Computer aided diagnosis of brain abnormalities using texture analysis of MRI images

The drive of this study is to develop a robust system. A method to classify brain magnetic resonance imaging (MRI) image into brain-related disease groups and tumor types has been proposed. The proposed method employed Gabor texture, statistical features, and support vector machine. Brain MRI images have been classified into normal, cerebrovascular, degenerative, inflammatory, and neoplastic. The proposed system has been trained on a complete dataset of Brain Atlas-Harvard Medical School. Further, to achieve robustness, a dataset developed locally has been used. Extraordinary results on different orientations, sequences of both of these datasets as per accuracy (up to 99.6%), sensitivity (up to 100%), specificity (up to 100%), precision (up to 100%), and AUC value (up to 1.0) have been achieved. The tumorous slices are further classified into primary or secondary tumor as well as their further types as glioma, sarcoma, meningioma, bronchogenic carcinoma, and adenocarcinoma, which could not be possible to determine without biopsy, otherwise.     

Synthesis,characterization and adsorption studies of h-BN crystal for efficient removal of Cd2+ from aqueous solution

In the present study, hexagonal boron nitride (h-BN) was synthesized from boric acid and melamine by thermal annealing method in a nitrogen atmosphere. The pure h-BN was used as an efficient sorbent for the uptake of Cd²⁺ ions from the solution phase. The kinetics and sorption studies of metal ions onto the h-BN were carried out in batch adsorption experiments at different temperature, time, pH, sorbent dosage, and concentration of metal ions. The optimum pH for the removal of the Cd²⁺ ions was found to be pH 7. The effect of temperature showed that the process of Cd²⁺ sorption remained endothermic in the range of 298 K–328 K. The Lagergren's first and Ho's second kinetic models were tested to interpret the adsorption kinetic data, however the present data was explained well by Ho's model for kinetics. The thermodynamic perameters ΔG, ΔS and ΔH were determined using the available adsorption data at different temperatures. The physicochemical properties of the synthesized product were also characterized before and after adsorption by different analytical techniques like FT-IR, TGA, XRD and Point of Zero Charge (PZC). The morphology of the surface was analyzed with the help of Scanning Electron Microscopy. The h-BN proved to be an efficient adsorbent for the uptake of the Cd²⁺ ions from aqueous media.     

Assessing and building municipal open data capability

Information Systems and e-Business Management - In the past decade, cities around the world have published their open data as a new service. Some have used this service innovatively, as a vehicle...     

Troubleshooting of Handover Problems in 900 MHz for Speech Quality

Although Long-term Evolution (LTE) technology has currently being used in data and voice transmission, reserved frequency bands for GSM is still in use due to its strengthen against multipath fading and it provides wider coverage area. Poor coverage problems caused by low signal level directly reduce network performance and cause undesirable cases for voice transmission. The aim of this study is first to solve handover (HO) problems due to the low signal quality and bad speech quality by hardware configuration and changing optimization parameters in detail as a novelty. The second is to examine the KPI values of the test region where HO problems have been solved and to determine whether the network contributed to the network quality. Offered method has made the network having following improvements; the value of RxQual drops from 0.61 to 0.57, number of failure in random access channel (RACH) drops down from 12 to 2, the number of failure in SDCCH drops down from 6 to 2, the total number of blocked calls from 18 to 4, and the number of dropped calls drops down to 2 from 5. Another criterion of the network quality the average for both uplink and downlink mean opinion score (MOS) value of region increased from 3.51 to 3.86. Also CSSR has been increased from 94.43 to 97.82% and HO success rate has been reached from 93.56 to 99.13%.

