur in the traumatized cortices. Computer-based diagnostic systems require precise segmentation, identification, and localization of anatomical regions in medical imagery. Deep learning-based object detection algorithms exhibit remarkable proficiency in real-time object identification and localization. In this study, the YOLOv8 model was employed to detect humeral head and scapular regions in 665 axial PD-weighted MR images. The YOLOv8 configuration achieved an overall success rate of 99.60% and 89.90% for detecting the humeral head and scapula, respectively, with an intersection over union (IoU) of 0.5. Our findings indicate a significant promise of employing YOLOv8-based detection for the humerus and scapula regions, particularly in the context of PD-weighted images affected by both noise and intensity inhomogeneity.

Keywords: YOLOv8, object detection, humerus, scapula, IRM

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Authors: Zeineb Ben Azzouz, Wahiba Ben Abdessalem Karaa

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The automated extraction of UML class diagram from natural language requirements is a highly challenging task. Many approaches, frameworks and tools have been presented in this field. Nonetheless, the experiments of these tools have shown that there is no approach that can work best all the time. In this context, we propose a new accurate approach to facilitate the automatic mapping from textual requirements to UML class diagram. Our new approach integrates the best properties of statistical Natural Language Processing (NLP) techniques to reduce ambiguity when analysing natural language requirements text. In addition, our approach follows the best practices defined by conceptual modelling experts to determine some patterns indispensable for the extraction of basic elements and concepts of the class diagram. Once the relevant information of class diagram is captured, a XMI document is generated and imported with a CASE tool to build the corresponding UML class diagram.

Keywords: class diagram, user’s requirements, XMI, software engineering

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Authors: Milad Kaboli

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A new CMOS low voltage current-mode four-quadrant analog multiplier based on the squarer circuit with voltage output is presented. The proposed circuit is composed of a pair of current subtractors, a pair differential-input V-I converters and a pair of voltage squarers. The circuit was simulated using HSPICE simulator in standard 0.18 μm CMOS level 49 MOSIS (BSIM3 V3.2 SPICE-based). Simulation results show the performance of the proposed circuit and experimental results are given to confirm the operation. This topology of multiplier results in a high-frequency capability with low power consumption. The multiplier operates for a power supply ±1.2V. The simulation results of analog multiplier demonstrate a THD of 0.65% in 10MHz, a −3dB bandwidth of 1.39GHz, and a maximum power consumption of 7.1mW.

Keywords: analog processing circuit, WTA, LTA, low voltage

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Authors: Cinthia Campoverde, Mateo Benavidez, Victor Arias, Milton Torres

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This article evaluates and compares three propagation models: the Okumura-Hata model, the Ericsson 9999 model, and the SUI model. The inclusion of vegetation attenuation in the area is also taken into account. These mathematical models aim to predict the power loss between a transmitting antenna (Tx) and a receiving antenna (Rx). The study was conducted in the open areas of the Livestock Department at the Escuela Superior Politécnica de Chimborazo (ESPOCH) University, located in the city of Riobamba, Ecuador. The necessary parameters for each model were calculated, considering LTE technology. The transmitting antenna belongs to the mobile phone company ”TUENTI” in Band 2, operating at a frequency of 1940 MHz. The reception power data in the area were empirically measured using the ”Network Cell Info” application. A total of 170 samples were collected, distributed across 19 radius, forming concentric circles around the transmitting antenna. The results demonstrate that the Okumura Hata urban model provides the best fit to the measured data.

Keywords: propagation models, reception power, LTE, power losses, correction factor

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Authors: Kittipong Tissayakorn, Fumio Akagi, Yu Song

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Logistics is the integrated management of all of the activities required to move products through the supply chain. For a typical product, this supply chain extends from a raw material source through the production and distribution system to the point of consumption and the associated reverse logistics. The logistical activities are comprised of freight transport, storage, inventory management, materials handling and all related information processing. This paper analyzes the green management system of logistics enterprise for Thailand and advances the concept of Green Logistics, which should be held by the public. In addition, it proposes that the government should strengthen its supervision and support for green logistics, and companies should construct self-disciplined green logistics management systems and corresponding processes, a reverse logistics management system and a modern green logistics information collection and management system.

