Development of a Low-Cost Wireless Bee-Hive Temperature and Sound Monitoring System

Precision beekeeping requires data acquisition, data analysis, and applications where the initial phase data on the beehive plays a fundamental role. This method of apiculture could be used to measure different bee colony parameters in real time, leveraging on wireless sensing technologies, which aid monitoring of a bee colony, and enhances the monitoring of infectious diseases like colony collapse disorder–a major loss in the management of honey bee population. In this paper, a low-cost wireless technology-based system, which measures in real-time, the temperature in and around the beehive, and the sound intensity inside the hive is presented. This monitoring system is developed using an Arduino microprocessor, an ESP8266 communication module, and a web-based server. The proposed system provides valuable information concerning the bee colony behavior in terms of temperature variations and sound characteristics. Based on the measured temperature and sound information, colony beekeepers could easily detect events like increased food usage by the bees, breeding start time, pre-swarming action, actual swarming pattern, and the bee colony's death

A STUDY ON LOW-COMPLEXITY TRANSMIT ANTENNA SELECTION FOR GENERALIZED SPATIAL MODULATION

ABSTRACT: Generalized spatial modulation (GSM) maps its information to the index of the transmit antenna combination, making simultaneous transmission of multiple symbol possible. However, SM outperform GSM scheme in terms of error performance of the same data rate, due to average power effect. Transmit and receive diversity or the combination of both allow huge improvement in mimo systems in  terms of error performance. In this paper, we investigate a near optimal low-complexity Euclidean distance antenna selection (LC-EDAS) technique in GSM system, to further improve the performance of the conventional GSM system. The LC-EDAS technique independently search across signal and spatial dimension to eliminate the worse channel prior to transmission. Secondly, we investigate a sub-optimal low-complexity transmit antenna selection (LCTAS) in the GSM system to further reduce the computational complexity (CC) imposed by LC-EDAS. The Monte Carlo simulation results obtained reveals a trade-off between the GSM scheme with LC-EDAS and GSM scheme with sub-optimal transmit antenna selection in terms of error performance and CC. ABSTRAK: Modulasi Spatial Keseluruhan (GSM) menghubung informasi kepada indeks kombinasi antena yang dipancarkan, membuatkan pemancaran keseluruhan simbol dapat dilakukan. Walau bagaimanapun, SM lebih bagus daripada skim GSM pada prestasi kesilapan pada kadar data yang sama, kerana kesan purata kuasa. Kepelbagaian penghantaran dan penerimaan ataupun kombinasi keduanya memberi pembaharuan yang lebih besar dalam sistem mimo pada prestasi kesalahan. Penyelidikan ini akan mengkaji optima terdekat Euclidean kurang rumit, melalui teknik (LC-EDAS) pilihan jarak antenna dalam sistem GSM, bagi menambah prestasi sistem GSM sedia ada. Teknik LC-EDAS secara sendiri mencari signal dan dimensi separa bagi mengurangkan saluran lebih teruk semasa penghantaran. Kedua, kami mengkaji sub-optima proses pemilihan kurang rumit penyebaran antena (LCTAS) dalam sistem GSM bagi mengurangkan kerumitan pengiraan (CC) yang dikenakan oleh LC-EDAS. Keputusan simulasi Monte Carlo yang diperoleh menunjukkan timbangan antara skim GSM dan LC-EDAS dan skim GSM bersama sub-optima proses pemilihan penyebaran antena berdasarkan kesilapan prestasi dan CC

Experimental Determination of Penetration Loss into Multi-Storey Buildings at 900 and 1800MHz

This study presents building pentration loss into and around multi-storey buildings at 900 and 1800MHz based on experimental data obtained through drive test, using Test Mobile System (TEMS) investigation tools. The received signal level was measured inside and outside three buildings; the Senate building of the University of Lagos (B1), Mike Adenuga Towers (B2) and the Sapetro Towers (B3) located in Victoria Island, Lagos Nigeria. The building penetration loss (BPL) was derived from measurements, and the average and standard deviations of the BPL were computed. Results showed that the average BPL of 17.0dB and 13.8dB obtained from building B1 at 900 and 1800MHz, respectively, are comparatively higher than those of buildings B2 and B3. The standard deviation of the BPL shows an increase from 5.2dB at 900MHz to 7.8dB at 1800MHz for building B1, whereas it fell drastically from 8.65dB at 900MHz to 1.40dB at 1800MHz for B2, and a similar behaviour in B1 is seen for building B3 where it rises sharply from 1.55dB at 900MHz to 6.55dB at 1800MHz. This is in agreement with the general trend of increasing penetration loss with increase in frequency except for building B2 where an anomaly is observed. In order to examine the correlation between the measured and the predicted BPL, cubic regression was used to fit a third order polynomial to the measured BPL. Overrall, the fitted models could find useful applications in the design of novel and robust BPL models for modern multi-floored buildings

