The operation of groups of heavy-duty vehicles at small inter-vehicular distances, known as platoons, lowers the overall aerodynamic drag and, therefore, reduces fuel consumption and greenhouse gas emissions. Experimental tests conducted on a flat road and without traffic have shown that platooning has the potential to reduce the fuel consumption up to 10%. However, platoons are expected to drive on public highways with varying topography and traffic. Due to the large mass and limited engine power of heavy-duty vehicles, road slopes can have a significant impact on feasible and optimal speed profiles. Therefore, maintaining a short inter-vehicular distance without coordination can result in inefficient or even infeasible speed trajectories. Furthermore, external traffic can interfere by affecting fuel-efficiency and threatening the safety of the platooning vehicles. This thesis addresses the problem of safe and fuel-efficient control for heavy-duty vehicle platooning. We propose a hierarchical control architecture that splits this complex control problem into two layers. The layers are responsible for the fuel-optimal control based on look-ahead information on road topography and the real-time vehicle control, respectively. The top layer, denoted the platoon coordinator, relies on a dynamic programming framework that computes the fuel-optimal speed profile for the entire platoon. The bottom layer, denoted the vehicle control layer, uses a distributed model predictive controller to track the optimal speed profile and the desired inter-vehicular spacing policy. Within this layer, constraints on the vehicles' states guarantee the safety of the platoon. The effectiveness of the proposed controller is analyzed by means of simulations of several realistic scenarios. They suggest a possible fuel saving of up to 12% for the follower vehicles compared to the use of existing platoon controllers. Analysis of the simulation results shows how the majority of the fuel saving comes from a reduced usage of vehicles brakes. A second problem addressed in the thesis is model predictive control for obstacle avoidance and lane keeping for a passenger car. We propose a control framework that allows to control the nonlinear vehicle dynamics with linear model predictive control. The controller decouples the longitudinal and lateral vehicle dynamics into two successive stages. First, plausible braking and throttle profiles are generated. Second, for each profile, linear time-varying models of the lateral dynamics are derived and used to formulate a collection of linear model predictive control problems. Their solution provides the optimal control input for the steering and braking actuators. The performance of the proposed controller has been evaluated by means of simulations and real experiments. / <p>QC 20150911</p>

<p>Bit-parallel addition can be performed using a number of adder structures with different area and latency. However, the power consumption of different adder structures is not well studied. Further, the effect of pipelining adders to increase the throughput is not well studied. In this thesis four different adders are described, implemented in VHDL and compared after synthesis. The results give a general idea of the time-delay-power tradeoffs between the adder structures. Pipelining is shown to be a good technique for increasing the circuit speed.</p>

This thesis proposes a decision-feedback equalizer (DFE) scheme for blind ADC-based receivers to overcome the challenges introduced by blind sampling. It presents the design, simulation, and implementation of a 5Gb/s speculative DFE for a 2x blind ADC-based receiver. The complete receiver, including the ADC, the DFE, and a 2x blind clock and data recovery (CDR) circuit, is implemented in Fujitsu’s 65-nm CMOS process. Measurements of the fabricated test-chip confirm 5Gb/s data recovery with bit error rate (BER) less than 1e−12 in the presence of a test channel introducing 13.3dB of attenuation at the Nyquist frequency of 2.5GHz. The receiver tolerates 0.24UIpp of high-frequency sinusoidal jitter (SJ) in this case. Without the DFE, the BER exceeds 1e−8 even when no SJ is applied.

This thesis proposes a decision-feedback equalizer (DFE) scheme for blind ADC-based receivers to overcome the challenges introduced by blind sampling. It presents the design, simulation, and implementation of a 5Gb/s speculative DFE for a 2x blind ADC-based receiver. The complete receiver, including the ADC, the DFE, and a 2x blind clock and data recovery (CDR) circuit, is implemented in Fujitsu’s 65-nm CMOS process. Measurements of the fabricated test-chip confirm 5Gb/s data recovery with bit error rate (BER) less than 1e−12 in the presence of a test channel introducing 13.3dB of attenuation at the Nyquist frequency of 2.5GHz. The receiver tolerates 0.24UIpp of high-frequency sinusoidal jitter (SJ) in this case. Without the DFE, the BER exceeds 1e−8 even when no SJ is applied.