Keywords: logistics, green logistics, management system, ecological economics

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Authors: Martin Pellon Consunji

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Due to the rise in popularity of Bitcoin and other crypto assets as a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools, such as bots, in order to gain an advantage over the market. Traditionally, trading in the stock market was done by professionals with years of training who understood patterns and exploited market opportunities in order to gain a profit. However, nowadays a larger portion of market participants are at minimum aided by market-data processing bots, which can generally generate more stable signals than the average human trader. The rise in trading bot usage can be accredited to the inherent advantages that bots have over humans in terms of processing large amounts of data, lack of emotions of fear or greed, and predicting market prices using past data and artificial intelligence, hence a growing number of approaches have been brought forward to tackle this task. However, the general limitation of these approaches can still be broken down to the fact that limited historical data doesn’t always determine the future, and that a lot of market participants are still human emotion-driven traders. Moreover, developing markets such as those of the cryptocurrency space have even less historical data to interpret than most other well-established markets. Due to this, some human traders have gone back to the tried-and-tested traditional technical analysis tools for exploiting market patterns and simplifying the broader spectrum of data that is involved in making market predictions. This paper proposes a method which uses neuro evolution techniques on both sentimental data and, the more traditionally human-consumed, technical analysis data in order to gain a more accurate forecast of future market behavior and account for the way both automated bots and human traders affect the market prices of Bitcoin and other cryptocurrencies. This study’s approach uses evolutionary algorithms to automatically develop increasingly improved populations of bots which, by using the latest inflows of market analysis and sentimental data, evolve to efficiently predict future market price movements. The effectiveness of the approach is validated by testing the system in a simulated historical trading scenario, a real Bitcoin market live trading scenario, and testing its robustness in other cryptocurrency and stock market scenarios. Experimental results during a 30-day period show that this method outperformed the buy and hold strategy by over 260% in terms of net profits, even when taking into consideration standard trading fees.

Keywords: neuro-evolution, Bitcoin, trading bots, artificial neural networks, technical analysis, evolutionary algorithms

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Authors: Tokihiko Akita, Seiichi Mita

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A millimeter-wave radar is the most robust against adverse environments, making it an essential environment recognition sensor for automated driving. However, the reflection signal is sparse and unstable, so it is difficult to obtain the high recognition accuracy. Deep learning provides high accuracy even for them in recognition, but requires large scale datasets with ground truth. Specially, it takes a lot of cost to annotate for a millimeter-wave radar. For the solution, utilizing a simulator that can generate an annotated huge dataset is effective. Simulation of the radar is more difficult to match with real world data than camera image, and recognition by deep learning with higher-order features using the simulator causes further deviation. We have challenged to improve the accuracy of traffic participant classification by fusing simulator and real-world data with domain adaptation technique. Experimental results with the domain adaptation network created by us show that classification accuracy can be improved even with a few real-world data.

Keywords: millimeter-wave radar, object classification, deep learning, simulation, domain adaptation

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Authors: R. Conde

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The Brazilian economic projections and social indicators show a future of crisis for the country. Solutions to avoid this crisis scenario are necessary. Several developed countries implement initiatives linked to sustainability, mainly related to the circular economy, to solve their crises quickly - green recovery. This article aims to assess social gains if Brazil followed the same recovery strategy. Furthermore, with the use of data presented and recognized in the international academic society, the number of jobs that can be created, if Brazil took the first steps towards a more circular economy, was found. Moreover, in addition to the gross value in the number of jobs created, this article also detailed the number of these jobs by type of activity (collection, processing, and manufacturing) and by type of material.

Keywords: circular economy, green recovery, job creation, social gains

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Authors: Irmak Gunes Yuceil

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This paper outlines interferences and analytical errors which are encountered in the qualification and quantification of archaeological and ethnographic artifacts, by means of handheld x-ray fluorescence. These shortcomings were evaluated through case studies carried out on metallic artifacts related to various periods and cultures around Anatolia. An Innov-X Delta Standard 2000 handheld x-ray fluorescence spectrometer was used to collect data from 1361 artifacts, through 6789 measurements and 70 hours’ tube usage, in between 2013-2017. Spectrum processing was done by Delta Advanced PC Software. Qualitative and quantitative results screened by the device were compared with the spectrum graphs, and major discrepancies associated with physical and analytical interferences were clarified in this paper.

