K-Means Fingerprint Clustering for Low-Complexity Floor Estimation in Indoor Mobile Localization

Indoor localization in multi-floor buildings is an important research problem. Finding the correct floor, in a fast and efficient manner, in a shopping mall or an unknown university building can save the users' search time and can enable a myriad of Location Based Services in the future. One of the most widely spread techniques for floor estimation in multi-floor buildings is the fingerprinting-based localization using Received Signal Strength (RSS) measurements coming from indoor networks, such as WLAN and BLE. The clear advantage of RSS-based floor estimation is its ease of implementation on a multitude of mobile devices at the Application Programming Interface (API) level, because RSS values are directly accessible through API interface. However, the downside of a fingerprinting approach, especially for large-scale floor estimation and positioning solutions, is their need to store and transmit a huge amount of fingerprinting data. The problem becomes more severe when the localization is intended to be done on mobile devices which have limited memory, power, and computational resources. An alternative floor estimation method, which has lower complexity and is faster than the fingerprinting is the Weighted Centroid Localization (WCL) method. The trade-off is however paid in terms of a lower accuracy than the one obtained with traditional fingerprinting with Nearest Neighbour (NN) estimates. In this paper a novel K-means-based method for floor estimation via fingerprint clustering of WiFi and various other positioning sensor outputs is introduced. Our method achieves a floor estimation accuracy close to the one with NN fingerprinting, while significantly improves the complexity and the speed of the floor detection algorithm. The decrease in the database size is achieved through storing and transmitting only the cluster heads (CH's) and their corresponding floor labels.Comment: Accepted to IEEE Globecom 2015, Workshop on Localization and Tracking: Indoors, Outdoors and Emerging Network

Yield response models to phosphorus application: a research synthesis of Finnish field trials to optimize fertilizer P use of cereals

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Modelling of Soil Organic Carbon Dynamics in Kazakhstan

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Phosphorus and Nitrogen Yield Response Models for Dynamic Bio-Economic Optimization: An Empirical Approach

Nitrogen (N) and phosphorus (P) are both essential plant nutrients. However, their joint response to plant growth is seldom described by models. This study provides an approach for modeling the joint impact of inorganic N and P fertilization on crop production, considering the P supplied by the soil, which was approximated using the soil test P (STP). We developed yield response models for Finnish spring barley crops (Hordeum vulgare L.) for clay and coarse-textured soils by using existing extensive experimental datasets and nonlinear estimation techniques. Model selection was based on iterative elimination from a wide diversity of plausible model formulations. The Cobb-Douglas type model specification, consisting of multiplicative elements, performed well against independent validation data, suggesting that the key relationships that determine crop responses are captured by the models. The estimated models were extended to dynamic economic optimization of fertilization inputs. According to the results, a fair STP level should be maintained on both coarse-textured soils (9.9 mg L-1 a(-1)) and clay soils (3.9 mg L-1 a(-1)). For coarse soils, a higher steady-state P fertilization rate is required (21.7 kg ha(-1) a(-1)) compared with clay soils (6.75 kg ha(-1) a(-1)). The steady-state N fertilization rate was slightly higher for clay soils (102.4 kg ha(-1) a(-1)) than for coarse soils (95.8 kg ha(-1) a(-1)). This study shows that the iterative elimination of plausible functional forms is a suitable method for reducing the effects of structural uncertainty on model output and optimal fertilization decisions.Peer reviewe

Dynamic phosphorus and nitrogen yield response model for economic optimisation

This paper provides an approach for modelling joint impact of two main nutrients in crop production for situations where there are available separate datasets for nitrogen and phosphorus fertiliser field experiments. Developing yield response models for Finnish spring barley crops (Hordeum vulgare L.) for clay and coarse soils and applying the models for dynamic economic analysis demonstrate the modelling approach. Model selection is based on iterative elimination from a wide diversity of plausible model formulations. Nonlinear weighted least squares method was utilised in estimation of the yield response models and dynamic programming was utilised in economic analysis. Our results suggest that fertiliser recommendations can be insufficient if soil phosphorus dynamics are ignored. Further, the optimal fertilisation rates for nitrogen and phosphorus, as well as the economic alternative costs of agri-environmental programmes depend on the soil texture of production area. Therefore, the efficiency of such programmes could be improved by targeting different fertilisation limits for different soil textures. In addition, uncertainty analysis revealed that the parameter uncertainty had a greater effect on the model output than the structural uncertainty. Further, the interaction of nitrogen and phosphorus fertilisers appeared to be a factor of relatively minor importance. The modelling approach and the model structure can be extended to other geographical areas, given that adequate datasets are available.This paper provides an approach for modelling joint impact of two main nutrients in crop production for situations where there are available separate datasets for nitrogen and phosphorus fertiliser field experiments. Developing yield response models for Finnish spring barley crops (Hordeum vulgare L.) for clay and coarse soils and applying the models for dynamic economic analysis demonstrate the modelling approach. Model selection is based on iterative elimination from a wide diversity of plausible model formulations. Nonlinear weighted least squares method was utilised in estimation of the yield response models and dynamic programming was utilised in economic analysis. Our results suggest that fertiliser recommendations can be insufficient if soil phosphorus dynamics are ignored. Further, the optimal fertilisation rates for nitrogen and phosphorus, as well as the economic alternative costs of agri-environmental programmes depend on the soil texture of production area. Therefore, the efficiency of such programmes could be improved by targeting different fertilisation limits for different soil textures. In addition, uncertainty analysis revealed that the parameter uncertainty had a greater effect on the model output than the structural uncertainty. Further, the interaction of nitrogen and phosphorus fertilisers appeared to be a factor of relatively minor importance. The modelling approach and the model structure can be extended to other geographical areas, given that adequate datasets are available