Bit-parallel addition can be performed using a number of adder structures with different area and latency. However, the power consumption of different adder structures is not well studied. Further, the effect of pipelining adders to increase the throughput is not well studied. In this thesis four different adders are described, implemented in VHDL and compared after synthesis. The results give a general idea of the time-delay-power tradeoffs between the adder structures. Pipelining is shown to be a good technique for increasing the circuit speed.

abstract: The environmental impact of the fossil fuels has increased tremendously in the last decade. This impact is one of the most contributing factors of global warming. This research aims to reduce the amount of fuel consumed by vehicles through optimizing the control scheme for the future route information. Taking advantage of more degrees of freedom available within PHEV, HEV, and FCHEV “energy management” allows more margin to maximize efficiency in the propulsion systems. The application focuses on reducing the energy consumption in vehicles by acquiring information about the road grade. Road elevations are obtained by use of Geographic Information System (GIS) maps to optimize the controller. The optimization is then reflected on the powertrain of the vehicle.The approach uses a Model Predictive Control (MPC) algorithm that allows the energy management strategy to leverage road grade to prepare the vehicle for minimizing energy consumption during an uphill and potential energy harvesting during a downhill. The control algorithm will predict future energy/power requirements of the vehicle and optimize the performance by instructing the power split between the internal combustion engine (ICE) and the electric-drive system. Allowing for more efficient operation and higher performance of the PHEV, and HEV. Implementation of different strategies, such as MPC and Dynamic Programming (DP), is considered for optimizing energy management systems. These strategies are utilized to have a low processing time. This approach allows the optimization to be integrated with ADAS applications, using current technology for implementable real time applications. The Thesis presents multiple control strategies designed, implemented, and tested using real-world road elevation data from three different routes. Initial simulation based results show significant energy savings. The savings range between 11.84% and 25.5% for both Rule Based (RB) and DP strategies on the real world tested routes. Future work will take advantage of vehicle connectivity and ADAS systems to utilize Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), traffic information, and sensor fusion to further optimize the PHEV and HEV toward more energy efficient operation. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2016

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Chen, Rosenberg和Shepp（1997）的“雙人決策者的秘書問題“（A Secretary Problem with Two Decision Makers），探討在完整訊息（Full Information）與選擇次序不變的情況下，具有優先選擇權的決策者佔有較大優勢。這裡所謂的優勢意指在雙方最終選擇的大小為勝負條件所產生獲勝機率的比較。而本篇文章主要是延伸此一探討，意即在若不變動兩者選擇的次序，但賦予後選擇決策者較多資訊的條件下，能否平衡雙方的優劣勢。我們首先討論後決策者擁有預知下一步（One-step look-ahead）資訊能力的條件下，雙方優勢的改變；隨之若是在後決策者能預知完全資訊的情況下，是否能平衡雙方的優劣勢。而事實上，即便在後決策者擁有所有資訊的條件，仍無法完全改變此一情況；更進一步而言，先選擇決策者甚至在不知道後決策者已掌握了所有資訊的情況下，仍可佔有獲勝機率大於後決策者的優勢。這裡我們將提供理論與理論上的數值結果。 / Chen, Rosenberg, and Shepp (1997) considered a variation of the "secretary problem" in which the salary demands of a group of applicants are from a known and continuous distribution (i.e., full information case) and these applicants are interviewed sequentially by two managers, say, I and II. For every applicant. Manager I has the right to interview and hire him/her first. If Manager I rejects the applicant, Manager II can interview him/her. No recall is allowed when the applicant is rejected by both managers, and neither manager can interview and hire another applicant once he/she has hired an applicant. The manager who chooses the applicants with the lower salary wins the game. Chen et al. shows that manager I has bigger winning chance than manager II in the full information case. This study is to extend the paper by Chen et al., by giving extra information to manager H. In particular, suppose that manager II can look a few applicants ahead, i.e., he/she knows the salary demands of applicants before manager I interview them. However, under the full-information assumption, even if manager II is a clairvoyant, who claims to be able to see what will happen in the future, his/her winning probability is still less than that of manager I. We provide theoretical proof and simulation to confirm this result.