Keywords: hand-held x-ray fluorescence spectroscopy, art and archaeology, interferences and analytical errors, pre-diagnosis in conservation

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Authors: Merry Meryam Martgrita, Marselina Irasonia Tan

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One of several aggresive cancer is cancer that overexpress c-erbB2 receptor along with the expression of estrogen receptor. Components of extracellular matrix play an important role to increase cancer cells proliferation, migration and invasion. Both components can affect cancer development by regulating the signal transduction pathways in cancer cells. In recent research, SKOV-3 ovarian cancer cell line, that overexpress c-erbB2 receptor was cultured on type IV collagen and treated with estradiol-17β, to reveal the role of both components on RNA and protein level of c-erbB2 receptor. In this research we found a modulation phenomena of increasing and decreasing of c-erbB2 RNA level and a stabilisation phenomena of c-erbB2 protein expression due to estradiol-17β and type IV collagen. It seemed that estradiol-17β has an important role to increase c-erbB2 transcription and the stability of c-erbB2 protein expression. Type IV collagen has an opposite role. It blocked c-erbB2 transcription when it bound to integrin receptor in SKOV-3 cells.

Keywords: c-erbB2, estradiol-17β, SKOV-3, type IV collagen

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Authors: Mahsa Moteshakeri

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Morinda citrifolia L., a miracle fruit with common name of Noni, has been widely used as food and traditional medicine in the Polynesians culture. Current scientific evidences have proved the therapeautical and nutritional properties of this fruit so that its extensive production in tropical regions in recent years has emerged a competitive global Noni market mainly as a dietary supplement in the form of juice or tablet. However, there is not much record on the processing method applied on fresh fruit postharvest or even its mechanism of action in controlling diseases. This review aimed to provide a comprehensive data on phytochemicals, technical, and nutritional advances on Noni fruit and recent patents published, as well as medicinal properties of the fruit in order to benefit future investigations on this precious fruit either in industrial or therapeautical section.

Keywords: noni fruit, phytochemicals, therapeautic properties of fruit, nutritional properties of fruit

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Authors: Logaiswari Indiran, Zainab Khalifah, Kamariah Ismail

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Business incubators have now been recognized as effective tools in providing business assistance to start-up firms. In both developed and developing countries, the number of incubators is growing tremendously. As the birth rate of incubators increases, so do its challenges. Malaysia, as one of the developing countries in the Asian continent, has also established a number of business incubators to breed and foster the growth and survival of start-up firms. Thus, this study discusses the incubation model applied in Malaysia and the challenges faced by these incubators using secondary data including policies, previous literature, and reports related to Malaysian incubators. The findings of this study call the government to rethink the key role of incubator managers and staffs, internal structure of the incubator concept and process, intellectual properties management, strategic alliances with universities-industries and funding supports in enhancing the support provided by the business incubators in Malaysia. The key challenges highlighted in this study signal important policy lessons for other developing countries that aim to create and map an effective business incubator ecosystem.

Keywords: business incubators, incubation challenges, funding support, incubator managers, internal structure, start-up firms

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Authors: M. C. Padma, R. J. Prathibha

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Parts of speech tagging is the process of assigning appropriate parts of speech tags to the words in a given text. The critical or crucial information needed for tagging a word come from its internal structure rather from its neighboring words. The internal structure of a word comprises of its morphological features and grammatical information. This paper presents a morpheme based parts of speech tagger for Kannada language. This proposed work uses hierarchical tag set for assigning tags. The system is tested on some Kannada words taken from EMILLE corpus. Experimental result shows that the performance of the proposed system is above 90%.

Keywords: hierarchical tag set, morphological analyzer, natural language processing, paradigms, parts of speech

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Authors: Patikineti Sreenivasulu, K. srinivasa Rao, A. Vinaya Babu

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The demand for portability, performance and high functional integration density of digital devices leads to the scaling of complementary metal oxide semiconductor (CMOS) devices inevitable. The increase in power consumption, coupled with the increasing demand for portable/hand-held electronics, has made power consumption a dominant concern in the design of VLSI circuits today. MTCMOS technology provides low leakage and high performance operation by utilizing high speed, low Vt (LVT) transistors for logic cells and low leakage, high Vt (HVT) devices as sleep transistors. Sleep transistors disconnect logic cells from the supply and/or ground to reduce the leakage in the sleep mode. In this technology, energy consumption while doing the mode transition and minimum time required to turn ON the circuit upon receiving the wake up signal are issues to be considered because these can adversely impact the performance of VLSI circuit. In this paper we are introducing an enhancing method of MTCMOS technology to optimize the power in MTCMOS sequential circuits.

