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The 2022 7th International Conference on Intelligent Information Processing

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A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 38481

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Dr. Francisco Martínez González

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Departamento de Matemática Aplicada y Estadística, Universidad Politécnica de Cartagena, Cartagena, Spain
Interests: fractional calculus; real analysis; complex analysis; mathematical physics; numerical analysis; computational science; mathematical modeling; theoretical physics; signal processing
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Dr. Mohammed K. A. Kaabar

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Guest Editor

Chinese Institute of Electric Power, Samarkand International University of Technology, Samarkand 140100, Uzbekistan
Interests: mathematics; electrical engineering; computer engineering; antennas and wave propagation; modern electronics; data analysis; design project; sustainable development; new technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2022 7th International Conference on Intelligent Information Processing (ICIIP) was hosted by the Romanian-American University and organized by the Romanian Academy, Center for Mountain Economics; the Romanian Academy, Center for Financial and Monetary Research—Victor Slăvescu; and the International Engineering and Technology Institute. It is a leading international conference in the research area of signals and intelligent information processing. The conference’s main goal is to provide an innovative exchange platform for students, engineers, practitioners, faculties, and researchers from all over the world. Running since 2016, this conference was held for the seventh time after the success of its previous versions held in various countries worldwide, including Australia, China, Malaysia, Romania, and Thailand. ICIIP 2022 was held during September 29–30, 2022, in Bucharest, Romania. The conference Special Issue is focused on addressing a wide range of theoretical aspects and applications of signals and intelligent information processing.

Topics of interest for submission include but are not limited to:

Signals and Systems;
Digital Signal Processing;
Digital Image Processing;
Artificial Intelligence, Big Data, Bioinformatics, Cognitive Modeling, and Computational Intelligence;
Computer Hardware and Computer Vision;
Communication Technologies, Data Mining, and Deep Learning;
Multi-Agent Systems, Multimedia Signal Processing, and Natural Language Processing;
Wireless Communications.

Dr. Francisco Martínez González
Dr. Mohammed K. A. Kaabar
Guest Editors

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Published Papers (9 papers)

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Research

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18 pages, 810 KiB

Open AccessArticle

Benford’s Law and Perceptual Features for Face Image Quality Assessment

by Domonkos Varga

Signals 2023, 4(4), 859-876; https://doi.org/10.3390/signals4040047 - 5 Dec 2023

Viewed by 1584

Abstract

The rapid growth in multimedia, storage systems, and digital computers has resulted in huge repositories of multimedia content and large image datasets in recent years. For instance, biometric databases, which can be used to identify individuals based on fingerprints, facial features, or iris patterns, have gained a lot of attention both from academia and industry. Specifically, face image quality assessment (FIQA) has become a very important part of face recognition systems, since the performance of such systems strongly depends on the quality of input data, such as blur, focus, compression, pose, or illumination. The main contribution of this paper is an analysis of Benford’s law-inspired first digit distribution and perceptual features for FIQA. To be more specific, I investigate the first digit distributions in different domains, such as wavelet or singular values, as quality-aware features for FIQA. My analysis revealed that first digit distributions with perceptual features are able to reach a high performance in the task of FIQA. Full article

(This article belongs to the Special Issue The 2022 7th International Conference on Intelligent Information Processing)

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18 pages, 2589 KiB

Open AccessArticle

Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms

by Madduma Wellalage Pasan Maduranga, Valmik Tilwari and Ruvan Abeysekera

Signals 2023, 4(4), 651-668; https://doi.org/10.3390/signals4040036 - 25 Sep 2023

Cited by 7 | Viewed by 2276

Abstract

Recent developments in machine learning algorithms are playing a significant role in wireless communication and Internet of Things (IoT) systems. Location-based Internet of Things services (LBIoTS) are considered one of the primary applications among those IoT applications. The key information involved in LBIoTS is finding an object’s geographical location. The Global Positioning System (GPS) technique does not perform better in indoor environments due to multipath. Numerous methods have been investigated for indoor localization scenarios. However, the precise location estimation of a moving object in such an application is challenging due to the high signal fluctuations. Therefore, this paper presents machine learning algorithms to estimate the object’s location based on the Received Signal Strength Indicator (RSSI) values collected through Bluetooth low-energy (BLE)-based nodes. In this experiment, we utilize a publicly available RSSI dataset. The RSSI data are collected from different BLE ibeacon nodes installed in a complex indoor environment with labels. Then, the RSSI data are linearized using the weighted least-squares method and filtered using moving average filters. Moreover, machine learning algorithms are used for training and testing the dataset to estimate the precise location of the objects. All the proposed algorithms were tested and evaluated under their different hyperparameters. The tested models provided approximately 85% accuracy for KNN, 84% for SVM and 76% accuracy in FFNN. Full article

(This article belongs to the Special Issue The 2022 7th International Conference on Intelligent Information Processing)

