Compressive sensing approach in the table grape cold chain logistics

Draganic, Andjela; Orović, Irena; Stanković, Srdjan; Zhang, Xiaoshuan; Wang, Xiang

doi:10.1109/meco.2017.7977143

Cited by 11 publications

(8 citation statements)

References 21 publications

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“…Traditional cold storage gauges only temperature, relative humidity and ignores all other environmental parameters that contribute to uplift the shelf-life of FVs ultimately reduce the high loss rate of FVs. Henceforth, it is vital to address this issue and a critical measure is needed to reduce the loss of perishable FVs in cold storage through real-time monitoring of vital environmental parameters such as temperature, relative humidity, CO2 [15] and light intensity [16]. The existing studies about cold supply chain that bank on an IoT-based approach and prediction model are illustrated in Table I and having following challenges.…”

Section: Introductionmentioning

confidence: 99%

An IoT-Based Real-Time Intelligent Monitoring and Notification System of Cold Storage

Afreen

Bajwa

2021

IEEE Access

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The intake of the perishable fruits and vegetables (FVs) in the human diet can contribute to reduce the risk of some chronic diseases. But unfortunately, FVs loss rate is high among all the food produced annually and occurs at storage stage of post-harvest life cycle. One of the key factors contributing to this high loss rate is inability to gauge vital ambient environmental parameters in cold storage. The existing monitoring solutions about cold storage are limited to only gauge temperature, relative humidity and ignore other vital ambient environmental parameters such as luminosity and concentration of gases. This is a critical issue that needs to be addressed to overcome the loss rate of FVs. This paper presents a real-time intelligent monitoring and notification system (RT-IMNS) banked on an Internet of Things (IoT)enabled approach for real-time monitoring of temperature, relative humidity, luminosity and concentration of gas in cold storage and notifies the personnel on exceeding of dangerous limits of these parameters. Moreover, decision support is implemented in the RT-IMNS using Artificial Neural Network (ANN) with forward propagation to classify the status of commodity into one of three classes i.e. good, unsatisfactory or alarming. The proposed prediction model outperforms Compress Sending (CS), Adaptive Naïve Bayes (ANB), Extreme Gradient Boosting (XGBoost) and Data Mining (DM) with respect to forecasting accuracy. We achieved 99% accuracy using forward propagation neural network model while existing models such as CS, ANB, XGBoost, DM achieved 95.60%, 87.50%, 93.59%, 90% accuracy respectively. Moreover, proposed approach achieved 100% precision, 100% recall, 100% F1-score for good class is achieved, for unsatisfactory class precision is 98%, recall is 99%, F1-score is 98% and for alarming class precision is 100%, recall is 98% and F1-score is 99%.

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Section: Introductionmentioning

confidence: 99%

An IoT-Based Real-Time Intelligent Monitoring and Notification System of Cold Storage

Afreen

Bajwa

2021

IEEE Access

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“…Therefore, highly efficient compression methods are required to transmit these enormous amount of medical data which can ensure preservation of its essential features. Thus, several researchers have shown their interest in finding a suitable compression method for medical data transmission and many researchers have considered Compressive Sensing (CS) method is a suitable and attractive option for biomedical signal compression [2][3][4][5]. CS is an exceptional signal compression technique which exploits the sparsity nature of medical signals to attain real-time, precise and power-effective compression outcomes.…”

Section: Introductionmentioning

confidence: 99%

Channel State Information (CSI) based Sparse Reconstruction for Biomedical Applications Using hybrid mm-WAVE MIMO System

et.al¹

2021

TURCOMAT

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The significance of Channel State Information (CSI) is very essential in a hybrid mm-WAVE Multiple Input Multiple Input (MIMO) System due to its direct dependency on medium capacity and energy efficiency of a network. Therefore, a Channel State Information (CSI)-based Sparse Reconstruction (CSISR) technique is adopted for effective evaluation of CSI for future 5G cellular network implementation. A hybrid mm-WAVE MIMO communication system is also employed for effective bandwidth spectrum utilization. Furthermore, a joint sparse coding algorithm is introduced to study the channel matrices of hybrid mm-WAVE MIMO system. The proposed CSISR technique ensure proficient signal reconstruction, signal compression and resource reduction by exploiting sparsity of channel matrix. The proposed CSISR technique under low SNR conditions as well for hybrid mm-WAVE MIMO system with optimization of pre-processors and combiners. The performance throughput of proposed CSISR technique is measured against conventional algorithms considering power consumption, Normalized Mean Square Error (NMSE) and spectral efficiency of the mm-Wave MIMO system. The superiority of proposed CSISR technique is concluded based on simulations considering different system configurations and performance matrices.

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“…Their research contributed to the analysis of battery life under cooling conditions and the evaluation of the reliability of communications and measurements. Draganić et al (2017) looked into a new type of compressive (compact) sensor that can describe the changes within the cold supply chain of CO2 level inside the packaging of table grapes. The proposed method was claimed to be able to prevent communication system overloading but still maintain high-quality monitoring.…”

Section: Introductionmentioning

confidence: 99%