Keywords: power consumption, ultra-low power, leakage, sub threshold, MTCMOS

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Authors: Vida Jodaeian, Behzad Sani

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Copper nanoparticles were successfully synthesized and characterized on green-extracted Carrageenan from seaweed by precipitation method without using any supporter and template with precipitation method. The crystallinity, optical properties, morphology, and composition of products were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), and Fourier transforms infrared (FT-IR) spectroscopy. The effects of processing parameters on the size and shape of Cu- nanostructures such as effect of pH were investigated. It is found that the reaction at lower pH values (acidic) could not be completed and pH = 8.00 was the best pH value to prepare very fine nanoparticles. They as synthesized Cu-nanoparticles were used as catalysts for the reduction of aromatic nitro compounds in presence of NaBH4. The results showed that Cu-nanoparticles are very active for reduction of these nitro aromatic compounds.

Keywords: nanoparticles, carrageenan, seaweed, nitro aromatic compound

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Authors: Cao Bin, Yuan Yi, Wang Qiang, Amor Abdelkader, Ali Reza Kamali, Diogo Montalvão

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The study of the electrodynamic behavior of non-ferrous particles in time-varying magnetic fields is a promising area of research with wide applications, including recycling of non-ferrous metals, mechanical transmission, and space debris. The key technology for recovering non-ferrous metals is eddy current separation (ECS), which utilizes the eddy current force and torque to separate non-ferrous metals. ECS has several advantages, such as low energy consumption, large processing capacity, and no secondary pollution, making it suitable for processing various mixtures like electronic scrap, auto shredder residue, aluminum scrap, and incineration bottom ash. Improving the separation efficiency of mixtures with different particle sizes in ECS can create significant social and economic benefits. Our previous study investigated the influence of particle size on separation efficiency by combining numerical simulations and separation experiments. Pearson correlation analysis found a strong correlation between the eddy current force in simulations and the repulsion distance in experiments, which confirmed the effectiveness of our simulation model. The interaction effects between particle size and material type, rotational speed, and magnetic pole arrangement were examined. It offer valuable insights for the design and optimization of eddy current separators. The underlying mechanism behind the effect of particle size on separation efficiency was discovered by analyzing eddy current and field gradient. The results showed that the magnitude and distribution heterogeneity of eddy current and magnetic field gradient increased with particle size in eddy current separation. Based on this, we further found that increasing the curvature of magnetic field lines within particles could also increase the eddy current force, providing a optimized method to improving the separation efficiency of fine particles. By combining the results of the studies, a more systematic and comprehensive set of optimization guidelines can be proposed for mixtures with different particle size ranges. The separation efficiency of fine particles could be improved by increasing the rotational speed, curvature of magnetic field lines, and electrical conductivity/density of materials, as well as utilizing the eddy current torque. When designing an ECS, the particle size range of the target mixture should be investigated in advance, and the suitable parameters for separating the mixture can be fixed accordingly. In summary, these results can guide the design and optimization of ECS, and also expand the application areas for ECS.

Keywords: eddy current separation, particle size, numerical simulation, metal recovery

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Authors: Paul Hernandez-Herrera, Fernando Montoya, Juan Manuel Rendon, Alberto Darszon, Gabriel Corkidi

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Intracellular calcium ([Ca2+]i) regulates sperm motility. The analysis of [Ca2+]i has been traditionally achieved in two dimensions while the real movement of the cell takes place in three spatial dimensions. Due to optical limitations (high speed cell movement and low light emission) important data concerning the three dimensional movement of these flagellated cells had been neglected. Visualizing [Ca2+]i in 3D is not a simple matter since it requires complex fluorescence microscopy techniques where the resulting images have very low intensity and consequently low SNR (Signal to Noise Ratio). In 4D sequences, this problem is magnified since the flagellum oscillates (for human sperm) at least at an average frequency of 15 Hz. In this paper, a novel approach to extract the flagellum’s center-line in 4D stacks is presented. For this purpose, an iterative algorithm based on the fast-marching method is proposed to extract the flagellum’s center-line. Quantitative and qualitative results are presented in a 4D stack to demonstrate the ability of the proposed algorithm to trace the flagellum’s center-line. The method reached a precision and recall of 0.96 as compared with a semi-manual method.

Keywords: flagellum, minimal path, segmentation, sperm

Procedia PDF Downloads 277

Authors: Keyi Wang

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Similar to the touchscreen, hand gesture based human-computer interaction (HCI) is a technology that could allow people to perform a variety of tasks faster and more conveniently. This paper proposes a training method of an image-based hand gesture image and video clip recognition system using a CNN (Convolutional Neural Network) with a dataset. A dataset containing 6 hand gesture images is used to train a 2D CNN model. ~98% accuracy is achieved. Furthermore, a 3D CNN model is trained on a dataset containing 4 hand gesture video clips resulting in ~83% accuracy. It is demonstrated that a Cozmo robot loaded with pre-trained models is able to recognize static and dynamic hand gestures.