Uplink Transmission Schemes for 5G NR Unlicensed : Design Principles and Achievable Performance

In this paper, we address and analyze different uplink (UL) transmission schemes for the 5G New Radio (NR) deployments at unlicensed spectrum. Specific emphasis is on the new NR-unlicensed (NR-U) wideband physical random access channel (PRACH) under short preamble formats as well as on the base-station receiver sensitivity requirements for the fixed reference channels. In general, in order to comply with ETSI regulations, the UL waveform resource allocation has been revised in NR-U. On one hand, the bandwidth (BW) of the PRACH sequences has been increased according to numerology, which based on the performance analysis presented in this paper provides a substantially improved detection performance while fulfilling the coverage requirements of Re1-15 preambles. On the other hand, the physical uplink shared channel (PUSCH) resource allocation is based on block interlace frequency division multiple access (B-IDFMA). This design is characterized by the number of interlaces allocated within the transmission BW and it should be properly defined for the multiple physical layer numerologies supported in NR systems. The NR-U PUSCH performance results provided in this paper show that the B-IFDMA design yields the best performance for the case of one interlace allocation compared to contiguous resource allocation for all numerologies. Additionally, it is shown that the non-B-IFDMA PUSCH design outperforms the NR-U design for larger number of allocated interlaces. There is thus a trade-off between the frequency diversity gain achieved by the sparse PRB distribution in NR-U and the corresponding channel estimation challenges impacting the demodulation performance.acceptedVersionPeer reviewe

Soil organic carbon under conservation agriculture in Mediterranean and humid subtropical climates: Global meta‐analysis

Conservation agriculture (CA) is an agronomic system based on minimum soil disturbance (no-tillage, NT), permanent soil cover, and species diversification. The effects of NT on soil organic carbon (SOC) changes have been widely studied, showing somewhat inconsistent conclusions, especially in relation to the Mediterranean and humid subtropical climates. These areas are highly vulnerable and predicted climate change is expected to accentuate desertification and, for these reasons, there is a need for clear agricultural guidelines to preserve or increment SOC. We quantitively summarized the results of 47 studies all around the world in these climates investigating the sources of variation in SOC responses to CA, such as soil characteristics, agricultural management, climate, and geography. Within the climatic area considered, the overall effect of CA on SOC accumulation in the plough layer (0–0.3 m) was 12% greater in comparison to conventional agriculture. On average, this result corresponds to a carbon increase of 0.48 Mg C ha−1 year−1. However, the effect was variable depending on the SOC content under conventional agriculture: it was 20% in soils which had ≤ 40 Mg C ha−1, while it was only 7% in soils that had > 40 Mg C ha−1. We proved that 10 years of CA impact the most on soil with SOC ≤ 40 Mg C ha−1. For soils with less than 40 Mg C ha−1, increasing the proportion of crops with bigger residue biomasses in a CA rotation was a solution to increase SOC. The effect of CA on SOC depended on clay content only in soils with more than 40 Mg C ha−1 and become null with a SOC/clay index of 3.2. Annual rainfall (that ranged between 331–1850 mm y−1) and geography had specific effects on SOC depending on its content under conventional agriculture. In conclusion, SOC increments due to CA application can be achieved especially in agricultural soils with less than 40 Mg C ha−1 and located in the middle latitudes or in the dry conditions of Mediterranean and humid subtropical climates

Embedding the Localization and Imaging Functions in Mobile Systems : an Airport Surveillance Use Case

Driven by the extended applications and scarce spectrum resources, integrating the radio sensing functions into the future mobile system has been a consensus between the stakeholders. This paper demonstrates the feasibility of joint localization and imaging functions by exploiting the reference signal in the radio frame defined in the mobile system. Essentially, the subspace-based algorithms are adopted to jointly estimate the angle and distance and to enable the localization function for uplink and downlink signals and the derivation of theoretical performance boundaries. The vector antenna and virtual array concepts are introduced in the downlink scenario to enhance the angle estimation resolution. The uplink sounding reference signal is exploited to enable the imaging function as analog to synthetic-aperture radar (SAR). The joint localization and imaging performances are verified with a realistic ray-tracing channel based on the 3D ground and buildings model of Muret airport in France. The simulation results show that the reference signal can provide acceptable localization accuracy and target-distinguishing capability by adopting a virtual array and joint time-spatial smoothing in downlink and uplink, respectively. The joint localization and imaging results prove that the future mobile network is potentially a viable infrastructure to provide economic surveillance solutions for airports, particularly for secondary airports that are not well equipped with dedicated surveillance systems.publishedVersionPeer reviewe

Embedding the Radio Imaging in 5G Networks : Signal Processing and an Airport Use Case

Integrating sensing and communications is becoming a rising trend in the architecture design of the foreseeable mobile communications system, which could be driven by multifold applications and scarce spectrum resources. Regarding the demand for the economic surveillance solution in the secondary airports, the inborn imaging function in the 5G networks could be a promising candidate. This paper investigates the feasibility and capability of using 5G uplink and downlink reference signals for imaging purposes. An ambiguity function-based signal processing method is proposed in this paper to elaborate the imaging functionality in the 5G networks. The 5G signal-based imaging idea is validated with a realistic ray-tracing channel model generated from a simulated 3D airport model. Our method empowers the imaging functionality of the wireless communications system solely without the aid of external signal resources. Different from the conventional synthetic-aperture radar processing, our methods are adjusted for unevenly allocated reference signal symbols, which causes mirror images problem. The mirror images are quantified in the simulation result, and the mitigation strategies such as lower flight speed and narrower beam are proposed to resolve the problem.acceptedVersionPeer reviewe