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29 pages, 2173 KiB

Open AccessArticle

Simulation of an Indoor Visible Light Communication System Using Optisystem

by Alwin Poulose

Signals 2022, 3(4), 765-793; https://doi.org/10.3390/signals3040046 - 1 Nov 2022

Cited by 8 | Viewed by 6121

Abstract

Visible light communication (VLC ) is an emerging research area in wireless communication. The system works the same way as optical fiber-based communication systems. However, the VLC system uses free space as its transmission medium. The invention of the light-emitting diode (LED) significantly updated the technologies used in modern communication systems. In VLC, the LED acts as a transmitter and sends data in the form of light when the receiver is in the line of sight (LOS) condition. The VLC system sends data by blinking the light at high speed, which is challenging to identify by human eyes. The detector receives the flashlight at high speed and decodes the transmitted data. One significant advantage of the VLC system over other communication systems is that it is easy to implement using an LED and a photodiode or phototransistor. The system is economical, compact, inexpensive, small, low power, prevents radio interference, and eliminates the need for broadcast rights and buried cables. In this paper, we investigate the performance of an indoor VLC system using Optisystem simulation software. We simulated an indoor VLC system using LOS and non-line-of-sight (NLOS) propagation models. Our simulation analyzes the LOS propagation model by considering the direct path with a single LED as a transmitter. The NLOS propagation model-based VLC system analyses two scenarios by considering single and dual LEDs as its transmitter. The effect of incident and irradiance angles in an LOS propagation model and an eye diagram of LOS/NLOS models are investigated to identify the signal distortion. We also analyzed the impact of the field of view (FOV) of an NLOS propagation model using a single LED as a transmitter and estimated the bitrate

(R_{b})

. Our theoretical results show that the system simulated in this paper achieved bitrates in the range of

2.1208 \times 10^{7}

4.2147 \times 10^{7}

bits/s when the FOV changes from

30^{\circ}

90^{\circ}

. A VLC hardware design is further considered for real-time implementations. Our VLC hardware system achieved an average of 70% data recovery rate in the LOS propagation model and a 40% data recovery rate in the NLOS propagation model. This paper’s analysis shows that our simulated VLC results are technically beneficial in real-world VLC systems. Full article

(This article belongs to the Special Issue The 2022 7th International Conference on Intelligent Information Processing)

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15 pages, 31062 KiB

Open AccessArticle

Text Line Extraction in Historical Documents Using Mask R-CNN

by Ahmad Droby, Berat Kurar Barakat, Reem Alaasam, Boraq Madi, Irina Rabaev and Jihad El-Sana

Signals 2022, 3(3), 535-549; https://doi.org/10.3390/signals3030032 - 4 Aug 2022

Cited by 14 | Viewed by 4038

Abstract

Text line extraction is an essential preprocessing step in many handwritten document image analysis tasks. It includes detecting text lines in a document image and segmenting the regions of each detected line. Deep learning-based methods are frequently used for text line detection. However, only a limited number of methods tackle the problems of detection and segmentation together. This paper proposes a holistic method that applies Mask R-CNN for text line extraction. A Mask R-CNN model is trained to extract text lines fractions from document patches, which are further merged to form the text lines of an entire page. The presented method was evaluated on the two well-known datasets of historical documents, DIVA-HisDB and ICDAR 2015-HTR, and achieved state-of-the-art results. In addition, we introduce a new challenging dataset of Arabic historical manuscripts, VML-AHTE, where numerous diacritics are present. We show that the presented Mask R-CNN-based method can successfully segment text lines, even in such a challenging scenario. Full article

(This article belongs to the Special Issue The 2022 7th International Conference on Intelligent Information Processing)

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11 pages, 2123 KiB

Open AccessArticle

Urban Plants Classification Using Deep-Learning Methodology: A Case Study on a New Dataset

by Marina Litvak, Sarit Divekar and Irina Rabaev

Signals 2022, 3(3), 524-534; https://doi.org/10.3390/signals3030031 - 3 Aug 2022

Cited by 4 | Viewed by 4216

Abstract

Plant classification requires the eye of an expert in botanics when the subtle differences in stem or petals differentiate between different species. Hence, an accurate automatic plant classification might be of great assistance to a person who studies agriculture, travels, or explores rare species. This paper focuses on a specific task of urban plants classification. The possible practical application of this work is a tool which assists people, growing plants at home, to recognize new species and to provide the relevant caring instructions. Because urban species are barely covered by the benchmark datasets, these species cannot be accurately recognized by the state-of-the-art pre-trained classification models. This paper introduces a new dataset, Urban Planter, for plant species classification with 1500 images categorized into 15 categories. The dataset contains 15 urban species, which can be grown at home in any climate (mostly desert) and are barely covered by existing datasets. We performed an extensive analysis of this dataset, aimed at answering the following research questions: (1) Does the Urban Planter dataset provide enough information to train accurate deep learning models? (2) Can pre-trained classification models be successfully applied on Urban Planter, and is the pre-training on ImageNet beneficial in comparison to the pre-training on a much smaller but more relevant dataset? (3) Does two-step transfer learning further improve the classification accuracy? We report the results of experiments designed to answer these questions. In addition, we provide the link to the installation code of the alpha version and the demo video of the web app for urban plants classification based on the best evaluated model. To conclude, our contribution is three-fold: (1) We introduce a new dataset of urban plant images; (2) We report the results of an extensive case study with several state-of-the-art deep networks and different configurations for transfer learning; (3) We provide a web application based on the best evaluated model. In addition, we believe that, by extending our dataset in the future to eatable plants and assisting people to grow food at home, our research contributes to achieve the United Nations’ 2030 Agenda for Sustainable Development. Full article

