Optimization of system’s parameters for wavelength conversion of E-band signals

Current and future wireless communication systems are designed to achieve the user’s demands such as high data rate and high speed with low latency and simultaneously to save bandwidth and spectrum. In 5G and 6G networks, a high speed of transmitting and switching is required for internet of things (IoT) applications with higher capacity. To achieve these requirements a semiconductor optical amplifier (SOA) is considered as a wavelength converter to transmit a signal with an orthogonal frequency division multiplexing with subcarrier power modulation (OFDM-SPM). It exploits the subcarrier’s power in conventional OFDM block in order to send additional bits beside the normally transmitted bits. In this paper, we optimized the SOA’s parameters to have efficient wavelength conversion process. These parameters are included the injection current (IC) of SOA, power of pump and probe signals. A 7 Gbps OFDM-SPM signal with a millimeter waves (MMW) carrier of 80 GHz is considered for signal switching. The simulation results investigated and analyzed the performance of the designed system in terms of error vector magnitude (EVM), bit error rate (BER) and optical signal-to-noise ratio (OSNR). The optimum value of IC is 0.6 A while probe power is 9.45 and 8.9 dBm for pump power. The simulation is executed by virtual photonic integrated (VPI) software.

LOGO: an efficient local and global data collection mechanism for remote underwater monitoring

The oceans play an important role in our daily life and they form the lungs of our planet. Subsequently, the world ocean provides so many benefits for humans and the planet including oxygen production, climate regulation, transportation, recreation, food, medicine, economic, etc. However, the oceans suffer nowadays from several challenges ranging from pollution to climate change and destruction of underwater habitat. Hence, the use of remote sensing technologies, like sensor networks and IoT, is becoming essential in order to continuously monitor the wide underwater areas and oceans. Unfortunately, the limited battery power constitutes one of the major challenges and limitations of such technologies. In this paper, we propose an efficient LOcal and GlObal data collection mechanism, called LOGO, that aims to conserve the energy in remote sensing applications. LOGO is based on the cluster scheme and works on two network stages: local and global. The local stage is at the sensor node and aims to reduce its data transmission by eliminating on-period and in-period data redundancies. The global stage is at the autonomous underwater vehicle (AUV) level and aims to minimize the data redundancy among neighboring nodes based on a spatial-temporal node correlation and Kempe’s graph techniques. The simulation results on real underwater data confirm that LOGO mechanism is less energy consumption with high data accuracy than the existing techniques.

Welfare Sanctions and Deprivation in Germany: Do First Sanctions Lead to Higher Levels of Deprivation Among the Long-Term Unemployed and Recipients of Basic Income Support?

In Germany, as in many other European countries, vast changes in the welfare regime – towards workfare – have taken place. As a central activating element of workfare, sanctions were introduced to take effect by temporarily increasing deprivation through benefit cuts. This paper provides first quantitative insights on the effect of first sanctions on deprivation and contributes to the recent debate on the (un)constitutionality of sanctions, which re-emerged after a verdict of the Federal Constitutional Court, criticizing the lack of knowledge about the effects of sanctions on those affected. We implement a difference-in-differences propensity score matching approach that addresses selection on observables and individual time constant unobserved differences. High data accuracy is ensured by combining the “Panel Labour Market and Social Security” (PASS) with administrative data from the Federal Employment Agency. The results illustrate a slightly higher yet statistically insignificant level of deprivation for first-sanctioned unemployment/basic income recipients compared to non-sanctioned recipients. The results hint in the direction that higher levels of deprivation are not what activates the sanctioned beneficiaries to reintegrate into the labour market. We discuss whether the results imply a significant deviation from the socio-cultural subsistence minimum of sanctioned recipients and a failure of the welfare state.