In my dissertation, I present ATLAS — the Auto-Training Look-Ahead Scheduler. ATLAS improves service to applications with regard to two non-functional properties: timeliness and overload detection. Timeliness is an important requirement to ensure user interface responsiveness and the smoothness of multimedia operations. Overload can occur when applications ask for more computation time than the machine can offer. Interactive systems have to handle overload situations dynamically at runtime. ATLAS provides timely service to applications, accessible through an easy-to-use interface. Deadlines specify timing requirements, workload metrics describe jobs. ATLAS employs machine learning to predict job execution times. Deadline misses are detected before they occur, so applications can react early.

Η άθροιση μεγάλων αριθμών είναι μια χρονοβόρα και ενεργοβόρα διαδικασία. Πολλές μέθοδοι έχουν αναπτυχθεί για να μειωθεί η καθυστέρηση υπολογισμού του αθροίσματος λόγω της μετάδοσης κρατουμένου. Τέτοιες είναι η πρόβλεψη κρατουμένου (carry look ahead) και η επιλογή κρατουμένου (carry select). Αυτές οι αρχιτεκτονικές δεν είναι επαρκώς επεκτάσιμες για μεγάλους αριθμούς (με πολλά bits) ή πολλούς αριθμούς, διότι παράγονται μεγάλα και ενεργοβόρα κυκλώματα. Στην παρούσα εργασία μελετάται η μέθοδος υπολοίπου (RNS), η οποία χρησιμοποιεί συστήματα αριθμών μεγαλύτερα από το δυαδικό. Ορίζεται μια βάση τριών αριθμών και οι αριθμοί αναπαρίστανται στα εκάστοτε τρία συστήματα της βάσης. Η άθροιση γίνεται παράλληλα σε κάθε σύστημα και τέλος οι αριθμοί μετατρέπονται πάλι στο δυαδικό. Τα πλεονεκτήματα αυτής της προσέγγισης είναι η παραλληλία και η απουσία μεγάλων κυκλωμάτων διάδοσης κρατουμένου. Το μειονέκτημα είναι ότι χρειάζονται κυκλώματα μετατροπής από και προς το δυαδικό σύστημα. Αυτού του είδους οι αθροιστές συγκρίνονται για κατανάλωση ενέργειας με τους γνωστούς carry look ahead και carry select. Διαπιστώθηκε ότι οι RNS αθροιστές καταναλώνουν λιγότερη ενέργεια. / The addition of many-bits numbers is a time and power consuming task. Many methods are developed to reduce the sum calculation delay due to carry propagation. Such techniques are Carry Look Ahead and Carry Select, Those techniques are not scalable to many bits numbers or a set of many numbers: the circuits needed are big and power consuming. In this thesis, the the RNS technique is investigated. This technique uses radix bigger than binary. A 3-numbers base is defined and the numbers that participate in the sum are represented uniquely in each element radix. The addition is performed in parallel in each radix. Finally the result is transformed back to the binary numbers system. The advantages of this technique are the parallelization of the process and the lack of carry propagation circuits. The disadvantage is that transformation circuits are need from/to binary system. The RNS adders are compared to CLA and CS for power. Such adders are compared to CLA and CS for power consumption. It is found that RNS adders consume less energy.