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Space-shift-keying (SSK) and spatial modulation (SM) enable multiple antenna transmission systems to convey information on antenna indices. While SSK/SM helps reduce the number of radio frequency (RF) chains, large numbers of antennas and low spatial correlations are required to achieve high data rates. This work investigates the use and design of multifunctional reconfigurable antennas (MRAs) for SSK/SM based transmission where a single-element MRA generates large numbers of modes. To enhance legacy SSK/SM performance while reducing RF hardware complexity, we propose single-and multi-carrier antenna mode-shift keying (MoSK) and mode modulation (MoM) schemes facilitated by MRAs. Based on an error probability analysis, we determine criteria for MRA design and mode set selection suitable for MoSK/MoM. We also develop two MRA designs and investigate their performances over Rayleigh fading channels. We argue that by creating MRA modes with low pattern correlations, channel correlations can be reduced to improve the detection performance. Extensive simulations demonstrate that MoSK/MoM performance exceeds that of SSK/SM along with significant complexity reduction. For instance, a single-carrier MoSK/MoM using a single MRA with 8 modes achieves about 2 dB gain compared to legacy SSK/SM requiring 8 antennas, and by multi-carrier MoSK/MoM using 4 subcarriers, an MRA with 32 modes can attain an error rate performance comparable to this single-carrier system.

Total length of paved road in Rajshahi City is about 186.64 km (2011 census). It shows that about 23% of total road lengths are present in failure condition. This study shows an investigation to compute the flexible pavement’s failure types, to define and identify the causes, and select the best maintenance for that failures of flexible pavement within Rajshahi City. There are many types of failures occur on the roads such as different types of cracks, potholes, raveling, water bleeding, corrugation and shoving, depression, and rutting. The possible causes of flexible pavement failures are improper bituminous mixes with poor quality of materials, heavy traffic loads, heavy rainfall, and bad drainage on pavement. The failures are gradually raised due to lack of proper planning, inspection, and treatment. These failures create different types of disadvantages like traffic jam, discomfort to the passengers as well as drivers, increasing vehicle operating, maintenance cost, etc. This study proves that the authorities practice maintenance procedures to repair the failures of pavement which are about 60% similar to the conventional road maintenance procedures. The authorities as Roads and Highway Department and Rajshahi City Corporation are suggested to do maintenance according to the requirements of urgent maintenance and availability of fund which is found in this investigation.

Traffic count stations play a key role in measuring roadway characteristics and traffic performance by collecting and monitoring travel behavior and vehicle data. Continuous counting stations (CCSs), which count traffic volumes continuously throughout the year, are used to develop seasonal adjustment factors to convert short-term traffic counts (average daily traffic) to annual average daily traffic (AADT). As data collection is conducted at limited locations, many state Departments of Transportation (DOTs) group the CCSs based on different traffic patterns and estimate the AADT at specific locations by considering seasonal adjustment factors. Computer-based clustering approaches are widely used in grouping continuous traffic data for their accuracy in traffic pattern recognition. However, most of the clustering techniques do not consider the spatial constraints and therefore overlooked the locational proximity and inference from nearby traffic data. In this study, a GIS-based multivariate spatial clustering approach was developed to recognize statewide traffic patterns based on temporal and spatial variables. A total of 12 optimal clusters were automatically computed and labeled based on the proposed clustering algorithm. The proposed clustering approach was compared and validated based on machine learning classifiers. The results showed that it outperformed the traditional Michigan DOT clustering approach and was consistent in nature across different years. The model was applied to estimate the AADT, and good accuracy was detected relative to other approaches. The proposed clustering method offers a new approach to group traffic patterns by simultaneously incorporating proximity and similarity of traffic data.