An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

The wavelet transform has emerged over recent years as a powerful time-frequency analysis and signal coding tool favoured for the interrogation of complex nonstationary signals. Its application to biosignal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the ECG. In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.

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Wavelet transforms and the ECG: a review.

Pattern recognition

Machine learning algorithms for wireless sensor networks: A survey

Increasing cost and demand of energy has led many organizations to find smart ways for monitoring, controlling and saving energy. A smart Energy Management System (EMS) can contribute towards cutting the costs while still meeting energy demand. The emerging technologies of Internet of Things (IoT) and Big Data can be utilized to better manage energy consumption in residential, commercial, and industrial sectors. This paper presents an Energy Management System (EMS) for smart homes. In this system, each home device is interfaced with a data acquisition module that is an IoT object with a unique IP address resulting in a large mesh wireless network of devices. The data acquisition System on Chip (SoC) module collects energy consumption data from each device of each smart home and transmits the data to a centralized server for further processing and analysis. This information from all residential areas accumulates in the utility’s server as Big Data. The proposed EMS utilizes off-the-shelf Business Intelligence (BI) and Big Data analytics software packages to better manage energy consumption and to meet consumer demand. Since air conditioning contributes to 60% of electricity consumption in Arab Gulf countries, HVAC (Heating, Ventilation and Air Conditioning) Units have been taken as a case study to validate the proposed system. A prototype was built and tested in the lab to mimic small residential area HVAC systems1.

A smart home energy management system using IoT and big data analytics approach

This paper presents a new technique for the design of IIR digital filters with simultaneous amplitude and group-delay requirements The filter stability is guaranteed by controlling the poles' positions and the filter delay This algorithm is based on genetic algorithm where the genetic parameters such as population size, number of generations, and crossover probability are adopted for designing filters with power of two, integer or real coefficients The fitness function is defined as the simple reciprocal of sum squared of the magnitude and delay errors The proposed method has been tested for the design of filters with different amplitude and group-delay specifications

Design of IIR filters with simultaneous amplitude and group-delay characteristics using genetic algorithm

A MIMO CPW-Fed slot antenna is presented for sub-6 GHz 5G applications. This antenna operates at the lower band which extended from 4.2 to 5.2 GHz. The overall MIMO dimensions are 21.6 × 42.2 mm2. The proposed MIMO consists of two identical antennas; each one is a rectangular slot excited with rectangular tuner. A triangle shape and two shunt stubs are connected to the rectangular slot to reduce the antenna size. A unique asymmetric electromagnetic bandgap (EBG) structure is utilized beside polarization diversity for coupling reduction. The element to element isolation is improved to vary from (-26 dB to -50 dB) in case of 0.1 λ 0 edge to edge spacing. The simulated and measured results confirm that the proposed antenna is suitable for sub-6 GHz 5G applications. The envelop correlation coefficient is less than 0.025 over the operating band and the diversity gain is almost 10 dB showing satisfactory performance.

Dual Port MIMO Antenna with Low Mutual Coupling Based on Asymmetric EBG Decoupling Structure

Wideband spectrum sensing is a challenging task in wideband cognitive radio networks. It needs to be efficient, robust and fast. However, there are many challenges facing sensing in wideband spectrum. One of these challenges is Nyquist sampling rate bottleneck which required high speed DSP and large storage spaces. Compressive sensing is a pioneer solution for wideband spectrum sensing which have proved sampling below Nyquist criterion. In this paper, we provide mathematical expression that computes the number of measurements required for compressed detection exploiting DCT as sensing matrix. The simulation results show that the accuracy of detection for certain number measurements is similar to the results obtained by compressed measurements based spectrum sensing for wideband cognitive radio systems with small relative error. The comparison is based on term of probability of detection versus compression ratios.

Detection of primary user signal in wideband cognitive radio networks exploiting DCT as sensing matrix

Remote patient monitoring systems are based on a combination of advanced remote monitoring devices, telecommunication technology, and innovative software and hardware. As mobile phones become more powerful and perform more complex interactions between mobile devices to resident software and other server based software, they have been recognized as effective tools for telemedicine. So, the merging of the Internet and mobile computing, introduce new opportunities and challenges in the telemedicine sector. This study describes the development and implementation of an Android based smart phone in the home monitoring health care system. The system utilizes Android devices as mobile access terminals for patient-monitoring services. The main goal is subsequently divided into two parts: in the first part we establish a reliable connection between mobile device and a sensor to collect continuous data from patients. In the second part we provide ElectroCardiGram (ECG) andPhoto-PlethysmoGraphy(PPG) signal filtering and processing, feature extraction, detection of any abnormalities in ECG and calculating heart rate using the most familiar and multi-purpose MATLAB software.

http://www.scipublish.com/journals/STSP/papers/download/3309-1248.pdf

Remote Online Vital Signs Processing For Patient Monitoring and Diagnosis

Accurately detecting early developmental stages of insect pests (larvae) from off-the-shelf stereo camera sensor data using deep learning holds several benefits for farmers, from simple robot configuration to early neutralization of this less agile but more disastrous stage. Machine vision technology has advanced from bulk spraying to precise dosage to directly rubbing on the infected crops. However, these solutions primarily focus on adult pests and post-infestation stages. This study suggested using a front-pointing red-green-blue (RGB) stereo camera mounted on a robot to identify pest larvae using deep learning. The camera feeds data into our deep-learning algorithms experimented on eight ImageNet pre-trained models. The combination of the insect classifier and the detector replicates the peripheral and foveal line-of-sight vision on our custom pest larvae dataset, respectively. This enables a trade-off between the robot’s smooth operation and localization precision in the pest captured, as it first appeared in the farsighted section. Consequently, the nearsighted part utilizes our faster region-based convolutional neural network-based pest detector to localize precisely. Simulating the employed robot dynamics using CoppeliaSim and MATLAB/SIMULINK with the deep-learning toolbox demonstrated the excellent feasibility of the proposed system. Our deep-learning classifier and detector exhibited 99% and 0.84 accuracy and a mean average precision, respectively.

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Agricultural Robot-Centered Recognition of Early-Developmental Pest Stage Based on Deep Learning: A Case Study on Fall Armyworm (Spodoptera frugiperda)

Sabah M. Ahmed

Papers

Design of IIR filters with simultaneous amplitude and group-delay characteristics using genetic algorithm

Dual Port MIMO Antenna with Low Mutual Coupling Based on Asymmetric EBG Decoupling Structure

Detection of primary user signal in wideband cognitive radio networks exploiting DCT as sensing matrix

Remote Online Vital Signs Processing For Patient Monitoring and Diagnosis

Agricultural Robot-Centered Recognition of Early-Developmental Pest Stage Based on Deep Learning: A Case Study on Fall Armyworm (Spodoptera frugiperda)