Terahertz Channel Propagation Phenomena, Measurement Techniques and Modeling for 6G Wireless Communication Applications: a Survey, Open Challenges and Future Research Directions

The Terahertz (THz) band (0.1-3.0 THz) spans a great portion of the Radio Frequency (RF) spectrum that is mostly unoccupied and unregulated. It is a potential candidate for application in Sixth-Generation (6G) wireless networks as it has the capabilities of satisfying the high data rate and capacity requirements of future wireless communication systems. Profound knowledge of the propagation channel is crucial in communication systems design which nonetheless, is still at its infancy as channel modeling at THz frequencies has been mostly limited to characterizing fixed Point-to-Point (P2P) scenarios up to 300 GHz. Provided the technology matures enough and models adapt to the distinctive characteristics of the THz wave, future wireless communications systems will enable a plethora of new use cases and applications to be realized in addition to delivering higher spectral efficiencies that would ultimately enhance the Quality-of-Service (QoS) to the end user. In this paper, we provide an insight into THz channel propagation characteristics, measurement capabilities and modeling methods along with recommendations that will aid in the development of future models in the THz band. We survey the most recent and important measurement campaigns and modeling efforts found in literature based on the use cases and system requirements identified. Finally, we discuss the challenges and limitations of measurements and modeling at such high frequencies and contemplate the future research outlook toward realizing the 6G vision.

Terahertz Channel Propagation Phenomena, Measurement Techniques and Modeling for 6G Wireless Communication Applications: a Survey, Open Challenges and Future Research Directions

The Terahertz (THz) band (0.1-3.0 THz) spans a great portion of the Radio Frequency (RF) spectrum that is mostly unoccupied and unregulated. It is a potential candidate for application in Sixth-Generation (6G) wireless networks as it has the capabilities of satisfying the high data rate and capacity requirements of future wireless communication systems. Profound knowledge of the propagation channel is crucial in communication systems design which nonetheless, is still at its infancy as channel modeling at THz frequencies has been mostly limited to characterizing fixed Point-to-Point (P2P) scenarios up to 300 GHz. Provided the technology matures enough and models adapt to the distinctive characteristics of the THz wave, future wireless communications systems will enable a plethora of new use cases and applications to be realized in addition to delivering higher spectral efficiencies that would ultimately enhance the Quality-of-Service (QoS) to the end user. In this paper, we provide an insight into THz channel propagation characteristics, measurement capabilities and modeling methods along with recommendations that will aid in the development of future models in the THz band. We survey the most recent and important measurement campaigns and modeling efforts found in literature based on the use cases and system requirements identified. Finally, we discuss the challenges and limitations of measurements and modeling at such high frequencies and contemplate the future research outlook toward realizing the 6G vision.

Multimarker Approach to Evaluate the Exposure to Electromagnetic Fields at 27 GHz (5g) On Danio rerio larvae

5G technology is evolving to satisfy several service requirements favoring high data-rate connections and lower latency times than current ones (< 1ms). 5G systems use different frequency bands of the radio wave spectrum, taking advantage of higher frequencies than previous mobile radio generations. In order to guarantee a capillary coverage of the territory for high reliability applications, it will be necessary to install a large number of repeaters because higher frequencies waves have a lower capacity to propagate in free space. Following the introduction of this new technology, there has been a growing concern about possible harmful effects on human health. The aim of this study is investigating possible short term effects induced by 5G-millimeter waves on embryonic development of Danio rerio. We have exposed fertilized eggs to 27 GHz frequency, 9.7 mW/cm2 incident power density, 23 dbm and have measured several endpoints every 24 hours. The exposure to electromagnetic fields at 27 GHz (5G) caused no significant impacts on mortality nor on morphology because the exposed larvae showed a normal detachment of the tail, presence of heart-beat and well-organised somites. A weak positivity on exposed larvae has been highlighted by immunohistochemical analysis.