Keywords: deep learning, hand gesture recognition, computer vision, image processing

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Authors: Ali Allahverdi, Harun Aydilek, Asiye Aydilek

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Minimizing the number of tardy jobs is an important factor to consider while making scheduling decisions. This is because on-time shipments are vital for lowering cost and increasing customers’ satisfaction. This paper addresses the single machine scheduling problem with the objective of minimizing the number of tardy jobs. The only known information is the lower and upper bounds for processing times, and deterministic job due dates. A dominance relation is established, and an algorithm is proposed. Several heuristics are generated from the proposed algorithm. Computational analysis indicates that the performance of one of the heuristics is very close to the optimal solution, i.e., on average, less than 1.5 % from the optimal solution.

Keywords: single machine scheduling, number of tardy jobs, heuristi, lower and upper bounds

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Authors: M. Khairudin

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This paper investigates the system identification and design quantitative feedback theory (QFT) for the robust control of a lathe spindle. The dynamic of the lathe spindle is uncertain and time variation due to the deepness variation on cutting process. System identification was used to obtain the dynamics model of the lathe spindle. In this work, real time system identification is used to construct a linear model of the system from the nonlinear system. These linear models and its uncertainty bound can then be used for controller synthesis. The real time nonlinear system identification process to obtain a set of linear models of the lathe spindle that represents the operating ranges of the dynamic system. With a selected input signal, the data of output and response is acquired and nonlinear system identification is performed using Matlab to obtain a linear model of the system. Practical design steps are presented in which the QFT-based conditions are formulated to obtain a compensator and pre-filter to control the lathe spindle. The performances of the proposed controller are evaluated in terms of velocity responses of the the lathe machine spindle in corporating deepness on cutting process.

Keywords: lathe spindle, QFT, robust control, system identification

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Authors: Katsuyuki Umezawa, Makoto Nakazawa, Manabu Kobayashi, Yutaka Ishii, Michiko Nakano, Shigeichi Hirasawa

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This research aims to develop a self-study system equipped with an artificial teacher who gives advice to students by detecting the learners and to evaluate language learning in a unified framework. 'Detecting the learners' means that the system understands the learners' learning conditions, such as each learner’s degree of understanding, the difference in each learner’s thinking process, the degree of concentration or boredom in learning, and problem solving for each learner, which can be interpreted from learning behavior. In this paper, we propose a system to efficiently collect brain waves from learners by focusing on only the brain waves among the biological information for 'detecting the learners'. The conventional Electroencephalograph (EEG) measurement method during learning using a simple EEG has the following disadvantages. (1) The start and end of EEG measurement must be done manually by the experiment participant or staff. (2) Even when the EEG signal is weak, it may not be noticed, and the data may not be obtained. (3) Since the acquired EEG data is stored in each PC, there is a possibility that the time of data acquisition will be different in each PC. This time, we developed a system to collect brain wave data on the server side. This system overcame the above disadvantages.

Keywords: artificial teacher, e-learning, self-study system, simple EEG

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Authors: Abdullah Khuwaileh

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While teaching and translating vocabulary is no longer a neglected area in ELT in general and in translation in particular, the psychology of its acquisition has been a neglected area. Our paper aims at exploring some of the learning and translating conditions under which vocabulary is acquired and translated properly. To achieve this objective, two teaching methods (experiments) were applied on 4 translators to measure their acquisition of a number of transparent vocabulary items. Some of these items were knowingly chosen from 'deceptively transparent words'. All the data, sample, etc., were taken from Jordan University of Science and Technology (JUST) and Yarmouk University, where the researcher is employed. The study showed that translators might translate transparent words inaccurately, particularly if these words are uncontextualised. It was also shown that the morphological structures of words may lead translators or even EFL learners to misinterpretations of meaning.

Keywords: english, transparent, word, processing, translation

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Authors: I. Grigoraviciute-Puroniene, K. Tsuru, E. Garskaite, Z. Stankeviciute, A. Beganskiene, K. Ishikawa, A. Kareiva

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Tricalcium phosphate (β-Ca₃(PO₄)₂, β-TCP) powders were synthesized using wet polymeric precipitation method for the first time to our best knowledge. The results of X-ray diffraction analysis showed the formation of almost single a Ca-deficient hydroxyapatite (CDHA) phase of a poor crystallinity already at room temperature. With continuously increasing the calcination temperature up to 800 °C, the crystalline β-TCP was obtained as the main phase. It was demonstrated that infrared spectroscopy is very effective method to characterize the formation of β-TCP. The SEM results showed that β-TCP solids were homogeneous having a small particle size distribution. The β-TCP powders consisted of spherical particles varying in size from 100 to 300 nm. Fabricated β-TCP specimens were placed to the bones of the rats and maintained for 1-2 months.