     

Effect of Cryogenic Cooling on the Heat Transfer during Turning of AZ31C Magnesium Alloy

ABSTRACT

A combined analytical and experimental study was carried out to analyze the effects of cryogenic cooling on temperature during turning of AZ31C magnesium alloy. Finite element method was employed to model and simulating the cryogenic and dry turning. Results obtained from the model were found to be in good agreement with the experimental observations. For the maximum temperature at the turned surface, the difference in the experimental and predicted value observed during dry and cryogenic turning was only 4 and 8% respectively. A significant reduction in the maximum temperature on the chip surface (around 35%) and tool surface (around 29%) was observed during the cryogenic turning compared to dry turning. This reduction in temperature was an attribute of liquid nitrogen, which produces intense cooling effect around the vicinity cutting zone where heat generation takes place hence enhancing the heat transfer. The isothermal region belonging to the highest temperature on the tool surface was also reduced by about 42%. The reduction in temperature during cryogenic conditions were found to be beneficial for the machining of magnesium alloys under safe conditions, reducing the risk of ignition and explosions, and also increases the sustainability of the process.     

Mitigation of ground vibrations induced by high speed railways using double geofoam barriers: Centrifuge modeling

This paper presents a study of the propagation and mitigation of ground vibrations induced by high speed railways using 8 centrifuge tests. In the reported tests here, geofoam is used as a barrier in various locations and arrangements (single and double) to mitigate ground vibrations. The results show that the surface waves guide the propagation pattern of ground vibrations induced by high speed railways and also reveal that geofoam is a proper material for the mitigation of such ground vibrations. While the use of single geofoam barriers can reduce ground vibrations by up to 54.5%, their performance at low input frequencies are undesirable. Double geofoam barriers are used and tested in various locations to eliminate such inconvenient effects and improve the mitigation of ground vibrations. The results show that double geofoam barriers can mitigate the vibrations by about 14%–35% more than a single geofoam barrier and undesirable performances for the mentioned low input frequencies are also eliminated.     

Factors that determine residents’ acceptance of smart city technologies

ABSTRACT

While some cities attempt to determine their residents’ demand for smart-city technologies, others simply move forward with smart-related strategies and projects. This study is among the first to empirically determine which factors most affect residents’ and public servants’ intention to use smart-city services. A Smart Cities Stakeholders Adoption Model (SSA), based on Unified Theory of Acceptance and Use of Technology (UTAUT2), is developed and tested on a mid-size U.S. city as a case study. A questionnaire was administered in order to determine the influence of seven factors – effort expectancy, self-efficacy, perceived privacy, perceived security, trust in technology, price value and trust in government – on behaviour intention, specifically the decision to adopt smart-city technologies. Results show that each of these factors significantly influenced citizen intention to use smart-city services. They also reveal perceived security and perceived privacy to be strong determinants of trust in technology, and price value a determinant of trust in government. In turn, both types of trust are shown to increase user intention to both adopt and use smart-city services. These findings offer city officials an approach to gauging residential intention to use smart-city services, as well as identify those factors critical to developing a successful smart-city strategy.     

Detection,localisation and tracking of pallets using machine learning techniques and 2D range data

The problem of autonomous transportation in industrial scenarios is receiving a renewed interest due to the way it can revolutionise internal logistics, especially in unstructured environments. This paper presents a novel architecture allowing a robot to detect, localise, and track (possibly multiple) pallets using machine learning techniques based on an on-board 2D laser rangefinder only. The architecture is composed of two main components: the first stage is a pallet detector employing a Faster Region-Based Convolutional Neural Network (Faster R-CNN) detector cascaded with a CNN-based classifier; the second stage is a Kalman filter for localising and tracking detected pallets, which we also use to defer commitment to a pallet detected in the first stage until sufficient confidence has been acquired via a sequential data acquisition process. For fine-tuning the CNNs, the architecture has been systematically evaluated using a real-world dataset containing 340 labelled 2D scans, which have been made freely available in an online repository. Detection performance has been assessed on the basis of the average accuracy over k-fold cross-validation, and it scored 99.58% in our tests. Concerning pallet localisation and tracking, experiments have been performed in a scenario where the robot is approaching the pallet to fork. Although data have been originally acquired by considering only one pallet as per specification of the use case we consider, artificial data have been generated as well to mimic the presence of multiple pallets in the robot workspace. Our experimental results confirm that the system is capable of identifying, localising and tracking pallets with a high success rate while being robust to false positives.