CGST: Provably Secure Lightweight Certificateless Group Signcryption Technique Based on Fractional Chaotic Maps

In recent years, there has been a lot of research interest in analyzing chaotic constructions and their associated cryptographic structures. Compared with the essential combination of encryption and signature, the signcryption scheme has a more realistic solution for achieving message confidentiality and authentication simultaneously. However, the security of a signcryption scheme is questionable when deployed in modern safety-critical systems, especially as billions of sensitive user information is transmitted over open communication channels. In order to address this problem, a lightweight, provably secure certificateless technique that uses Fractional Chaotic Maps (FCM) for group-oriented signcryption (CGST) is proposed. The main feature of the CGST-FCM technique is that any group signcrypter may encrypt data/information with the group manager (GM) and have it sent to the verifier seamlessly. This implies the legitimacy of the signcrypted information/data is verifiable using the public conditions of the group, but they cannot link it to the conforming signcrypter. In this scenario, valid signcrypted information/data cannot be produced by the GM or any signcrypter in that category alone. However, the GM is allowed to reveal the identity of the signcrypter when there is a legal conflict to restrict repudiation of the signature. Generally, the CGST-FCM technique is protected from the indistinguishably chosen ciphertext attack (IND-CCA). Additionally, the computationally difficult Diffie-Hellman (DH) problems have been used to build unlinkability, untraceability, unforgeability, and robustness of the projected CGST-FCM scheme. Finally, the security investigation of the presented CGST-FCM technique shows appreciable consistency and high efficiency when applied in real-time security applications

HO-SsNF: heap optimizer-based self-systematized neural fuzzy approach for cervical cancer classification using pap smear images

Cervical cancer is a significant concern for women, necessitating early detection and precise treatment. Conventional cytological methods often fall short in early diagnosis. The proposed innovative Heap Optimizer-based Self-Systematized Neural Fuzzy (HO-SsNF) method offers a viable solution. It utilizes HO-based segmentation, extracting features via Gray-Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). The proposed SsNF-based classifier achieves an impressive 99.6% accuracy in classifying cervical cancer cells, using the Herlev Pap Smear database. Comparative analyses underscore its superiority, establishing it as a valuable tool for precise cervical cancer detection. This algorithm has been seamlessly integrated into cervical cancer diagnosis centers, accessible through smartphone applications, with minimal resource demands. The resulting insights provide a foundation for advancing cancer prevention methods

Development of a Low-Latency Wireless Telemetry System for Monitoring Patients Heart Rates

Telemetry systems provide a means to monitor the physical state of a machine, human beings, and environmental conditions. The need for a telemetry system arises due to the increasing desire to simultaneously monitor, measure and record critical data of patients located at different locations at optimal cost. Toward this end, a wireless telemetry system was designed and implemented, specially for small-scale medical applications, specifically, for proper monitoring of the heart-rates of mostly elderly patients. In order to enhance the capabilities of the system, an infrared-based pulse Oximeter sensor was designed and integrated into the telemetry system, and this sends the acquired data to an Arduino microcontroller for pre-processing. The controller sends the pre-processed data to a coordinator PC over a ZigBee mesh wireless network, via a graphical user interface (GUI). For seamless transfer of data from source to destination, a source routing algorithm was applied to route the desired data over the proposed ZigBee network. Thus, an optimized source routing algorithm was evolved, and applied to the telemetry system in order to lower the latency of data transmission of the nodes over the wireless network. Finally, the performance of the optimized routing algorithm was compared with the existing source routing algorithm, and the wired telemetry system. Results show that data routing through the wired telemetry has much lower latency among the investigated routing schemes, and the optimized source routing algorithm transmits data with substantial gains in throughput, and achieved average latency about half the latency of the source routing algorithm