Keywords: Tricalcium phosphate (β-Ca3(PO4)2, bone regeneration, wet chemical processing, polymeric precipitation

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Gaurav Shinde, Sai Charan Gongiguntla, Prajwal Shirur, Ahmed Hambaba

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Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline that ensures that data mirrors real-world settings by incorporating Gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification, and explainability in a single pipeline called DeClEx.

Keywords: machine learning, healthcare, classification, explainability

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Authors: G. C. Tikkiwal, Mukesh Upadhyay

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During the surveillance operations at war or peace time, the Radar operator gets a scatter of targets over the screen. This may be a tracked vehicle like tank vis-à-vis T72, BMP etc, or it may be a wheeled vehicle like ALS, TATRA, 2.5Tonne, Shaktiman or moving army, moving convoys etc. The Radar operator selects one of the promising targets into Single Target Tracking (STT) mode. Once the target is locked, the operator gets a typical audible signal into his headphones. With reference to the gained experience and training over the time, the operator then identifies the random target. But this process is cumbersome and is solely dependent on the skills of the operator, thus may lead to misclassification of the object. In this paper we present a technique using mathematical and statistical methods like Fast Fourier Transformation (FFT) and Principal Component Analysis (PCA) to identify the random objects. The process of classification is based on transforming the audible signature of target into music octave-notes. The whole methodology is then automated by developing suitable software. This automation increases the efficiency of identification of the random target by reducing the chances of misclassification. This whole study is based on live data.

Keywords: radar target, fft, principal component analysis, eigenvector, octave-notes, dsp

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Authors: Nagaratna Rajur, J. D. Mallapur, Y. B. Kirankumar

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The security in mobile communication is very different from the internet or telecommunication, because of its poor user interface and limited processing capacity, as well as combination of complex network protocols. Hence, it poses a challenge for less memory usage and low computation speed based security system. Security involves all the activities that are undertaken to protect the value and on-going usability of assets and the integrity and continuity of operations. An effective network security strategies requires identifying threats and then choosing the most effective set of tools to combat them. Cryptography is a simple and efficient way to provide security in communication. RSA is an asymmetric key approach that is highly reliable and widely used in internet communication. However, it has not been efficiently implemented in mobile communication due its computational complexity and large memory utilization. The proposed algorithm modifies the current RSA to be useful in mobile communication by reducing its computational complexity and memory utilization.

Keywords: M-RSA, sensor networks, sensor applications, security

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Authors: Zineb Nougrara

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In this paper, we propose a novel methodology for extracting a road network and its nodes from satellite images of Algeria country. This developed technique is a progress of our previous research works. It is founded on the information theory and the mathematical morphology; the information theory and the mathematical morphology are combined together to extract and link the road segments to form a road network and its nodes. We, therefore, have to define objects as sets of pixels and to study the shape of these objects and the relations that exist between them. In this approach, geometric and radiometric features of roads are integrated by a cost function and a set of selected points of a crossing road. Its performances were tested on satellite images of Algeria country.

Keywords: satellite image, road network, nodes, image analysis and processing

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Authors: Biji Jacob

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Vector quantization algorithm which uses minimum distance calculation for codebook generation, a time consuming calculation performed on each pixel values leads to computation complexity. The codebook is updated by comparing the distance of each vector to their centroid vector and measure for their closeness. In this paper vector quantization is modified based on vector difference algorithm for image enhancement purpose. In the proposed scheme, vector differences between the vectors are considered as the new generation vectors or new codebook vectors. The codebook is updated by comparing the new generation vector with a threshold value having minimum error with the parent vector. The minimum error decides the fitness of each newly generated vector. Thus the codebook is generated in an adaptive manner and the fitness value is determined for the suppression of the degraded portion of the image and thereby leads to the enhancement of the image through the adaptive searching capability of the vector quantization through vector difference algorithm. Experimental results shows that the vector difference scheme efficiently modifies the vector quantization algorithm for enhancing the image with peak signal to noise ratio (PSNR), mean square error (MSE), Euclidean distance (E_dist) as the performance parameters.

Keywords: codebook, image enhancement, vector difference, vector quantization

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