Secure Authentication Scheme Using Diffie–Hellman Key Agreement for Smart IoT Irrigation Systems

Smart irrigation is considered one of the most significant agriculture management systems worldwide, considering the current context of water scarcity. There is a clear consensus that such smart systems will play an essential role in achieving the economic growth of other vital sectors. In general, the consequences of global warming and the unavailability of clean water sources for the agricultural sector are clear indications that the demand for these systems will increase in the near future, especially considering the recent expansions in the use of the Internet of Things (IoT) and Wireless Sensor Network (WSN) technologies, which have been employed in the development of such systems. An obvious result is that security challenges will be one of the main obstacles to attaining the widespread adoption of such systems. Therefore, this paper proposes a secure authentication scheme using Diffie–Hellman key agreement for smart IoT irrigation systems using WSNs. This scheme is based on Diffie–Hellman and one-way hash cryptographic functions in order to support the basic security services with a high data rate and ability to resist well-known attacks. The Burrows–Abadi–Needham (BAN) logic model is used to verify the proposed scheme formally. Based on various possible attack scenarios, a resistance analysis of the proposed scheme is discussed. Further analyses are performed in terms of the storage size, intercommunication, and running time costs. Therefore, the proposed scheme not only can be considered a secure authentication scheme but is also practical for smart IoT irrigation systems due to its reasonable efficiency factors.

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks

The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results.

Quantifying Streamflow Depletion from Groundwater Pumping: A Practical Review of Past and Emerging Approaches for Water Management

Groundwater pumping can cause reductions in streamflow (‘streamflow depletion’) that must be quantified for conjunctive management of groundwater and surface water resources. However, streamflow depletion cannot be measured directly and is challenging to estimate because pumping impacts are masked by streamflow variability due to other factors. Here, we conduct a management-focused review of analytical, numerical, and statistical models for estimating streamflow depletion and highlight promising emerging approaches. Analytical models are easy to implement, but include many assumptions about the stream and aquifer. Numerical models are widely used for streamflow depletion assessment and can represent many processes affecting streamflow, but have high data, expertise, and computational needs. Statistical approaches are a historically underutilized tool due to difficulty in attributing causality, but emerging causal inference techniques merit future research and development. We propose that streamflow depletion-related management questions can be divided into three broad categories (attribution, impacts, and mitigation) that influence which methodology is most appropriate. We then develop decision criteria for method selection based on suitability for local conditions and the management goal, actionability with current or obtainable data and resources, transparency with respect to process and uncertainties, and reproducibility.

Interactive Deep Learning for Shelf Life Prediction of Muskmelons Based on an Active Learning Approach

A pivotal topic in agriculture and food monitoring is the assessment of the quality and ripeness of agricultural products by using non-destructive testing techniques. Acoustic testing offers a rapid in situ analysis of the state of the agricultural good, obtaining global information of its interior. While deep learning (DL) methods have outperformed state-of-the-art benchmarks in various applications, the reason for lacking adaptation of DL algorithms such as convolutional neural networks (CNNs) can be traced back to its high data inefficiency and the absence of annotated data. Active learning is a framework that has been heavily used in machine learning when the labelled instances are scarce or cumbersome to obtain. This is specifically of interest when the DL algorithm is highly uncertain about the label of an instance. By allowing the human-in-the-loop for guidance, a continuous improvement of the DL algorithm based on a sample efficient manner can be obtained. This paper seeks to study the applicability of active learning when grading ‘Galia’ muskmelons based on its shelf life. We propose k-Determinantal Point Processes (k-DPP), which is a purely diversity-based method that allows to take influence on the exploration within the feature space based on the chosen subset k. While getting coequal results to uncertainty-based approaches when k is large, we simultaneously obtain a better exploration of the data distribution. While the implementation based on eigendecomposition takes up a runtime of O(n3), this can further be reduced to O(n·poly(k)) based on rejection sampling. We suggest the use of diversity-based acquisition when only a few labelled samples are available, allowing for better exploration while counteracting the disadvantage of missing the training objective in uncertainty-based methods following a greedy fashion.