Optimized deep knowledge-based no-reference image quality index for denoised MRI images

The quality of the Magnetic Resonance Imaging (MRI) image influences the disease diagnosis and consequent treatment. However, noise distortion severely impacts these images and tends to interfere with diagnosis during the data acquisition/transmission. This contribution proposes a novel No-reference Image Quality Index (NIQI) method for the intelligent estimation of MRI images and to evaluate its efficacy compared to well-established approaches. A novel Optimized Deep Knowledge-based NIQI (ODK-NIQI) method is developed and tested rigorously. The ODK-NIQI method combines shuffle shepherd optimization and improved deep mish-activated ConvNet approach. The implementation of the projected approach is conducted in MATLAB software. The results demonstrate that the proposed method achieves the best performance and the highest consistency of objectives for both the noisy and denoised MRI brain images investigated. Additionally, the proposed method shows significant improvement over the traditional NIQI techniques using standard performance metrics comprising the Spearman's Rank Correlation Coefficient (SROCC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Pearson Linear Correlation Coefficient (PLCC). Overall, the proposed ODK-based NIQI strategy performs well in denoising MRI images

A Brief Overview of Energy Efficiency Resources in Emerging Wireless Communication Systems

It is crucial to design new communication technologies to surmount the setbacks in RF communication systems. A suitable energy-efficiency scheme helps evade needless energy consumption in wireless communication. Appropriate choice of the most suitable energy-efficiency scheme aids in selecting the most energy-efficient equipment to minimize the expense of energy towards decreasing individual network element energy consumption without affecting their unique features. This review presents the energy efficiency challenges in wireless communication by employing different technologies. The emergence of visible light communication (VLC) provides an energy-efficient wireless communication system despite the various challenges inherent in its adoption that limit its physical realization. This work seeks to harness the potential of the transmission capabilities of VLC while providing an insight into novel practical implementation techniques. The work also addresses the energy consumption problem of low-active components and idle period of active components of base stations by using sleep modes for their systematic turning off and on. The high cost of power supply and the environmental emission of gases from base stations are also addressed by integrating a renewable energy resource into the conventional standalone diesel generators. Overall, the work provides an overview of information necessary for foundational research in energy-efficient resources applied to emerging wireless communication systems

Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning

Over the past couple of decades, many telecommunication industries have passed through the different facets of the digital revolution by integrating artificial intelligence (AI) techniques into the way they run and define their processes. Relevant data acquisition, analysis, harnessing, and mining are now fully considered vital drivers for business growth in these industries. Machine learning, a subset of artificial intelligence (AI), can assist, particularly in learning patterns in big data chunks, intelligent extrapolative extraction of data and automatic decision-making in predictive learning. Firstly, in this paper, a detailed performance benchmarking of adaptive learning capacities of different key machine-learning-based regression models is provided for extrapolative analysis of throughput data acquired at the different user communication distances to the gNodeB transmitter in 5G new radio networks. Secondly, a random forest (RF)-based machine learning model combined with a least-squares boosting algorithm and Bayesian hyperparameter tuning method for further extrapolative analysis of the acquired throughput data is proposed. The proposed model is herein referred to as the RF-LS-BPT method. While the least-squares boosting algorithm is engaged to turn the possible RF weak learners to form stronger ones, resulting in a single strong prediction model, the Bayesian hyperparameter tuning automatically determines the best RF hyperparameter values, thereby enabling the proposed RF-LS-BPT model to obtain desired optimal prediction performance. The application of the proposed RF-LS-BPT method showed superior prediction accuracy over the ordinary random forest model and six other machine-learning-based regression models on the acquired throughput data. The coefficient of determination (Rsq) and mean absolute error (MAE) values obtained for the throughput prediction at different user locations using the proposed RF-LS-BPT method range from 0.9800 to 0.9999 and 0.42 to 4.24, respectively. The standard RF models attained 0.9644 to 0.9944 Rsq and 5.47 to 12.56 MAE values. The improved throughput prediction accuracy of the proposed RF-LS-BPT method demonstrates the significance of hyperparameter tuning/optimization in developing precise and reliable machine-learning-based regression models. The projected model would find valuable applications in throughput estimation and modeling in 5G and beyond 5G wireless communication systems