(This article belongs to the Special Issue The 2022 7th International Conference on Intelligent Information Processing)

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14 pages, 1640 KiB

Open AccessArticle

Saliency-Guided Local Full-Reference Image Quality Assessment

by Domonkos Varga

Signals 2022, 3(3), 483-496; https://doi.org/10.3390/signals3030028 - 11 Jul 2022

Cited by 10 | Viewed by 3377

Abstract

Research and development of image quality assessment (IQA) algorithms have been in the focus of the computer vision and image processing community for decades. The intent of IQA methods is to estimate the perceptual quality of digital images correlating as high as possible with human judgements. Full-reference image quality assessment algorithms, which have full access to the distortion-free images, usually contain two phases: local image quality estimation and pooling. Previous works have utilized visual saliency in the final pooling stage. In addition to this, visual saliency was utilized as weights in the weighted averaging of local image quality scores, emphasizing image regions that are salient to human observers. In contrast to this common practice, visual saliency is applied in the computation of local image quality in this study, based on the observation that local image quality is determined both by local image degradation and visual saliency simultaneously. Experimental results on KADID-10k, TID2013, TID2008, and CSIQ have shown that the proposed method was able to improve the state-of-the-art’s performance at low computational costs. Full article

(This article belongs to the Special Issue The 2022 7th International Conference on Intelligent Information Processing)

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18 pages, 3026 KiB

Open AccessArticle

A Perspective on Information Optimality in a Neural Circuit and Other Biological Systems

by Robert Friedman

Signals 2022, 3(2), 410-427; https://doi.org/10.3390/signals3020025 - 20 Jun 2022

Cited by 6 | Viewed by 2522

Abstract

The nematode worm Caenorhabditis elegans has a relatively simple neural system for analysis of information transmission from sensory organ to muscle fiber. Consequently, this study includes an example of a neural circuit from the nematode worm, and a procedure is shown for measuring its information optimality by use of a logic gate model. This approach is useful where the assumptions are applicable for a neural circuit, and also for choosing between competing mathematical hypotheses that explain the function of a neural circuit. In this latter case, the logic gate model can estimate computational complexity and distinguish which of the mathematical models require fewer computations. In addition, the concept of information optimality is generalized to other biological systems, along with an extended discussion of its role in genetic-based pathways of organisms. Full article

(This article belongs to the Special Issue The 2022 7th International Conference on Intelligent Information Processing)

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17 pages, 7296 KiB

Open AccessArticle

COVID-19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis?

by Muhammad Saqib, Abbas Anwar, Saeed Anwar, Lars Petersson, Nabin Sharma and Michael Blumenstein

Signals 2022, 3(2), 296-312; https://doi.org/10.3390/signals3020019 - 11 May 2022

Cited by 7 | Viewed by 2670

Abstract

Deep learning in the last decade has been very successful in computer vision and machine learning applications. Deep learning networks provide state-of-the-art performance in almost all of the applications where they have been employed. In this review, we aim to summarize the essential deep learning techniques and then apply them to COVID-19, a highly contagious viral infection that wreaks havoc on everyone’s lives in various ways. According to the World Health Organization and scientists, more testing potentially helps contain the virus’s spread. The use of chest radiographs is one of the early screening tests for determining disease, as the infection affects the lungs severely. To detect the COVID-19 infection, this experimental survey investigates and automates the process of testing by employing state-of-the-art deep learning classifiers. Moreover, the viruses are of many types, such as influenza, hepatitis, and COVID. Here, our focus is on COVID-19. Therefore, we employ binary classification, where one class is COVID-19 while the other viral infection types are treated as non-COVID-19 in the radiographs. The classification task is challenging due to the limited number of scans available for COVID-19 and the minute variations in the viral infections. We aim to employ current state-of-the-art CNN architectures, compare their results, and determine whether deep learning algorithms can handle the crisis appropriately and accurately. We train and evaluate 34 models. We also provide the limitations and future direction. Full article

(This article belongs to the Special Issue The 2022 7th International Conference on Intelligent Information Processing)

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Review

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37 pages, 4200 KiB

Open AccessReview

A Survey on MIMO-OFDM Systems: Review of Recent Trends

by Houda Harkat, Paulo Monteiro, Atilio Gameiro, Fernando Guiomar and Hasmath Farhana Thariq Ahmed

Signals 2022, 3(2), 359-395; https://doi.org/10.3390/signals3020023 - 2 Jun 2022

Cited by 29 | Viewed by 9222

Abstract

MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified. Full article

(This article belongs to the Special Issue The 2022 7th International Conference on Intelligent Information Processing)

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